b2txt25/model_training_nnn/rnn_model.py

import torch
from torch import nn

class GradientReversalFn(torch.autograd.Function):
    """
    Gradient Reversal Layer (GRL)
    Forward: identity
    Backward: multiply incoming gradient by -lambda
    """
    @staticmethod
    def forward(ctx, x, lambd: float):
        ctx.lambd = lambd
        return x.view_as(x)

    @staticmethod
    def backward(ctx, grad_output):
        return -ctx.lambd * grad_output, None

def gradient_reverse(x, lambd: float = 1.0):
    return GradientReversalFn.apply(x, lambd)

class NoiseModel(nn.Module):
    '''
    Noise Model: 2-layer GRU that learns to estimate noise in the neural data
    '''
    def __init__(self,
                 neural_dim,
                 n_units,
                 n_days,
                 rnn_dropout=0.0,
                 input_dropout=0.0,
                 patch_size=0,
                 patch_stride=0):
        super(NoiseModel, self).__init__()

        self.neural_dim = neural_dim
        self.n_units = n_units
        self.n_days = n_days
        self.rnn_dropout = rnn_dropout
        self.input_dropout = input_dropout
        self.patch_size = patch_size
        self.patch_stride = patch_stride

        # Day-specific input layers
        self.day_layer_activation = nn.Softsign()
        # Let Accelerator handle dtype automatically for TPU compatibility
        self.day_weights = nn.ParameterList([nn.Parameter(torch.eye(self.neural_dim)) for _ in range(self.n_days)])
        self.day_biases = nn.ParameterList([nn.Parameter(torch.zeros(1, self.neural_dim)) for _ in range(self.n_days)])
        self.day_layer_dropout = nn.Dropout(input_dropout)

        # Calculate input size after patching
        self.input_size = self.neural_dim
        if self.patch_size > 0:
            self.input_size *= self.patch_size

        # 2-layer GRU for noise estimation
        self.gru = nn.GRU(
            input_size=self.input_size,
            hidden_size=self.input_size,  # Output same dimension as input
            num_layers=2,
            dropout=self.rnn_dropout,
            batch_first=True,
            bidirectional=False,
        )

        # Initialize GRU parameters
        for name, param in self.gru.named_parameters():
            if "weight_hh" in name:
                nn.init.orthogonal_(param)
            if "weight_ih" in name:
                nn.init.xavier_uniform_(param)

        # Learnable initial hidden state - let Accelerator handle dtype
        self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.input_size)))

    def forward(self, x, day_idx, states=None):
        # XLA-friendly day-specific transformation using gather instead of dynamic indexing
        batch_size = x.size(0)

        # Stack all day weights and biases upfront for static indexing
        all_day_weights = torch.stack(list(self.day_weights), dim=0)  # [n_days, neural_dim, neural_dim]
        all_day_biases = torch.stack([bias.squeeze(0) for bias in self.day_biases], dim=0)  # [n_days, neural_dim]

        # XLA-friendly gather operation
        day_weights = torch.index_select(all_day_weights, 0, day_idx)  # [batch_size, neural_dim, neural_dim]
        day_biases = torch.index_select(all_day_biases, 0, day_idx).unsqueeze(1)  # [batch_size, 1, neural_dim]

        # Use bmm (batch matrix multiply) which is highly optimized in XLA
        # Ensure dtype consistency for mixed precision training
        x = torch.bmm(x, day_weights.to(x.dtype)) + day_biases.to(x.dtype)
        x = self.day_layer_activation(x)

        # XLA-friendly conditional dropout
        if self.input_dropout > 0:
            x = self.day_layer_dropout(x)

        # Apply patch processing if enabled (keep conditional for now, optimize later)
        if self.patch_size > 0:
            x = x.unsqueeze(1)
            x = x.permute(0, 3, 1, 2)
            x_unfold = x.unfold(3, self.patch_size, self.patch_stride)
            x_unfold = x_unfold.squeeze(2)
            x_unfold = x_unfold.permute(0, 2, 3, 1)
            x = x_unfold.reshape(batch_size, x_unfold.size(1), -1)

