1059 lines
47 KiB
Python
1059 lines
47 KiB
Python
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# coding=utf-8
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# Copyright 2024 state-spaces/mamba2 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 MAMBA2 model."""
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import math
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from dataclasses import dataclass
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from typing import Optional, 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 ...activations import ACT2FN
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from ...generation import GenerationMixin
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from ...modeling_layers import GradientCheckpointingLayer
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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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auto_docstring,
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logging,
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)
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from ...utils.import_utils import is_causal_conv1d_available, is_mamba_2_ssm_available
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from .configuration_mamba2 import Mamba2Config
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logger = logging.get_logger(__name__)
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if is_mamba_2_ssm_available():
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from mamba_ssm.ops.triton.selective_state_update import selective_state_update
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from mamba_ssm.ops.triton.ssd_combined import mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined
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else:
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mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined, selective_state_update = 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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(
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selective_state_update,
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mamba_chunk_scan_combined,
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mamba_split_conv1d_scan_combined,
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causal_conv1d_fn,
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causal_conv1d_update,
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)
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)
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# Helper methods for segment sum computation
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def pad_tensor_by_size(input_tensor: torch.Tensor, pad_size: int):
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"""
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Padding x tensor with `pad_size` on the seq_len dim (dim=1)
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Assumes that we only have tensors of either size 4 or 3
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"""
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pad_shape = (0, 0, 0, 0, 0, pad_size, 0, 0) if len(input_tensor.shape) == 4 else (0, 0, 0, pad_size, 0, 0)
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return torch.nn.functional.pad(input_tensor, pad_shape, mode="constant", value=0)
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def reshape_into_chunks(input_tensor, pad_size, chunk_size):
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"""
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Padding input_tensor with `pad_size` on the seq_len dim (dim=1) and
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simultaneously splitting it into chunk sequences.
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Assumes that we only have tensors of either size 4 or 3
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"""
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# [bsz, seq_len, ...] -> [bsz, seq_len multiple of chunk_size, ...]
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input_tensor = pad_tensor_by_size(input_tensor, pad_size)
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if len(input_tensor.shape) == 3:
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# [bsz, seq_len multiple of chunk_size, num_heads] -> [bsz, -1, chunk_size, num_heads]
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return input_tensor.reshape(input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2])
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else:
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# [bsz, seq_len multiple of chunk_size, num_heads, head_dim or state_size] -> [bsz, -1, chunk_size, num_heads, head_dim or state_size]
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return input_tensor.reshape(
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input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2], input_tensor.shape[3]
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)
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def segment_sum(input_tensor):
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"""
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More stable segment sum calculation. Uses cumulative sums and masking instead of direct subtractions.
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"""
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chunk_size = input_tensor.size(-1)
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# 1. expand input tensor to have an additional dimension and repeat along that dimension
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# [..., chunk_size] -> [..., chunk_size, chunk_size]
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input_tensor = input_tensor[..., None].expand(*input_tensor.size(), chunk_size)
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# 2. create a lower triangular mask with the diagonal set to 0 to 0 out elements above diag
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mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=-1)
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input_tensor = input_tensor.masked_fill(~mask, 0)
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# 3. compute actual cumsum
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tensor_segsum = torch.cumsum(input_tensor, dim=-2)
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# 4. apply mask to keep only the lower triangular part of the cumulative sum result (incl diagonal this time)
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mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=0)
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tensor_segsum = tensor_segsum.masked_fill(~mask, -torch.inf)
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return tensor_segsum
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def apply_mask_to_padding_states(hidden_states, attention_mask):
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"""
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Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66
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"""
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if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1:
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dtype = hidden_states.dtype
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hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
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return hidden_states
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class Mamba2Cache:
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"""
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Arguments:
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config: Mamba2Config
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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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dtype: (`torch.dtype`):
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The default `dtype` used to initializing the cache.
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conv_kernel_size: (`int`):
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Model's convolution kernel size taken from config.
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n_groups: (`int`):
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Model's number of groups taken from the config - similar to tensor parallel in Transformer.
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state_size: (`int`):
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Model's SSM state size taken from config.
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num_heads: (`int`):
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The number of heads used in the linear attention / SSM.
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head_dim: (`int`):
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The respective dimension of the heads used in the linear attention / SSM.
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intermediate_size: (`int`):
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Model's intermediate_size based on (expand * hidden_dim) from config.
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conv_states: (`torch.Tensor`):
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A tensor of shape `[num_layers, batch_size, conv_kernel_size, intermediate_size + 2 * n_groups * state_size]` that holds convolutional states.
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ssm_states: (`torch.Tensor`):
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A tensor of shape `[num_layers, batch_size, num_heads, head_dim, state_size]` that holds ssm states.
