710 lines
32 KiB
Python
710 lines
32 KiB
Python
# coding=utf-8
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# Copyright 2024 state-spaces/mamba org and HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch MAMBA model."""
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import math
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from dataclasses import dataclass
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from typing import Any, Dict, Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from ...activations import ACT2FN
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from ...modeling_utils import PreTrainedModel
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from ...utils import (
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ModelOutput,
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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)
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from ...utils.import_utils import is_causal_conv1d_available, is_mamba_ssm_available
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from .configuration_mamba import MambaConfig
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logger = logging.get_logger(__name__)
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if is_mamba_ssm_available():
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from mamba_ssm.ops.selective_scan_interface import mamba_inner_fn, selective_scan_fn
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from mamba_ssm.ops.triton.selective_state_update import selective_state_update
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else:
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selective_state_update, selective_scan_fn, mamba_inner_fn = None, None, None
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if is_causal_conv1d_available():
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from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
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else:
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causal_conv1d_update, causal_conv1d_fn = None, None
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is_fast_path_available = all(
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(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
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)
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_CHECKPOINT_FOR_DOC = "state-spaces/mamba-130m-hf"
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_CONFIG_FOR_DOC = "MambaConfig"
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from ..deprecated._archive_maps import MAMBA_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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class MambaCache:
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"""
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Arguments:
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config: MambaConfig
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batch_size: int
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dtype: torch.dtype
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device: torch.device
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Attributes:
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seqlen_offset: int
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dtype: torch.dtype
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conv_states: Dict[int, torch.Tensor] # layer_idx -> [batch_size, intermediate_size, conv_kernel_size]
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ssm_states: Dict[int, torch.Tensor] # layer_idx -> [batch_size, intermediate_size, ssm_state_size]
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"""
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def __init__(
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self, config: MambaConfig, batch_size: int, dtype: torch.dtype = torch.float16, device: Optional[str] = None
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):
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self.seqlen_offset = 0
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self.dtype = dtype
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intermediate_size = config.intermediate_size
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ssm_state_size = config.state_size
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conv_kernel_size = config.conv_kernel
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self.conv_states = {
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i: torch.zeros(batch_size, intermediate_size, conv_kernel_size, device=device, dtype=dtype)
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for i in range(config.num_hidden_layers)
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}
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self.ssm_states = {
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i: torch.zeros(batch_size, intermediate_size, ssm_state_size, device=device, dtype=dtype)
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for i in range(config.num_hidden_layers)
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}
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class MambaMixer(nn.Module):
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"""
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Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
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A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
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∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
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and is why Mamba is called **selective** state spaces)
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"""
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def __init__(self, config: MambaConfig, layer_idx: int):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.ssm_state_size = config.state_size
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self.conv_kernel_size = config.conv_kernel
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self.intermediate_size = config.intermediate_size
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self.time_step_rank = int(config.time_step_rank)
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self.layer_idx = layer_idx
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self.use_conv_bias = config.use_conv_bias
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self.conv1d = nn.Conv1d(
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in_channels=self.intermediate_size,
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out_channels=self.intermediate_size,
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bias=config.use_conv_bias,
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kernel_size=config.conv_kernel,
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groups=self.intermediate_size,
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padding=config.conv_kernel - 1,
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)
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self.activation = config.hidden_act
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self.act = ACT2FN[config.hidden_act]
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# projection of the input hidden states
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self.in_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=config.use_bias)
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# selective projection used to make dt, B and C input dependant
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self.x_proj = nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False)
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# time step projection (discretization)
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self.dt_proj = nn.Linear(self.time_step_rank, self.intermediate_size, bias=True)
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# S4D real initialization. These are not discretized!