        # XLA-friendly hidden state initialization - avoid dynamic allocation
        if states is None:
            states = self.h0.expand(2, batch_size, self.input_size).contiguous()

        # GRU forward pass
        output, hidden_states = self.gru(x, states)

        return output, hidden_states


class CleanSpeechModel(nn.Module):
    '''
    Clean Speech Model: 3-layer GRU that processes denoised signal for speech recognition
    '''
    def __init__(self,
                 neural_dim,
                 n_units,
                 n_days,
                 n_classes,
                 rnn_dropout=0.0,
                 input_dropout=0.0,
                 patch_size=0,
                 patch_stride=0):
        super(CleanSpeechModel, self).__init__()

        self.neural_dim = neural_dim
        self.n_units = n_units
        self.n_days = n_days
        self.n_classes = n_classes
        self.rnn_dropout = rnn_dropout
        self.input_dropout = input_dropout
        self.patch_size = patch_size
        self.patch_stride = patch_stride

        # Day-specific input layers
        self.day_layer_activation = nn.Softsign()
        # Let Accelerator handle dtype automatically for TPU compatibility
        self.day_weights = nn.ParameterList([nn.Parameter(torch.eye(self.neural_dim)) for _ in range(self.n_days)])
        self.day_biases = nn.ParameterList([nn.Parameter(torch.zeros(1, self.neural_dim)) for _ in range(self.n_days)])
        self.day_layer_dropout = nn.Dropout(input_dropout)

        # Calculate input size after patching
        self.input_size = self.neural_dim
        if self.patch_size > 0:
            self.input_size *= self.patch_size

        # 3-layer GRU for clean speech recognition
        self.gru = nn.GRU(
            input_size=self.input_size,
            hidden_size=self.n_units,
            num_layers=3,
            dropout=self.rnn_dropout,
            batch_first=True,
            bidirectional=False,
        )

        # Initialize GRU parameters
        for name, param in self.gru.named_parameters():
            if "weight_hh" in name:
                nn.init.orthogonal_(param)
            if "weight_ih" in name:
                nn.init.xavier_uniform_(param)

        # Output classification layer
        self.out = nn.Linear(self.n_units, self.n_classes)
        nn.init.xavier_uniform_(self.out.weight)

        # Learnable initial hidden state
        self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.n_units)))

    def forward(self, x, day_idx, states=None, return_state=False):
        # XLA-friendly day-specific transformation using gather instead of dynamic indexing
        batch_size = x.size(0)

        # Stack all day weights and biases upfront for static indexing
        all_day_weights = torch.stack(list(self.day_weights), dim=0)  # [n_days, neural_dim, neural_dim]
        all_day_biases = torch.stack([bias.squeeze(0) for bias in self.day_biases], dim=0)  # [n_days, neural_dim]

        # XLA-friendly gather operation
        day_weights = torch.index_select(all_day_weights, 0, day_idx)  # [batch_size, neural_dim, neural_dim]
        day_biases = torch.index_select(all_day_biases, 0, day_idx).unsqueeze(1)  # [batch_size, 1, neural_dim]

        # Use bmm (batch matrix multiply) which is highly optimized in XLA
        # Ensure dtype consistency for mixed precision training
        x = torch.bmm(x, day_weights.to(x.dtype)) + day_biases.to(x.dtype)
        x = self.day_layer_activation(x)

        if self.input_dropout > 0:
            x = self.day_layer_dropout(x)

        # Apply patch processing if enabled
        if self.patch_size > 0:
            x = x.unsqueeze(1)
            x = x.permute(0, 3, 1, 2)
            x_unfold = x.unfold(3, self.patch_size, self.patch_stride)
            x_unfold = x_unfold.squeeze(2)
            x_unfold = x_unfold.permute(0, 2, 3, 1)
            x = x_unfold.reshape(batch_size, x_unfold.size(1), -1)