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"""
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def __init__(
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self, config: Mamba2Config, batch_size: int, dtype: torch.dtype = torch.float16, device: Optional[str] = None
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):
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self.dtype = dtype
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self.conv_kernel_size = config.conv_kernel
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self.n_groups = config.n_groups
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self.state_size = config.state_size
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self.num_heads = config.num_heads
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self.head_dim = config.head_dim
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self.intermediate_size = int(config.expand * config.hidden_size)
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self.conv_states = torch.zeros(
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config.num_hidden_layers,
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batch_size,
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self.intermediate_size + 2 * self.n_groups * self.state_size,
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self.conv_kernel_size,
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device=device,
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dtype=dtype,
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)
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self.ssm_states = torch.zeros(
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config.num_hidden_layers,
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batch_size,
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self.num_heads,
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self.head_dim,
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self.state_size,
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device=device,
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dtype=dtype,
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)
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def update_conv_state(
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self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool = False
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) -> torch.Tensor:
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if cache_init:
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self.conv_states[layer_idx] = new_conv_state.to(self.conv_states.device)
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else:
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self.conv_states[layer_idx] = self.conv_states[layer_idx].roll(shifts=-1, dims=-1)
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self.conv_states[layer_idx][:, :, -1] = new_conv_state[:, 0, :].to(self.conv_states.device)
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return self.conv_states[layer_idx]
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def update_ssm_state(self, layer_idx: int, new_ssm_state: torch.Tensor):
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self.ssm_states[layer_idx] = new_ssm_state.to(self.ssm_states.device)
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return self.ssm_states[layer_idx]
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def reset(self):
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self.conv_states.zero_()
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self.ssm_states.zero_()
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class MambaRMSNormGated(torch.nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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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, gate=None):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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if gate is not None:
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hidden_states = hidden_states * nn.functional.silu(gate.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 Mamba2Mixer(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: Mamba2Config, layer_idx: int):
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super().__init__()
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self.num_heads = config.num_heads
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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 = int(config.expand * self.hidden_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.activation = config.hidden_act
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self.act = ACT2FN[config.hidden_act]
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self.layer_norm_epsilon = config.layer_norm_epsilon
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self.rms_norm = config.rms_norm
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self.n_groups = config.n_groups
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self.head_dim = config.head_dim
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self.chunk_size = config.chunk_size
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self.time_step_limit = config.time_step_limit
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self.time_step_min = config.time_step_min
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self.time_step_max = config.time_step_max
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self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size
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self.conv1d = nn.Conv1d(
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in_channels=self.conv_dim,
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out_channels=self.conv_dim,
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bias=config.use_conv_bias,
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kernel_size=config.conv_kernel,
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groups=self.conv_dim,
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padding=config.conv_kernel - 1,
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)
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# projection of the input hidden states
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projection_size = self.intermediate_size + self.conv_dim + self.num_heads
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self.in_proj = nn.Linear(
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self.hidden_size,
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projection_size,
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bias=config.use_bias,
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)
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# selective projection used to make dt, B and C input dependent
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# time step projection (discretization)
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# instantiate once and copy inv_dt in init_weights of PretrainedModel
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self.dt_bias = nn.Parameter(torch.ones(self.num_heads))
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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.num_heads + 1)
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self.A_log = nn.Parameter(torch.log(A))
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self.A_log._no_weight_decay = True
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self.norm = MambaRMSNormGated(self.intermediate_size, eps=self.layer_norm_epsilon)
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self.D = nn.Parameter(torch.ones(self.num_heads))
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self.D._no_weight_decay = True
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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, causal_conv1d_fn, causal_conv1d_update)`"
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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(
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self,
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hidden_states: torch.Tensor,
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cache_params: Optional[Mamba2Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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):
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# 1. Gated MLP's linear projection
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hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
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projected_states = self.in_proj(hidden_states)
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# Set up dimensions for reshapes later
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batch_size, seq_len, _ = hidden_states.shape
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groups_time_state_size = self.n_groups * self.ssm_state_size
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d_mlp = (
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projected_states.shape[-1]
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- 2 * self.intermediate_size
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- 2 * self.n_groups * self.ssm_state_size
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- self.num_heads
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) // 2
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# Single step calculations via cache
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if cache_params is not None and cache_position is not None and cache_position[0] > 0:
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_, _, gate, hidden_states_B_C, dt = projected_states.squeeze(1).split(
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[d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
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)
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# 2. Convolution sequence transformation
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hidden_states_B_C = causal_conv1d_update(
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hidden_states_B_C,
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cache_params.conv_states[self.layer_idx],
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self.conv1d.weight.squeeze(1),
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self.conv1d.bias,
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self.activation,
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)
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hidden_states, B, C = torch.split(
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hidden_states_B_C,
|
||
|
|
[self.intermediate_size, groups_time_state_size, groups_time_state_size],
|
||
|
|
dim=-1,
|
||
|
|
)
|
||
|
|
|
||
|
|
# 3. SSM transformation
|
||
|
|
A = -torch.exp(self.A_log.float()) # (nheads,)
|
||
|
|
A = A[:, None, ...][:, :, None].expand(-1, self.head_dim, self.ssm_state_size).to(dtype=torch.float32)
|
||
|
|
dt = dt[:, :, None].expand(-1, -1, self.head_dim)
|
||
|
|
dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim)
|
||
|
|
D = self.D[:, None, ...].expand(-1, self.head_dim)
|
||
|
|
B = B.view(batch_size, self.n_groups, B.shape[1] // self.n_groups)
|
||
|
|
C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
|
||
|
|
hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
|
||
|
|
hidden_states = selective_state_update(
|
||
|
|
cache_params.ssm_states[self.layer_idx],
|
||
|
|
hidden_states_reshaped,
|
||
|
|
dt,
|
||
|
|
A,
|
||
|
|
B,
|
||
|
|
C,
|
||
|
|
D,
|
||
|
|
z=None,
|
||
|
|
dt_bias=dt_bias,
|
||
|
|
dt_softplus=True,
|
||
|
|
)
|
||
|
|
hidden_states = hidden_states.view(batch_size, self.num_heads * self.head_dim)
|
||
|
|
hidden_states = self.norm(hidden_states, gate)
|
||
|
|
|
||
|
|
# 4. Final linear projection
|
||
|
|
out = self.out_proj(hidden_states)[:, None, ...]
