transformer_fnqs.py

import jax
import jax.numpy as jnp

from flax import linen as nn
import jax.numpy as jnp
from einops import rearrange
from .attentions import FMHA

def extract_patches1d(x, b):
    return rearrange(x, 'batch (L_eff b) -> batch L_eff b', b=b)

def extract_patches2d(x, b):
    batch = x.shape[0]
    L_eff = int((x.shape[1] // b**2)**0.5)
    x = x.reshape(batch, L_eff, b, L_eff, b)   # [L_eff, b, L_eff, b]
    x = x.transpose(0, 1, 3, 2, 4)         # [L_eff, L_eff, b, b]
    # flatten the patches
    x = x.reshape(batch, L_eff, L_eff, -1)     # [L_eff, L_eff, b*b]
    x = x.reshape(batch, L_eff*L_eff, -1)      # [L_eff*L_eff, b*b]
    return x

def log_cosh(x):
    sgn_x = -2 * jnp.signbit(x.real) + 1
    x = x * sgn_x
    return x + jnp.log1p(jnp.exp(-2.0 * x)) - jnp.log(2.0)

class Embed(nn.Module):
    d_model : int
    b: int
    two_dimensional: bool = False

    def setup(self):
        if self.two_dimensional:
            self.extract_patches = extract_patches2d
        else:
            self.extract_patches = extract_patches1d

        self.embed = nn.Dense(self.d_model, kernel_init=nn.initializers.xavier_uniform(), param_dtype=jnp.float64, dtype=jnp.float64)

    def __call__(self, x):
        x = self.extract_patches(x, self.b)
        x = self.embed(x)

        return x

class EncoderBlock(nn.Module):
    d_model : int
    h: int
    L_eff: int
    transl_invariant: bool = True
    two_dimensional: bool = False

    def setup(self):
        self.attn = FMHA(d_model=self.d_model, h=self.h, L_eff=self.L_eff, transl_invariant=self.transl_invariant, two_dimensional=self.two_dimensional)
        
        self.layer_norm_1 = nn.LayerNorm(dtype=jnp.float64, param_dtype=jnp.float64)
        self.layer_norm_2 = nn.LayerNorm(dtype=jnp.float64, param_dtype=jnp.float64)

        self.ff = nn.Sequential([
            nn.Dense(2*self.d_model, kernel_init=nn.initializers.xavier_uniform(), param_dtype=jnp.float64, dtype=jnp.float64),
            nn.relu,
            nn.Dense(self.d_model, kernel_init=nn.initializers.xavier_uniform(), param_dtype=jnp.float64, dtype=jnp.float64),
        ])

    def __call__(self, x):
        x = x + self.attn(self.layer_norm_1(x))

        x = x + self.ff( self.layer_norm_2(x) )
        return x
    
class Encoder(nn.Module):
    num_layers: int
    d_model : int
    h: int
    L_eff: int
    transl_invariant: bool = True
    two_dimensional: bool = False

    def setup(self):
        self.layers = [EncoderBlock(d_model=self.d_model, h=self.h, L_eff=self.L_eff, transl_invariant=self.transl_invariant, two_dimensional=self.two_dimensional) for _ in range(self.num_layers)]

    def __call__(self, x):
        
        for l in self.layers:
            x = l(x)

        return x

class OuputHead(nn.Module):
    d_model : int
    complex: bool = False

    def setup(self):
        self.out_layer_norm = nn.LayerNorm(dtype=jnp.float64, param_dtype=jnp.float64)

        self.norm0 = nn.LayerNorm(use_scale=True, use_bias=True, dtype=jnp.float64, param_dtype=jnp.float64)
        self.norm1 = nn.LayerNorm(use_scale=True, use_bias=True, dtype=jnp.float64, param_dtype=jnp.float64)

        self.output_layer0 = nn.Dense(self.d_model, param_dtype=jnp.float64, dtype=jnp.float64, kernel_init=nn.initializers.xavier_uniform(), bias_init=jax.nn.initializers.zeros)
        self.output_layer1 = nn.Dense(self.d_model, param_dtype=jnp.float64, dtype=jnp.float64, kernel_init=nn.initializers.xavier_uniform(), bias_init=jax.nn.initializers.zeros)

    def __call__(self, x, return_z=False):

        z = self.out_layer_norm(x.sum(axis=1))

        if return_z:
            return z
        
        amp = self.norm0(self.output_layer0(z))
        
        if self.complex:
            sign = self.norm1(self.output_layer1(z))
            out = amp + 1j*sign
        else:
            out = amp

        return jnp.sum(log_cosh(out), axis=-1)

class ViTFNQS(nn.Module):
    num_layers: int
    d_model : int
    heads: int
    L_eff: int
    b: int
    complex: bool = False
    disorder: bool = False
    transl_invariant: bool = True
    two_dimensional: bool = False

    def setup(self):
        if self.disorder:
            self.patches_and_embed = Embed(self.d_model//2, self.b, two_dimensional=self.two_dimensional)
            self.patches_and_embed_coup = Embed(self.d_model//2, self.b, two_dimensional=self.two_dimensional)
        else:
            self.embed = nn.Dense(self.d_model, kernel_init=nn.initializers.xavier_uniform(), param_dtype=jnp.float64, dtype=jnp.float64)

        self.encoder = Encoder(num_layers=self.num_layers, d_model=self.d_model, h=self.heads, L_eff=self.L_eff, transl_invariant=self.transl_invariant, two_dimensional=self.two_dimensional)

        self.output = OuputHead(self.d_model, complex=self.complex)

    def __call__(self, spins, coups, return_z=False):
        x = jnp.atleast_2d(spins)

        if self.disorder:
            x_spins = self.patches_and_embed(x)
            x_coups = self.patches_and_embed(coups)
            x = jnp.concatenate((x_spins, x_coups), axis=-1)
        else:
            if self.two_dimensional:
                x = extract_patches2d(x, self.b)
            else:
                x = extract_patches1d(x, self.b)
            coups = jnp.broadcast_to(coups, (x.shape[0], x.shape[1], 1))
            # coups = jnp.repeat(coups[:, None], repeats=x.shape[1], axis=1)
            x = jnp.concatenate((x, coups), axis=-1)
            x = self.embed(x)

        x = self.encoder(x)

        out = self.output(x, return_z=return_z)

        return out