        # XLA-friendly hidden state initialization
        if states is None:
            states = self.h0.expand(3, batch_size, self.n_units).contiguous()

        # GRU forward pass
        output, hidden_states = self.gru(x, states)

        # Classification
        logits = self.out(output)

        if return_state:
            return logits, hidden_states
        return logits


class NoisySpeechModel(nn.Module):
    '''
    Noisy Speech Model: 2-layer GRU that processes noise signal for speech recognition
    '''
    def __init__(self,
                 neural_dim,
                 n_units,
                 n_days,
                 n_classes,
                 rnn_dropout=0.0,
                 input_dropout=0.0,
                 patch_size=0,
                 patch_stride=0):
        super(NoisySpeechModel, self).__init__()

        self.neural_dim = neural_dim
        self.n_units = n_units
        self.n_days = n_days
        self.n_classes = n_classes
        self.rnn_dropout = rnn_dropout
        self.input_dropout = input_dropout
        self.patch_size = patch_size
        self.patch_stride = patch_stride

        # Calculate input size after patching
        self.input_size = self.neural_dim
        if self.patch_size > 0:
            self.input_size *= self.patch_size

        # 2-layer GRU for noisy speech recognition
        self.gru = nn.GRU(
            input_size=self.input_size,
            hidden_size=self.n_units,
            num_layers=2,
            dropout=self.rnn_dropout,
            batch_first=True,
            bidirectional=False,
        )

        # Initialize GRU parameters
        for name, param in self.gru.named_parameters():
            if "weight_hh" in name:
                nn.init.orthogonal_(param)
            if "weight_ih" in name:
                nn.init.xavier_uniform_(param)

        # Output classification layer
        self.out = nn.Linear(self.n_units, self.n_classes)
        nn.init.xavier_uniform_(self.out.weight)

        # Learnable initial hidden state
        self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.n_units)))

    def forward(self, x, states=None, return_state=False):
        # Note: NoisySpeechModel doesn't need day-specific layers as it processes noise
        batch_size = x.size(0)

        # XLA-friendly hidden state initialization
        if states is None:
            states = self.h0.expand(2, batch_size, self.n_units).contiguous()

        # GRU forward pass
        output, hidden_states = self.gru(x, states)

        # Classification
        logits = self.out(output)

        if return_state:
            return logits, hidden_states
        return logits


class TripleGRUDecoder(nn.Module):
    '''
    Three-model adversarial architecture for neural speech decoding

    Combines:
    - NoiseModel: estimates noise in neural data
    - CleanSpeechModel: processes denoised signal for recognition
    - NoisySpeechModel: processes noise signal for recognition
    '''
    def __init__(self,
                 neural_dim,
                 n_units,
                 n_days,
                 n_classes,
                 rnn_dropout=0.0,
                 input_dropout=0.0,
                 patch_size=0,
                 patch_stride=0,
                 ):
        '''
        neural_dim  (int)      - number of channels in a single timestep (e.g. 512)
        n_units     (int)      - number of hidden units in each recurrent layer
        n_days      (int)      - number of days in the dataset
        n_classes   (int)      - number of classes (phonemes)
        rnn_dropout    (float) - percentage of units to dropout during training
        input_dropout (float)  - percentage of input units to dropout during training
        patch_size  (int)      - number of timesteps to concat on initial input layer
        patch_stride(int)      - number of timesteps to stride over when concatenating initial input
        '''
        super(TripleGRUDecoder, self).__init__()

        self.neural_dim = neural_dim
        self.n_units = n_units
        self.n_classes = n_classes
        self.n_days = n_days

        self.rnn_dropout = rnn_dropout
        self.input_dropout = input_dropout
        self.patch_size = patch_size
        self.patch_stride = patch_stride