|
||
|
|
|
||
|
|
# Fused calculations or step by step if no initialized cache is found
|
||
|
|
else:
|
||
|
|
A = -torch.exp(self.A_log.float()) # (num_heads) or (intermediate_size, state_size)
|
||
|
|
dt_limit_kwargs = {} if self.time_step_limit == (0.0, float("inf")) else {"dt_limit": self.time_step_limit}
|
||
|
|
|
||
|
|
# 2-4. Fused kernel for conv1d, SSM, and the final projection
|
||
|
|
if self.training and cache_params is None:
|
||
|
|
out = mamba_split_conv1d_scan_combined(
|
||
|
|
projected_states,
|
||
|
|
self.conv1d.weight.squeeze(1),
|
||
|
|
self.conv1d.bias,
|
||
|
|
self.dt_bias,
|
||
|
|
A,
|
||
|
|
D=self.D,
|
||
|
|
chunk_size=self.chunk_size,
|
||
|
|
seq_idx=None, # was seq_idx
|
||
|
|
activation=self.activation,
|
||
|
|
rmsnorm_weight=self.norm.weight,
|
||
|
|
rmsnorm_eps=self.norm.variance_epsilon,
|
||
|
|
outproj_weight=self.out_proj.weight,
|
||
|
|
outproj_bias=self.out_proj.bias,
|
||
|
|
headdim=self.head_dim,
|
||
|
|
ngroups=self.n_groups,
|
||
|
|
norm_before_gate=False,
|
||
|
|
return_final_states=False,
|
||
|
|
**dt_limit_kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
else:
|
||
|
|
_, _, gate, hidden_states_B_C, dt = projected_states.split(
|
||
|
|
[d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
|
||
|
|
)
|
||
|
|
|
||
|
|
# 2. Convolution sequence transformation
|
||
|
|
# Init cache
|
||
|
|
if cache_params is not None:
|
||
|
|
hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
|
||
|
|
conv_states = nn.functional.pad(
|
||
|
|
hidden_states_B_C_transposed,
|
||
|
|
(cache_params.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0),
|
||
|
|
)
|
||
|
|
cache_params.update_conv_state(
|
||
|
|
layer_idx=self.layer_idx, new_conv_state=conv_states, cache_init=True
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.activation not in ["silu", "swish"]:
|
||
|
|
hidden_states_B_C = self.act(
|
||
|
|
self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2)
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
hidden_states_B_C = causal_conv1d_fn(
|
||
|
|
x=hidden_states_B_C.transpose(1, 2),
|
||
|
|
weight=self.conv1d.weight.squeeze(1),
|
||
|
|
bias=self.conv1d.bias,
|
||
|
|
activation=self.activation,
|
||
|
|
).transpose(1, 2)
|
||
|
|
|
||
|
|
hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
|
||
|
|
hidden_states, B, C = torch.split(
|
||
|
|
hidden_states_B_C,
|
||
|
|
[self.intermediate_size, groups_time_state_size, groups_time_state_size],
|
||
|
|
dim=-1,
|
||
|
|
)
|
||
|
|
|
||
|
|
# 3. SSM transformation
|
||
|
|
scan_output, ssm_state = mamba_chunk_scan_combined(
|
||
|
|
hidden_states.view(batch_size, seq_len, -1, self.head_dim),
|
||
|
|
dt,
|
||
|
|
A,
|
||
|
|
B.view(batch_size, seq_len, self.n_groups, -1),
|
||
|
|
C.view(batch_size, seq_len, self.n_groups, -1),
|
||
|
|
chunk_size=self.chunk_size,
|
||
|
|
D=self.D,
|
||
|
|
z=None,
|
||
|
|
seq_idx=None,
|
||
|
|
return_final_states=True,
|
||
|
|
dt_bias=self.dt_bias,
|
||
|
|
dt_softplus=True,
|
||
|
|
**dt_limit_kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Init cache
|
||
|
|
if ssm_state is not None and cache_params is not None:
|
||
|
|
cache_params.update_ssm_state(layer_idx=self.layer_idx, new_ssm_state=ssm_state)
|
||
|
|
|
||
|
|
scan_output = scan_output.view(batch_size, seq_len, -1)
|
||
|
|
# Multiply "gate" branch and apply extra normalization layer
|
||
|
|
scan_output = self.norm(scan_output, gate)
|
||
|
|
|
||
|
|
# 4. Final linear projection
|
||
|
|
out = self.out_proj(scan_output)
|
||
|
|
return out
|
||
|
|
|
||
|
|
# fmt: off
|
||
|
|
def torch_forward(
|
||
|
|
self,
|
||
|
|
hidden_states: torch.Tensor,
|
||
|
|
cache_params: Optional[Mamba2Cache]=None,
|
||
|
|
cache_position:Optional[torch.LongTensor]=None,
|
||
|
|
attention_mask: Optional[torch.Tensor]=None
|
||
|
|
):
|
||
|
|
batch_size, seq_len, _ = hidden_states.shape
|
||
|
|
dtype = hidden_states.dtype
|
||
|
|
|
||
|
|
# 1. Gated MLP's linear projection
|
||
|
|
hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
|
||
|
|
projected_states = self.in_proj(hidden_states)
|
||
|
|
d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.ssm_state_size-self.num_heads) // 2
|
||
|
|
_, _, gate, hidden_states_B_C, dt = projected_states.split(
|
||
|
|
[d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
|
||
|
|
)
|
||
|
|
|
||
|
|
# 2. Convolution sequence transformation
|
||
|
|
if cache_params is not None and cache_position is not None and cache_position[0] > 0:
|
||
|
|