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# The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
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A = torch.arange(1, self.ssm_state_size + 1, dtype=torch.float32)[None, :]
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A = A.expand(self.intermediate_size, -1).contiguous()
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self.A_log = nn.Parameter(torch.log(A))
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self.D = nn.Parameter(torch.ones(self.intermediate_size))
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self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias)
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self.use_bias = config.use_bias
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if not is_fast_path_available:
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logger.warning_once(
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"The fast path is not available because on of `(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)`"
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" is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and"
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" https://github.com/Dao-AILab/causal-conv1d"
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)
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def cuda_kernels_forward(self, hidden_states: torch.Tensor, cache_params: Optional[MambaCache] = None):
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# 1. Gated MLP's linear projection
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projected_states = self.in_proj(hidden_states).transpose(1, 2)
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if self.training and cache_params is None: # Doesn't support outputting the states -> used for training
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contextualized_states = mamba_inner_fn(
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projected_states,
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self.conv1d.weight,
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self.conv1d.bias if self.use_conv_bias else None,
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self.x_proj.weight,
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self.dt_proj.weight,
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self.out_proj.weight,
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self.out_proj.bias.float() if self.use_bias else None,
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-torch.exp(self.A_log.float()),
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None, # input-dependent B
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None, # input-dependent C
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self.D.float(),
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delta_bias=self.dt_proj.bias.float(),
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delta_softplus=True,
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)
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else:
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hidden_states, gate = projected_states.chunk(2, dim=1)
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# 2. Convolution sequence transformation
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conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
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if cache_params is not None and cache_params.seqlen_offset > 0:
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hidden_states = causal_conv1d_update(
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hidden_states.squeeze(-1),
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cache_params.conv_states[self.layer_idx],
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conv_weights,
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self.conv1d.bias,
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self.activation,
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)
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hidden_states = hidden_states.unsqueeze(-1)
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else:
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if cache_params is not None:
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conv_states = nn.functional.pad(
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hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0)
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)
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cache_params.conv_states[self.layer_idx].copy_(conv_states)
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hidden_states = causal_conv1d_fn(
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hidden_states, conv_weights, self.conv1d.bias, activation=self.activation
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)
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# 3. State Space Model sequence transformation
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# 3.a. input varying initialization of time_step, B and C
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ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
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time_step, B, C = torch.split(
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ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1
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)
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discrete_time_step = self.dt_proj.weight @ time_step.transpose(1, 2)
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A = -torch.exp(self.A_log.float())
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# 3.c perform the recurrence y ← SSM(A, B, C)(x)
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time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, "bias") else None
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if cache_params is not None and cache_params.seqlen_offset > 0:
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scan_outputs = selective_state_update(
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cache_params.ssm_states[self.layer_idx],
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hidden_states[..., 0],
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discrete_time_step[..., 0],
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A,
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B[:, 0],
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C[:, 0],
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self.D,
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gate[..., 0],
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time_proj_bias,
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dt_softplus=True,
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).unsqueeze(-1)
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else:
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scan_outputs, ssm_state = selective_scan_fn(
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hidden_states,
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discrete_time_step,
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A,
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B.transpose(1, 2),
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C.transpose(1, 2),
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self.D.float(),
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gate,
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time_proj_bias,
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delta_softplus=True,
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return_last_state=True,
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)
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if ssm_state is not None and cache_params is not None:
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cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
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# 4. Final linear projection
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contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
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return contextualized_states
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# fmt: off
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def slow_forward(self, input_states, cache_params: Optional[MambaCache]=None):
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batch_size, seq_len, _ = input_states.shape
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dtype = input_states.dtype
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# 1. Gated MLP's linear projection
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projected_states = self.in_proj(input_states).transpose(1, 2) # [batch, 2 * intermediate_size, seq_len]
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hidden_states, gate = projected_states.chunk(2, dim=1)
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# 2. Convolution sequence transformation
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if cache_params is not None:
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ssm_state = cache_params.ssm_states[self.layer_idx].clone()
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if cache_params.seqlen_offset > 0:
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conv_state = cache_params.conv_states[self.layer_idx] # [batch, intermediate_size, conv_kernel_size]
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conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
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conv_state[:, :, -1] = hidden_states[:, :, 0]
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cache_params.conv_states[self.layer_idx].copy_(conv_state)
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hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
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if self.use_conv_bias:
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hidden_states += self.conv1d.bias
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hidden_states = self.act(hidden_states).to(dtype).unsqueeze(-1) # [batch, intermediate_size, 1] : decoding
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else:
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conv_state = nn.functional.pad(
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hidden_states,
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(self.conv_kernel_size - hidden_states.shape[-1], 0)
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)
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cache_params.conv_states[self.layer_idx].copy_(conv_state)