        # Create the three models
        self.noise_model = NoiseModel(
            neural_dim=neural_dim,
            n_units=n_units,
            n_days=n_days,
            rnn_dropout=rnn_dropout,
            input_dropout=input_dropout,
            patch_size=patch_size,
            patch_stride=patch_stride
        )

        self.clean_speech_model = CleanSpeechModel(
            neural_dim=neural_dim,
            n_units=n_units,
            n_days=n_days,
            n_classes=n_classes,
            rnn_dropout=rnn_dropout,
            input_dropout=input_dropout,
            patch_size=patch_size,
            patch_stride=patch_stride
        )

        self.noisy_speech_model = NoisySpeechModel(
            neural_dim=neural_dim,
            n_units=n_units,
            n_days=n_days,
            n_classes=n_classes,
            rnn_dropout=rnn_dropout,
            input_dropout=input_dropout,
            patch_size=patch_size,
            patch_stride=patch_stride
        )

        # Training mode flag
        self.training_mode = 'full'  # 'full', 'inference'

    def _apply_preprocessing(self, x, day_idx):
        '''XLA-friendly preprocessing with static operations'''
        batch_size = x.size(0)

        # XLA-friendly day-specific transformation using gather instead of dynamic indexing
        all_day_weights = torch.stack(list(self.clean_speech_model.day_weights), dim=0)
        all_day_biases = torch.stack([bias.squeeze(0) for bias in self.clean_speech_model.day_biases], dim=0)

        # XLA-friendly gather operation
        day_weights = torch.index_select(all_day_weights, 0, day_idx)
        day_biases = torch.index_select(all_day_biases, 0, day_idx).unsqueeze(1)

        # Use bmm (batch matrix multiply) which is highly optimized in XLA
        # Ensure dtype consistency for mixed precision training
        x_processed = torch.bmm(x, day_weights.to(x.dtype)) + day_biases.to(x.dtype)
        x_processed = self.clean_speech_model.day_layer_activation(x_processed)

        # Apply patch processing if enabled
        if self.patch_size > 0:
            x_processed = x_processed.unsqueeze(1)
            x_processed = x_processed.permute(0, 3, 1, 2)
            x_unfold = x_processed.unfold(3, self.patch_size, self.patch_stride)
            x_unfold = x_unfold.squeeze(2)
            x_unfold = x_unfold.permute(0, 2, 3, 1)
            x_processed = x_unfold.reshape(batch_size, x_unfold.size(1), -1)

        return x_processed

    def _clean_forward_with_processed_input(self, x_processed, day_idx, states=None):
        '''Forward pass for CleanSpeechModel with already processed input (bypasses day layers and patching)'''
        batch_size = x_processed.size(0)

        # XLA-friendly hidden state initialization
        if states is None:
            states = self.clean_speech_model.h0.expand(3, batch_size, self.clean_speech_model.n_units).contiguous()

        # GRU forward pass (skip preprocessing since input is already processed)
        output, hidden_states = self.clean_speech_model.gru(x_processed, states)

        # Classification
        logits = self.clean_speech_model.out(output)
        return logits

    def _noisy_forward_with_processed_input(self, x_processed, states=None):
        '''Forward pass for NoisySpeechModel with already processed input'''
        batch_size = x_processed.size(0)

        # XLA-friendly hidden state initialization
        if states is None:
            states = self.noisy_speech_model.h0.expand(2, batch_size, self.noisy_speech_model.n_units).contiguous()

        # GRU forward pass (NoisySpeechModel doesn't have day layers anyway)
        output, hidden_states = self.noisy_speech_model.gru(x_processed, states)