cache_params.update_conv_state(layer_idx=self.layer_idx, new_conv_state=hidden_states_B_C, cache_init=False)
|
||
|
|
|
||
|
|
# We need to guarantee that anything regarding the cache is on the same device
|
||
|
|
conv_states = cache_params.conv_states[self.layer_idx].to(device=self.conv1d.weight.device)
|
||
|
|
|
||
|
|
hidden_states_B_C = torch.sum(
|
||
|
|
conv_states * self.conv1d.weight.squeeze(1), dim=-1
|
||
|
|
)
|
||
|
|
if self.use_conv_bias:
|
||
|
|
hidden_states_B_C = hidden_states_B_C + self.conv1d.bias
|
||
|
|
hidden_states_B_C = self.act(hidden_states_B_C)
|
||
|
|
else:
|
||
|
|
# Init cache
|
||
|
|
if cache_params is not None:
|
||
|
|
hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
|
||
|
|
conv_states = nn.functional.pad(
|
||
|
|
hidden_states_B_C_transposed, (cache_params.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0)
|
||
|
|
)
|
||
|
|
cache_params.update_conv_state(layer_idx=self.layer_idx, new_conv_state=conv_states, cache_init=True)
|
||
|
|
|
||
|
|
hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2))
|
||
|
|
|
||
|
|
hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
|
||
|
|
hidden_states, B, C = torch.split(
|
||
|
|
hidden_states_B_C,
|
||
|
|
[self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size],
|
||
|
|
dim=-1
|
||
|
|
)
|
||
|
|
|
||
|
|
# 3. SSM transformation
|
||
|
|
A = -torch.exp(self.A_log.float()) # [num_heads]
|
||
|
|
if cache_params is not None and cache_position is not None and cache_position[0] > 0:
|
||
|
|
# We need to guarantee that anything regarding the cache is on the same device
|
||
|
|
cache_device = cache_params.ssm_states.device
|
||
|
|
|
||
|
|
# Note: there is no need to pad parameter matrices here, as there is just one new token
|
||
|
|
# for batched generation
|
||
|
|
dt = dt[:, 0, :][:, None, ...]
|
||
|
|
dt = dt.transpose(1, 2).expand(batch_size, dt.shape[-1], self.head_dim)
|
||
|
|
# [num_heads] -> [num_heads, head_dim]
|
||
|
|
dt_bias = self.dt_bias[..., None].expand(self.dt_bias.shape[0], self.head_dim)
|
||
|
|
|
||
|
|
dt = torch.nn.functional.softplus(dt + dt_bias.to(dt.dtype))
|
||
|
|
dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1])
|
||
|
|
A = A[..., None, None].expand(self.num_heads, self.head_dim, self.ssm_state_size).to(dtype=torch.float32)
|
||
|
|
# [bsz, num_heads, head_dim, state_size]
|
||
|
|
dA = (torch.exp(dt[..., None] * A)).to(device=cache_device)
|
||
|
|
|
||
|
|
# Discretize B
|
||
|
|
# [bsz, n_groups * state_size] -> [bsz, n_groups, 1, state_size] ->
|
||
|
|
# -> [bsz, n_groups, group to head repetition factor, state_size] -> [bsz, num_heads, state_size]
|
||
|
|
B = B.reshape(batch_size, self.n_groups, -1)[..., None, :]
|
||
|
|
B = B.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, B.shape[-1]).contiguous()
|
||
|
|
B = B.reshape(batch_size, -1, B.shape[-1])
|
||
|
|
# [bsz, num_heads, head_dim, state_size]
|
||
|
|
dB = dt[..., None] * B[..., None, :]
|
||
|
|
|
||
|
|
# Discretize x into dB
|
||
|
|
# [bsz, intermediate_size] -> [bsz, num_heads, head_dim]
|
||
|
|
hidden_states = hidden_states.reshape(batch_size, -1, self.head_dim)
|
||
|
|
dBx = (dB * hidden_states[..., None]).to(device=cache_device)
|
||
|
|
|
||
|
|
# State calculation
|
||
|
|
cache_params.update_ssm_state(
|
||
|
|
layer_idx=self.layer_idx,
|
||
|
|
new_ssm_state=cache_params.ssm_states[self.layer_idx] * dA + dBx
|
||
|
|
)
|
||
|
|
|
||
|
|
# Subsequent output
|
||
|
|
# [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
|
||
|
|
C = C.reshape(batch_size, self.n_groups, -1)[..., None, :]
|
||
|
|
C = C.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, C.shape[-1]).contiguous()
|
||
|
|
C = C.reshape(batch_size, -1, C.shape[-1])
|
||
|
|
# [bsz, num_heads, head_dim]
|
||
|
|
|
||
|
|
ssm_states = cache_params.ssm_states[self.layer_idx].to(device=C.device, dtype=C.dtype) # Shape: [b, h, d, n]
|
||
|
|
# Reshape ssm_states to merge the first two dimensions
|
||
|
|
ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size) # Shape: [b*h, d, n]
|
||
|
|
C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1) # Shape: [b*h, n, 1]
|
||
|
|
y = torch.bmm(ssm_states_reshaped, C_reshaped)
|
||
|
|
y = y.view(batch_size, self.num_heads, self.head_dim)
|
||
|
|
|
||
|
|
# D skip connection
|
||
|
|
# [num_heads] -> [num_heads, head_dim]
|
||
|
|
D = self.D[..., None].expand(self.D.shape[0], self.head_dim)
|
||
|
|
y = (y + hidden_states * D).to(y.dtype)
|
||
|
|
|
||
|
|
# [bsz, num_heads, head_dim] -> [bsz, 1, intermediate_size]
|
||
|
|
y = y.reshape(batch_size, -1)[:, None, ...]