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hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) # [batch, intermediate_size, seq_len]
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else:
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ssm_state = torch.zeros(
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(batch_size, self.intermediate_size, self.ssm_state_size),
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device=hidden_states.device, dtype=dtype
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)
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hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) # [batch, intermediate_size, seq_len]
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# 3. State Space Model sequence transformation
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# 3.a. Selection: [batch, seq_len, self.time_step_rank + self.ssm_state_size * 2]
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ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
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time_step, B, C = torch.split(
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ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1
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)
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discrete_time_step = self.dt_proj(time_step) # [batch, seq_len, intermediate_size]
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discrete_time_step = nn.functional.softplus(discrete_time_step).transpose(1, 2) # [batch, intermediate_size, seq_len]
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# 3.b. Discretization: B and C to [batch, seq_len, intermediate_size, ssm_state_size] (SRAM)
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A = -torch.exp(self.A_log.float()) # [intermediate_size, ssm_state_size]
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discrete_A = torch.exp(A[None, :, None, :] * discrete_time_step[:, :, :, None]) # [batch, intermediate_size, seq_len, ssm_state_size]
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discrete_B = discrete_time_step[:, :, :, None] * B[:, None, :, :].float() # [batch, intermediade_size, seq_len, ssm_state_size]
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deltaB_u = discrete_B * hidden_states[:, :, :, None].float()
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# 3.c perform the recurrence y ← SSM(A, B, C)(x)
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scan_outputs = []
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for i in range(seq_len):
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ssm_state = discrete_A[:, :, i, :] * ssm_state + deltaB_u[:, :, i, :] # [batch, intermediade_size, ssm_state]
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scan_output = torch.matmul(ssm_state.to(dtype), C[:, i, :].unsqueeze(-1)) # [batch, intermediade_size, 1]
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scan_outputs.append(scan_output[:, :, 0])
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scan_output = torch.stack(scan_outputs, dim=-1) # [batch, seq_len, intermediade_size]
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scan_output = scan_output + (hidden_states * self.D[None, :, None])
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scan_output = (scan_output * self.act(gate))
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if cache_params is not None:
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cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
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# 4. Final linear projection
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contextualized_states = self.out_proj(scan_output.transpose(1, 2)) # [batch, seq_len, hidden_size]
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return contextualized_states
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# fmt: on
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def forward(self, hidden_states, cache_params: Optional[MambaCache] = None):
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if is_fast_path_available and "cuda" in self.x_proj.weight.device.type:
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return self.cuda_kernels_forward(hidden_states, cache_params)
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return self.slow_forward(hidden_states, cache_params)
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class MambaRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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MambaRMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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class MambaBlock(nn.Module):
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def __init__(self, config, layer_idx):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.residual_in_fp32 = config.residual_in_fp32
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self.norm = MambaRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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self.mixer = MambaMixer(config, layer_idx=layer_idx)
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def forward(self, hidden_states, cache_params: Optional[MambaCache] = None):
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residual = hidden_states
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hidden_states = self.norm(hidden_states.to(dtype=self.norm.weight.dtype))
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if self.residual_in_fp32:
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residual = residual.to(torch.float32)
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hidden_states = self.mixer(hidden_states, cache_params=cache_params)
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hidden_states = residual + hidden_states
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return hidden_states
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class MambaPreTrainedModel(PreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config_class = MambaConfig
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base_model_prefix = "backbone"
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_no_split_modules = ["MambaBlock"]
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supports_gradient_checkpointing = True
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def _init_weights(self, module):
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"""Initialize the weights."""
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if isinstance(module, MambaMixer):
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module.A_log._no_weight_decay = True
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module.D._no_weight_decay = True
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dt_init_std = self.config.time_step_rank**-0.5 * self.config.time_step_scale
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if self.config.time_step_init_scheme == "constant":
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nn.init.constant_(module.dt_proj.weight, dt_init_std)
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elif self.config.time_step_init_scheme == "random":
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nn.init.uniform_(module.dt_proj.weight, -dt_init_std, dt_init_std)
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dt = torch.exp(
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torch.rand(self.config.intermediate_size)
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* (math.log(self.config.time_step_max) - math.log(self.config.time_step_min))
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+ math.log(self.config.time_step_min)
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).clamp(min=self.config.time_step_floor)
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# # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
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inv_dt = dt + torch.log(-torch.expm1(-dt))
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with torch.no_grad():
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module.dt_proj.bias.copy_(inv_dt)
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module.dt_proj.bias._no_reinit = True
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if isinstance(module, nn.Linear):
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if module.bias is not None:
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if not getattr(module.bias, "_no_reinit", False):
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nn.init.zeros_(module.bias)
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elif isinstance(module, nn.Embedding):
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nn.init.normal_(module.weight, std=self.config.initializer_range)
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if self.config.rescale_prenorm_residual:
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# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
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# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
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# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
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# > -- GPT-2 :: https://openai.com/blog/better-language-models/
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#
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# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
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for name, p in module.named_parameters():
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if name in ["out_proj.weight"]:
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# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
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# Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
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# We need to reinit p since this code could be called multiple times
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# Having just p *= scale would repeatedly scale it down
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nn.init.kaiming_uniform_(p, a=math.sqrt(5))
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with torch.no_grad():
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p /= math.sqrt(self.config.num_layers)
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@dataclass
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class MambaOutput(ModelOutput):
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"""
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Class for the MAMBA model outputs.