        # Classification
        logits = self.noisy_speech_model.out(output)
        return logits

    def forward(self, x, day_idx, states=None, return_state=False, mode='inference', grl_lambda: float = 0.0):
        '''
        Three-model adversarial forward pass

        x        (tensor)  - batch of examples (trials) of shape: (batch_size, time_series_length, neural_dim)
        day_idx  (tensor)  - tensor of day indices for each example in the batch
        states   (dict)    - dictionary with 'noise', 'clean', 'noisy' states or None
        mode     (str)     - 'full' for training (all three models), 'inference' for inference (noise + clean only)
        grl_lambda (float) - when > 0 and mode='full', applies Gradient Reversal to the noise branch input
        '''

        if mode == 'full':
            # Training mode: run all three models

            # 1. Noise model estimates noise in the data
            noise_output, noise_hidden = self.noise_model(x, day_idx,
                                                         states['noise'] if states else None)

            # 2. For residual connection, we need x in the same space as noise_output
            # Apply the same preprocessing that the models use internally
            x_processed = self._apply_preprocessing(x, day_idx)

            # 3. Clean speech model processes denoised signal
            # Ensure dtype consistency for mixed precision training in residual connection
            denoised_input = x_processed - noise_output.to(x_processed.dtype)  # Residual connection in processed space
            # Clean speech model will apply its own preprocessing, so we pass the denoised processed data
            # But we need to reverse the preprocessing first, then let clean model do its own
            # Actually, it's simpler to pass the residual directly to clean model after bypassing its preprocessing
            clean_logits = self._clean_forward_with_processed_input(denoised_input, day_idx,
                                                                   states['clean'] if states else None)

            # 4. Noisy speech model processes noise signal directly (no day layers needed)
            # Optionally apply Gradient Reversal to enforce adversarial training on noise output
            noisy_input = gradient_reverse(noise_output, grl_lambda) if grl_lambda and grl_lambda != 0.0 else noise_output
            noisy_logits = self._noisy_forward_with_processed_input(noisy_input,
                                                                   states['noisy'] if states else None)

            # XLA-friendly return - use tuple instead of dict for better compilation
            if return_state:
                return (clean_logits, noisy_logits, noise_output), noise_hidden
            return clean_logits, noisy_logits, noise_output

        elif mode == 'inference':
            # Inference mode: only noise model + clean speech model

            # 1. Estimate noise
            noise_output, noise_hidden = self.noise_model(x, day_idx,
                                                         states['noise'] if states else None)

            # 2. For residual connection, we need x in the same space as noise_output
            x_processed = self._apply_preprocessing(x, day_idx)

            # 3. Process denoised signal
            # Ensure dtype consistency for mixed precision training in residual connection
            denoised_input = x_processed - noise_output.to(x_processed.dtype)
            clean_logits = self._clean_forward_with_processed_input(denoised_input, day_idx,
                                                                   states['clean'] if states else None)

            # XLA-friendly return - use tuple for consistency
            if return_state:
                return clean_logits, noise_hidden
            return clean_logits

        else:
            raise ValueError(f"Unknown mode: {mode}. Use 'full' or 'inference'")

    def apply_gradient_combination(self, clean_grad, noisy_grad, learning_rate=1e-3):
        '''
        Apply combined gradients to noise model parameters

        clean_grad (tensor) - gradients from clean speech model output layer
        noisy_grad (tensor) - gradients from noisy speech model output layer
        learning_rate (float) - learning rate for gradient update
        '''
        # Combine gradients: negative from clean model, positive from noisy model
        combined_grad = -clean_grad + noisy_grad

        # Apply gradients to noise model parameters
        # This is a simplified implementation - in practice you'd want more sophisticated update rules
        with torch.no_grad():
            for param in self.noise_model.parameters():
                if param.grad is not None:
                    # Scale the combined gradient appropriately
                    # This is a placeholder - you'd need to implement proper gradient mapping
                    param.data -= learning_rate * combined_grad.mean() * torch.ones_like(param.data)

    def set_mode(self, mode):
        '''Set the operating mode'''
        self.training_mode = mode