|
||
|
|
else:
|
||
|
|
# begin ssd naive implementation without einsums
|
||
|
|
dt = nn.functional.softplus(dt + self.dt_bias)
|
||
|
|
dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1])
|
||
|
|
hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).float()
|
||
|
|
B = B.reshape(batch_size, seq_len, -1, self.ssm_state_size).float()
|
||
|
|
C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).float()
|
||
|
|
B = B.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads)
|
||
|
|
C = C.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads)
|
||
|
|
pad_size = (self.chunk_size - seq_len % self.chunk_size) % self.chunk_size
|
||
|
|
|
||
|
|
D_residual = self.D[..., None] * pad_tensor_by_size(hidden_states, pad_size)
|
||
|
|
|
||
|
|
# Discretize x and A
|
||
|
|
hidden_states = hidden_states * dt[..., None]
|
||
|
|
A = A.to(hidden_states.dtype) * dt
|
||
|
|
|
||
|
|
# Rearrange into blocks/chunks
|
||
|
|
hidden_states, A, B, C = [reshape_into_chunks(t, pad_size, self.chunk_size) for t in (hidden_states, A, B, C)]
|
||
|
|
|
||
|
|
# [bsz, -1, chunk_size, num_heads] -> [bsz, num_heads, -1, chunk_size]
|
||
|
|
A = A.permute(0, 3, 1, 2)
|
||
|
|
A_cumsum = torch.cumsum(A, dim=-1)
|
||
|
|
|
||
|
|
# 1. Compute the output for each intra-chunk (diagonal blocks)
|
||
|
|
# This is the analog of a causal mask
|
||
|
|
L = torch.exp(segment_sum(A))
|
||
|
|
|
||
|
|
# Contraction of C and B to get G (attention-weights like)
|
||
|
|
G_intermediate = C[:, :, :, None, :, :] * B[:, :, None, :, :, :] # shape: (b, c, l, s, h, n)
|
||
|
|
G = G_intermediate.sum(dim=-1) # shape: (b, c, l, s, h)
|
||
|
|
|
||
|
|
# Compute M, equivalent to applying attention mask to weights
|
||
|
|
M_intermediate = G[..., None] * L.permute(0, 2, 3, 4, 1)[..., None]
|
||
|
|
M = M_intermediate.sum(dim=-1)
|
||
|
|
|
||
|
|
# Compute Y_diag (apply to values)
|
||
|
|
Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(dim=3)
|
||
|
|
|
||
|
|
# 2. Compute the state for each intra-chunk
|
||
|
|
# (right term of low-rank factorization of off-diagonal blocks; B terms)
|
||
|
|
decay_states = torch.exp(A_cumsum[:, :, :, -1:] - A_cumsum)
|
||
|
|
B_decay = B * decay_states.permute(0, -2, -1, 1)[..., None]
|
||
|
|
states = (B_decay[..., None, :] * hidden_states[..., None]).sum(dim=2)
|
||
|
|
|
||
|
|
# 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries
|
||
|
|
# (middle term of factorization of off-diag blocks; A terms)
|
||
|
|
if cache_params is not None and cache_position is not None and cache_position[0] > 0:
|
||
|
|
previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...].to(device=states.device)
|
||
|
|
else:
|
||
|
|
previous_states = torch.zeros_like(states[:, :1])
|
||
|
|
states = torch.cat([previous_states, states], dim=1)
|
||
|
|
decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
|
||
|
|
decay_chunk = decay_chunk.transpose(1, 3)
|
||
|
|
new_states = (decay_chunk[..., None, None] * states[:, :, None, ...]).sum(dim=1)
|
||
|
|
states, ssm_state = new_states[:, :-1], new_states[:, -1]
|
||
|
|
|
||
|
|
# 4. Compute state -> output conversion per chunk
|
||
|
|
# (left term of low-rank factorization of off-diagonal blocks; C terms)
|
||
|
|
state_decay_out = torch.exp(A_cumsum)
|
||
|
|
C_times_states = (C[..., None, :] * states[:, :, None, ...])
