modeling_mamba.py

import torch.nn as nn
import torch 
from configuration_mamba import MambaConfig
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.modeling_utils import PreTrainedModel
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
import math
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass
from einops import rearrange, repeat, einsum
from typing import Optional , Union ,Tuple

# Dear contributors of the https://github.com/johnma2006/mamba-minimal/tree/master repository, special thanks to Albert Gu and Tri Dao for their articles. (https://arxiv.org/abs/2312.00752)


class MambaRMSNorm(nn.Module):
    def __init__(self,
                 d_model: int,
                 eps: float = 1e-5):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(d_model))
    def forward(self, x):
        output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight
        return output
    

class MambaBlock(nn.Module):
    def __init__(self, config: MambaConfig):
        """A single Mamba block, as described in Figure 3 in Section 3.4 in the Mamba paper [1]."""
        super().__init__()
        self.config = config

        self.in_proj = nn.Linear(config.d_model, config.d_inner * 2, bias=config.bias)

        self.conv1d = nn.Conv1d(
            in_channels=config.d_inner,
            out_channels=config.d_inner,
            bias=config.conv_bias,
            kernel_size=config.d_conv,
            groups=config.d_inner,
            padding=config.d_conv - 1,
        )

        # x_proj takes in `x` and outputs the input-specific Δ, B, C
        self.x_proj = nn.Linear(config.d_inner, config.dt_rank + config.d_state * 2, bias=False)
        
        # dt_proj projects Δ from dt_rank to d_in
        self.dt_proj = nn.Linear(config.dt_rank, config.d_inner, bias=True)

        A = repeat(torch.arange(1, config.d_state + 1), 'n -> d n', d=config.d_inner)
        self.A_log = nn.Parameter(torch.log(A))
        self.D = nn.Parameter(torch.ones(config.d_inner))
        self.out_proj = nn.Linear(config.d_inner, config.d_model, bias=config.bias)
        self.norm = MambaRMSNorm(config.d_model)

    def forward(self, x):
        """Mamba block forward. This looks the same as Figure 3 in Section 3.4 in the Mamba paper [1].
    
        Args:
            x: shape (b, l, d)    (See Glossary at top for definitions of b, l, d_in, n...)
    
        Returns:
            output: shape (b, l, d)
        
        Official Implementation:
            class Mamba, https://github.com/state-spaces/mamba/blob/main/mamba_ssm/modules/mamba_simple.py#L119
            mamba_inner_ref(), https://github.com/state-spaces/mamba/blob/main/mamba_ssm/ops/selective_scan_interface.py#L311
            
        """

        (b, l, d) = x.shape
        x_copy = x # There was a separate class for residual, I deleted that part and added it here.
        x = self.norm(x)
        x_and_res = self.in_proj(x)  # shape (b, l, 2 * d_in)
        (x, res) = x_and_res.split(split_size=[self.config.d_inner, self.config.d_inner], dim=-1)

        x = rearrange(x, 'b l d_in -> b d_in l')
        x = self.conv1d(x)[:, :, :l]
        x = rearrange(x, 'b d_in l -> b l d_in')
        
        x = F.silu(x)

        y = self.ssm(x)
        
        y = y * F.silu(res)
        
        output = self.out_proj(y) + x_copy

        return output

    
    def ssm(self, x):
        """Runs the SSM. See:
            - Algorithm 2 in Section 3.2 in the Mamba paper [1]
            - run_SSM(A, B, C, u) in The Annotated S4 [2]

        Args:
            x: shape (b, l, d_in)    (See Glossary at top for definitions of b, l, d_in, n...)
    
        Returns:
            output: shape (b, l, d_in)

        Official Implementation:
            mamba_inner_ref(), https://github.com/state-spaces/mamba/blob/main/mamba_ssm/ops/selective_scan_interface.py#L311
            
        """
        (d_in, n) = self.A_log.shape

        # Compute ∆ A B C D, the state space parameters.
        #     A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
        #     ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
        #                                  and is why Mamba is called **selective** state spaces)
        
        A = -torch.exp(self.A_log.float())  # shape (d_in, n)
        D = self.D.float()

        x_dbl = self.x_proj(x)  # (b, l, dt_rank + 2*n)
        
        (delta, B, C) = x_dbl.split(split_size=[self.config.dt_rank, n, n], dim=-1)  # delta: (b, l, dt_rank). B, C: (b, l, n)
        delta = F.softplus(self.dt_proj(delta))  # (b, l, d_in)
        
        y = self.selective_scan(x, delta, A, B, C, D)  # This is similar to run_SSM(A, B, C, u) in The Annotated S4 [2]
        
        return y

    
    def selective_scan(self, u, delta, A, B, C, D):
        """Does selective scan algorithm. See:
            - Section 2 State Space Models in the Mamba paper [1]
            - Algorithm 2 in Section 3.2 in the Mamba paper [1]
            - run_SSM(A, B, C, u) in The Annotated S4 [2]

        This is the classic discrete state space formula:
            x(t + 1) = Ax(t) + Bu(t)
            y(t)     = Cx(t) + Du(t)
        except B and C (and the step size delta, which is used for discretization) are dependent on the input x(t).
    