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Args:
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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: Optional[torch.FloatTensor] = None
|
|
cache_params: Optional[MambaCache] = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@dataclass
|
|
class MambaCausalLMOutput(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: Optional[torch.FloatTensor] = None
|
|
logits: Optional[torch.FloatTensor] = None
|
|
cache_params: Optional[MambaCache] = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
MAMBA_START_DOCSTRING = r"""
|
|
|
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
|
etc.)
|
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
|
and behavior.
|
|
|
|
Parameters:
|
|
config ([`MambaConfig`]): Model configuration class with all the parameters of the model.
|
|
Initializing with a config file does not load the weights associated with the model, only the
|
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
MAMBA_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
If `cache_params.seqlen_offset>0`, only `input_ids` that do not have their past calculated should be passed as
|
|
`input_ids`.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
cache_params (`MambaCache`, *optional*):
|
|
If passed along, the model uses the previous state in all the blocks (which will give the output for the
|
|
`input_ids` provided as if the model add `state_input_ids + input_ids` as context).
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare MAMBA Model transformer outputting raw hidden-states without any specific head on top.",
|
|
MAMBA_START_DOCSTRING,
|
|
)
|
|
class MambaModel(MambaPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
|
|
self.layers = nn.ModuleList([MambaBlock(config, layer_idx=idx) for idx in range(config.num_hidden_layers)])
|
|
|
|
self.gradient_checkpointing = False
|
|
self.norm_f = MambaRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
|
|
# Initialize weights and apply final processing
|
|
self._register_load_state_dict_pre_hook(self.load_hook)
|
|
self.post_init()
|
|
|
|
def load_hook(self, state_dict, prefix, *args):
|
|
for k in state_dict:
|
|
if "embedding." in k:
|
|
state_dict[k.replace("embedding.", "embeddings.")] = state_dict.pop(k)
|
|
break
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
self.embeddings = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(MAMBA_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=MambaOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: Optional[torch.LongTensor] = None,
|
|
cache_params: Optional[MambaCache] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
**kwargs, # `attention_mask` is passed by the tokenizer and we don't want it
|
|
) -> Union[Tuple, MambaOutput]:
|
|
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 self.gradient_checkpointing and self.training and use_cache:
|
|
use_cache = False
|
|
|
|
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:
|
|
if self.gradient_checkpointing and self.training:
|
|
hidden_states = self._gradient_checkpointing_func(mixer_block.__call__, hidden_states, cache_params)
|
|
else:
|
|
hidden_states = mixer_block(hidden_states, cache_params=cache_params)
|
|
|
|
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 MambaOutput(
|
|
last_hidden_state=hidden_states,
|
|
cache_params=cache_params if use_cache else None,
|
|
hidden_states=all_hidden_states,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The MAMBA Model transformer with a language modeling head on top (linear layer with weights tied to the input
|
|
embeddings).
|
|
""",
|
|
MAMBA_START_DOCSTRING,
|
|
)
|
|
class MambaForCausalLM(MambaPreTrainedModel):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.backbone = MambaModel(config)
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
# 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
|
|
|
|
def prepare_inputs_for_generation(
|
|
self, input_ids, cache_params: Optional[MambaCache] = None, inputs_embeds=None, attention_mask=None, **kwargs
|
|
):
|
|
# only last token for inputs_ids if the state is passed along.
|
|
if cache_params is not None:
|
|
input_ids = input_ids[:, -1].unsqueeze(-1)
|
|
|
|
if inputs_embeds is not None and cache_params is None:
|
|
model_inputs = {"inputs_embeds": inputs_embeds}
|
|
else:
|
|
model_inputs = {"input_ids": input_ids}
|
|
|
|
model_inputs["cache_params"] = cache_params
|
|
return model_inputs
|
|
|
|
@add_start_docstrings_to_model_forward(MAMBA_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=MambaCausalLMOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
cache_params: Optional[MambaCache] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
use_cache: Optional[bool] = None,
|
|
**kwargs, # for now we need this for generation
|
|
) -> Union[Tuple, MambaCausalLMOutput]:
|
|
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
|
|
|
|
mamba_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,
|
|
)
|
|
hidden_states = mamba_outputs[0]
|
|
|
|
logits = self.lm_head(hidden_states.to(self.lm_head.weight.dtype)).float()
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
# Shift so that tokens < n predict n
|
|
shift_logits = logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + mamba_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return MambaCausalLMOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
cache_params=mamba_outputs.cache_params,
|
|
hidden_states=mamba_outputs.hidden_states,
|
|
)
|