|
||
|
|
state_decay_out_permuted = state_decay_out.permute(0, 2, 3, 1)
|
||
|
|
Y_off = (C_times_states.sum(-1) * state_decay_out_permuted[..., None])
|
||
|
|
|
||
|
|
# Add output of intra-chunk and inter-chunk terms (diagonal and off-diagonal blocks)
|
||
|
|
y = Y_diag + Y_off
|
||
|
|
# [bsz, -1, self.chunk_size, num_heads, head_dim] -> [bsz, (padded) seq_len, num_heads, head_dim]
|
||
|
|
y = y.reshape(batch_size, -1, self.num_heads, self.head_dim)
|
||
|
|
|
||
|
|
y = y + D_residual
|
||
|
|
# Cutting off padded chunks
|
||
|
|
if pad_size > 0:
|
||
|
|
y = y[:, :seq_len, :, :]
|
||
|
|
y = y.reshape(batch_size, seq_len, -1)
|
||
|
|
|
||
|
|
# Init cache
|
||
|
|
if ssm_state is not None and cache_params is not None:
|
||
|
|
cache_params.update_ssm_state(layer_idx=self.layer_idx, new_ssm_state=ssm_state)
|
||
|
|
|
||
|
|
scan_output = self.norm(y, gate)
|
||
|
|
|
||
|
|
# end ssd naive
|
||
|
|
|
||
|
|
# 4. Final linear projection
|
||
|
|
contextualized_states = self.out_proj(scan_output.to(dtype)) # [batch, seq_len, hidden_size]
|
||
|
|
return contextualized_states
|
||
|
|
# fmt: on
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hidden_states,
|
||
|
|
cache_params: Optional[Mamba2Cache] = None,
|
||
|
|
cache_position: Optional[torch.LongTensor] = None,
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
):
|
||
|
|
if is_fast_path_available and "cuda" in self.in_proj.weight.device.type:
|
||
|
|
return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask)
|
||
|
|
return self.torch_forward(hidden_states, cache_params, cache_position, attention_mask)
|
||
|
|
|
||
|
|
|
||
|
|
class Mamba2RMSNorm(nn.Module):
|
||
|
|
def __init__(self, hidden_size, eps=1e-6):
|
||
|
|
"""
|
||
|
|
Mamba2RMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm
|
||
|
|
"""
|
||
|
|
super().__init__()
|
||
|
|
self.weight = nn.Parameter(torch.ones(hidden_size))
|
||
|
|
self.variance_epsilon = eps
|
||
|
|
|
||
|
|
def forward(self, hidden_states):
|
||
|
|
input_dtype = hidden_states.dtype
|
||
|
|
hidden_states = hidden_states.to(torch.float32)
|
||
|
|
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
||
|
|
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
||
|
|
return self.weight * hidden_states.to(input_dtype)
|
||
|
|
|
||
|
|
|
||
|
|
class Mamba2Block(GradientCheckpointingLayer):
|
||
|
|
def __init__(self, config, layer_idx):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
self.layer_idx = layer_idx
|
||
|
|
self.residual_in_fp32 = config.residual_in_fp32
|
||
|
|
self.norm = Mamba2RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
|
||
|
|
self.mixer = Mamba2Mixer(config, layer_idx=layer_idx)
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hidden_states,
|
||
|
|
cache_params: Optional[Mamba2Cache] = None,
|
||
|
|
cache_position: Optional[torch.LongTensor] = None,
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
):
|
||
|
|
residual = hidden_states
|
||
|
|
hidden_states = self.norm(hidden_states.to(dtype=self.norm.weight.dtype))
|
||
|
|
if self.residual_in_fp32:
|
||
|
|
residual = residual.to(torch.float32)
|
||
|
|
|
||
|
|
hidden_states = self.mixer(
|
||
|
|
hidden_states, cache_params=cache_params, cache_position=cache_position, attention_mask=attention_mask
|
||
|
|
)
|
||
|
|
hidden_states = residual + hidden_states
|
||
|
|
return hidden_states
|
||
|
|
|
||
|
|
|
||
|
|
@auto_docstring
|
||
|
|
class Mamba2PreTrainedModel(PreTrainedModel):
|
||
|
|
config: Mamba2Config
|
||
|
|
base_model_prefix = "backbone"
|
||
|
|
_no_split_modules = ["Mamba2Block"]
|
||
|
|
supports_gradient_checkpointing = True
|
||
|
|
_is_stateful = True
|
||
|
|
|
||
|
|
def _init_weights(self, module):
|
||
|
|
"""Initialize the weights."""
|
||
|
|
std = self.config.initializer_range
|
||
|
|
if isinstance(module, Mamba2Mixer):
|
||
|
|
# 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.config.num_heads + 1)
|
||
|
|
module.A_log.copy_(torch.log(A))
|
||
|
|
module.A_log._no_weight_decay = True
|
||
|
|
module.D._no_weight_decay = True
|
||
|
|
module.D.data.fill_(1.0)
|
||
|
|
|
||
|
|
dt = torch.exp(
|
||
|
|
torch.rand(self.config.num_heads)
|
||
|
|
* (math.log(self.config.time_step_max) - math.log(self.config.time_step_min))
|
||
|
|
+ math.log(self.config.time_step_min)
|
||
|
|
).clamp(min=self.config.time_step_floor)
|
||
|
|
|
||
|
|
# # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
|
||
|
|
inv_dt = dt + torch.log(-torch.expm1(-dt))
|
||
|
|
module.dt_bias.copy_(inv_dt)
|
||
|
|
module.dt_bias._no_reinit = True
|
||
|
|
|
||
|
|
nn.init.kaiming_uniform_(module.conv1d.weight, a=math.sqrt(5))
|
||
|
|
if module.conv1d.bias is not None:
|
||
|
|
if not getattr(module.conv1d.bias, "_no_reinit", False):
|
||
|
|
nn.init.zeros_(module.conv1d.bias)
|
||
|
|
nn.init.kaiming_uniform_(module.out_proj.weight, a=math.sqrt(5))
|
||
|
|
|
||
|
|
if self.config.rescale_prenorm_residual:
|
||
|
|
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
|
||
|
|
# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
|
||
|
|
# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
|
||
|
|
# > -- GPT-2 :: https://openai.com/blog/better-language-models/
|
||
|
|
#
|
||
|
|
# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
|
||
|
|
# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
|
||
|
|
# Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
|
||
|
|
# We need to reinit p since this code could be called multiple times
|
||
|
|
# Having just p *= scale would repeatedly scale it down
|
||
|
|
p = module.out_proj.weight
|
||
|
|
p /= math.sqrt(self.config.num_hidden_layers)
|
||
|
|
|
||
|
|
if isinstance(module, nn.Linear):
|
||
|
|
if not getattr(module.weight, "_no_reinit", False):
|
||
|
|
nn.init.normal_(module.weight, std=std)
|
||
|
|
if module.bias is not None:
|
||
|
|
if not getattr(module.bias, "_no_reinit", False):
|
||
|
|
nn.init.zeros_(module.bias)
|
||
|
|
elif isinstance(module, (Mamba2RMSNorm, MambaRMSNormGated)):
|
||
|
|
module.weight.data.fill_(1.0)
|
||
|
|
elif isinstance(module, nn.Embedding):
|
||
|
|
nn.init.normal_(module.weight, std=std)
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
@auto_docstring(
|
||
|
|
custom_intro="""
|
||
|
|
Class for the MAMBA2 model outputs.