        Args:
            u: shape (b, l, d_in)    (See Glossary at top for definitions of b, l, d_in, n...)
            delta: shape (b, l, d_in)
            A: shape (d_in, n)
            B: shape (b, l, n)
            C: shape (b, l, n)
            D: shape (d_in,)
    
        Returns:
            output: shape (b, l, d_in)
    
        Official Implementation:
            selective_scan_ref(), https://github.com/state-spaces/mamba/blob/main/mamba_ssm/ops/selective_scan_interface.py#L86
            Note: I refactored some parts out of `selective_scan_ref` out, so the functionality doesn't match exactly.
            
        """
        (b, l, d_in) = u.shape
        n = A.shape[1]
        
        # Discretize continuous parameters (A, B)
        # - A is discretized using zero-order hold (ZOH) discretization (see Section 2 Equation 4 in the Mamba paper [1])
        # - B is discretized using a simplified Euler discretization instead of ZOH. From a discussion with authors:
        #   "A is the more important term and the performance doesn't change much with the simplication on B"
        deltaA = torch.exp(einsum(delta, A, 'b l d_in, d_in n -> b d_in l n'))
        deltaB_u = einsum(delta, B, u, 'b l d_in, b l n, b l d_in -> b d_in l n')
        
        # Perform selective scan (see scan_SSM() in The Annotated S4 [2])
        x = torch.zeros((b, d_in, n), device=deltaA.device)
        ys = []    
        for i in range(l):
            x = deltaA[:, :, i] * x + deltaB_u[:, :, i]
            y = einsum(x, C[:, i, :], 'b d_in n, b n -> b d_in')
            ys.append(y)
        y = torch.stack(ys, dim=1)  # shape (b, l, d_in)
        
        y = y + u * D
    
        return y
    
class MambaPreTrainedModel(PreTrainedModel):
    config_class = MambaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["MambaBlock"]

    def _init_weights(self, module):
        std = 0.02
        if isinstance(module, (nn.Linear, nn.Conv1d)):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

class MambaModel(MambaPreTrainedModel):
    def __init__(self, config: MambaConfig):
        """Full Mamba model.
    Mamba model decoder consisting of *config.n_layer* layers. Each layer is a [`MambaBlock`]

    Args:
        config: MambaConfig
    """
        super().__init__(config)
        self.config = config
        
        self.embedding = nn.Embedding(config.vocab_size, config.d_model)
        self.layers = nn.ModuleList([MambaBlock(config) for _ in range(config.n_layer)])
        self.norm_f = MambaRMSNorm(config.d_model)

        self.gradient_checkpointing = False
        self.post_init()

    def get_input_embeddings(self):
        return self.embedding

    def set_input_embeddings(self, value):
        self.embedding = value

    def forward(self,
                input_ids: torch.LongTensor = None,
                return_dict: Optional[bool] = None,
                )-> Union[Tuple, BaseModelOutputWithPast]:
        x = self.embedding(input_ids)
        all_hidden_states = list()
        for layer in self.layers:
            x = layer(x)
            all_hidden_states.append(x)
            
        hidden_states = self.norm_f(x)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
        )
class MambaForCausalLM(MambaPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = MambaModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
        self.lm_head.weight = self.model.embedding.weight
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embedding

    def set_input_embeddings(self, value):
        self.model.embedding = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model
    
    def forward(self,
                input_ids: torch.LongTensor = None,
                labels: Optional[torch.LongTensor] = None,
                output_attentions: Optional[bool] = None,
                output_hidden_states: Optional[bool] = None,
                return_dict: Optional[bool] = None,
                )-> Union[Tuple, CausalLMOutputWithPast]:
        outputs = self.model(
            input_ids=input_ids,
            return_dict=return_dict,
        )
        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits.float()
        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
        )
    
    def prepare_inputs_for_generation(
        self, input_ids, **kwargs
    ):
        model_inputs = {"input_ids": input_ids}
        return model_inputs