|
||
|
|
"""
|
||
|
|
)
|
||
|
|
# Copied from transformers.models.mamba.modeling_mamba.MambaOutput with MAMBA->MAMBA2,Mamba->Mamba2
|
||
|
|
class Mamba2Output(ModelOutput):
|
||
|
|
r"""
|
||
|
|
cache_params (`Mamba2Cache`):
|
||
|
|
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
|
||
|
|
"""
|
||
|
|
|
||
|
|
last_hidden_state: Optional[torch.FloatTensor] = None
|
||
|
|
cache_params: Optional[Mamba2Cache] = None
|
||
|
|
hidden_states: Optional[tuple[torch.FloatTensor]] = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
@auto_docstring(
|
||
|
|
custom_intro="""
|
||
|
|
Base class for causal language model (or autoregressive) outputs.
|
||
|
|
"""
|
||
|
|
)
|
||
|
|
# Copied from transformers.models.mamba.modeling_mamba.MambaCausalLMOutput with Mamba->Mamba2
|
||
|
|
class Mamba2CausalLMOutput(ModelOutput):
|
||
|
|
r"""
|
||
|
|
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 (`Mamba2Cache`):
|
||
|
|
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
|
||
|
|
"""
|
||
|
|
|
||
|
|
loss: Optional[torch.FloatTensor] = None
|
||
|
|
logits: Optional[torch.FloatTensor] = None
|
||
|
|
cache_params: Optional[Mamba2Cache] = None
|
||
|
|
hidden_states: Optional[tuple[torch.FloatTensor]] = None
|
||
|
|
|
||
|
|
|
||
|
|
@auto_docstring
|
||
|
|
class Mamba2Model(Mamba2PreTrainedModel):
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__(config)
|
||
|
|
|
||
|
|
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
|
||
|
|
self.layers = nn.ModuleList([Mamba2Block(config, layer_idx=idx) for idx in range(config.num_hidden_layers)])
|
||
|
|
|
||
|
|
self.gradient_checkpointing = False
|
||
|
|
self.norm_f = Mamba2RMSNorm(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
|
||
|
|
|
||
|
|
@auto_docstring
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
input_ids: Optional[torch.LongTensor] = None,
|
||
|
|
inputs_embeds: Optional[torch.LongTensor] = None,
|
||
|
|
cache_params: Optional[Mamba2Cache] = None,
|
||
|
|
use_cache: Optional[bool] = None,
|
||
|
|
output_hidden_states: Optional[bool] = None,
|
||
|
|
return_dict: Optional[bool] = None,
|
||
|
|
cache_position: Optional[torch.LongTensor] = None,
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
**kwargs,
|
||
|
|
) -> Union[tuple, Mamba2Output]:
|
||
|
|
r"""
|
||
|
|
cache_params (`Mamba2Cache`, *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.
|
||
|
|
cache_position (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||
|
|
The position of the current input in the cache. This is used to ensure that the cache is correctly updated.
|
||
|
|
If `cache_params` is passed, `cache_position` should also be passed.
|
||
|
|
"""
|
||
|
|
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 must specify exactly one of input_ids or inputs_embeds")
|
||
|
|
|
||
|
|
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 use_cache:
|
||
|
|
if cache_params is None:
|
||
|
|
cache_params = Mamba2Cache(
|
||
|
|
self.config, inputs_embeds.size(0), device=inputs_embeds.device, dtype=inputs_embeds.dtype
|
||
|
|
)
|
||
|
|
cache_position = torch.arange(0, self.config.conv_kernel, device=inputs_embeds.device)
|
||
|
|
elif cache_position is None:
|
||
|
|
# cases when we do manual forward instead of using `model.generate` which will initiate
|
||
|
|
# `cache_position` and makes sure it is not None, throw error here instead of doing some
|
||
|
|
# hack to conjecture the current cache position
|
||
|
|
raise ValueError(
|
||
|
|
"You have to specify the `cache_position` manually when `use_cache=True` and `cache_params` is passed, "
|
||
|
|
"you don't have to pass a `cache_params` if you are in prefilling stage because in that case it will "
|
||
|
|
"be initialized for you automatically"
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
cache_params = None
|
||
|
|
|
||
|
|
hidden_states = inputs_embeds
|
||
|
|
all_hidden_states = () if output_hidden_states else None
|
||
|
|
for mixer_block in self.layers:
|
||
|
|
hidden_states = mixer_block(
|
||
|
|
hidden_states,
|
||
|
|
cache_params=cache_params,
|
||
|
|
cache_position=cache_position,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
)
|
||
|
|
|
||
|
|
if output_hidden_states:
|
||
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
||
|
|
|
||
|
|
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 Mamba2Output(
|
||
|
|
last_hidden_state=hidden_states,
|
||
|
|
cache_params=cache_params if use_cache else None,
|
||
|
|
hidden_states=all_hidden_states,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@auto_docstring(
|
||
|
|
custom_intro="""
|
||
|
|
The MAMBA2 Model transformer with a language modeling head on top (linear layer with weights not tied to the input
|
||
|
|
embeddings).
|
||
|
|
"""
|
||
|
|
)
|
||
|
|
class Mamba2ForCausalLM(Mamba2PreTrainedModel, GenerationMixin):
|
||
|
|
_tied_weights_keys = []
|
||
|
|
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__(config)
|
||
|
|
self.backbone = Mamba2Model(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_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 prepare_inputs_for_generation(
|
||
|
|
self,
|
||
|
|
input_ids,
|
||
|
|
inputs_embeds=None,
|
||
|
|
use_cache=None,
|
||
|
|
cache_params: Optional[Mamba2Cache] = None,
|
||
|
|
cache_position: Optional[torch.LongTensor] = None,
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
# Overwritten -- uses `cache_params` as opposed to `past_key_values`
|
||
|
|
model_inputs = {"input_ids": input_ids.contiguous()}
|
||
|
|
if use_cache and cache_params is None:
|
||
|
|
# we initialize the `cache_position` to full size of `conv_states` at prefill stage
|
||
|
|
# considering padding will be applied when input length is shorter, and truncation
|
||
|
|
# will be applied when it is longer, so it will be equivalent to always have it match
|
||
|
|
# the length of `cache_params.conv_states`, which is `config.conv_kernel`
|
||
|
|
cache_position = torch.arange(0, self.backbone.config.conv_kernel, device=input_ids.device)
|
||
|
|
if inputs_embeds is not None:
|
||
|
|
model_inputs = {"inputs_embeds": inputs_embeds}
|
||
|
|
max_batch_size = inputs_embeds.size(0)
|
||
|
|
else:
|
||
|
|
max_batch_size = input_ids.size(0)
|
||
|
|
cache_params = Mamba2Cache(self.backbone.config, max_batch_size, device=self.device, dtype=self.dtype)
|
||
|
|
|
||
|
|
if use_cache and cache_position[0] > 0:
|
||
|
|
model_inputs["input_ids"] = input_ids[:, -1].unsqueeze(-1).contiguous()
|
||
|
|
attention_mask = None
|
||
|
|
|
||
|
|
if not use_cache and inputs_embeds is not None:
|
||
|
|
model_inputs = {"inputs_embeds": inputs_embeds}
|
||
|
|
|
||
|
|
model_inputs.update(
|
||
|
|
{
|
||
|
|
"cache_params": cache_params,
|
||
|
|
"use_cache": use_cache,
|
||
|
|
"cache_position": cache_position,
|
||
|
|
"attention_mask": attention_mask,
|
||
|
|
}
|
||
|
|
)
|
||
|
|
return model_inputs
|
||
|
|
|
||
|
|
@auto_docstring
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
input_ids: Optional[torch.LongTensor] = None,
|
||
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
|
|
cache_params: Optional[Mamba2Cache] = None,
|
||
|
|
labels: Optional[torch.LongTensor] = None,
|
||
|
|
output_hidden_states: Optional[bool] = None,
|
||
|
|
return_dict: Optional[bool] = None,
|
||
|
|
use_cache: Optional[bool] = None,
|
||
|
|
cache_position: Optional[torch.Tensor] = None,
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
**kwargs, # for now we need this for generation and loss_function
|
||
|
|
) -> Union[tuple, Mamba2CausalLMOutput]:
|
||
|
|
r"""
|
||
|
|
cache_params (`Mamba2Cache`, *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).
|
||
|
|
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]`
|
||
|
|
use_cache (`bool`, *optional*):
|
||
|
|
If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
|
||
|
|
cache_position (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||
|
|
The position of the current input in the cache. This is used to ensure that the cache is correctly updated.
|
||
|
|
If `cache_params` is passed, `cache_position` should also be passed.
|
||
|
|
"""
|
||
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
|
||
|
|
mamba2_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,
|
||
|
|
cache_position=cache_position,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
)
|
||
|
|
hidden_states = mamba2_outputs[0]
|
||
|
|
|
||
|
|
logits = self.lm_head(hidden_states.to(self.lm_head.weight.dtype)).float()
|
||
|
|
|
||
|
|
loss = None
|
||
|
|
if labels is not None:
|
||
|
|
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
output = (logits,) + mamba2_outputs[1:]
|
||
|
|
return ((loss,) + output) if loss is not None else output
|
||
|
|
|
||
|
|
return Mamba2CausalLMOutput(
|
||
|
|
loss=loss,
|
||
|
|
logits=logits,
|
||
|
|
cache_params=mamba2_outputs.cache_params,
|
||
|
|
hidden_states=mamba2_outputs.hidden_states,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
__all__ = ["Mamba2ForCausalLM", "Mamba2Model", "Mamba2PreTrainedModel"]
|