model.py

"""
MLX implementation of CAM++ model - ModelScope architecture (Clean implementation)

Based on analysis of iic/speech_campplus_sv_zh_en_16k-common_advanced:
- Dense connections: each layer's output is concatenated with all previous outputs
- TDNN layers use kernel_size=1 (no temporal context in main conv)
- CAM layers provide the actual feature extraction
- Architecture: Input → Dense Blocks (with CAM) → Transitions → Dense Layer
"""

import mlx.core as mx
import mlx.nn as nn
from typing import Dict, List, Optional
import json


class EmbeddedCAM(nn.Module):
    """
    Context-Aware Masking module embedded within TDNN layers

    Architecture (verified from ModelScope weights):
    - linear1: 1x1 Conv (in_channels → cam_channels//2) with bias
    - linear2: 1x1 Conv (cam_channels//2 → cam_channels//4) with bias
    - linear_local: 3x3 Conv (in_channels → cam_channels//4) without bias
    - Output: cam_channels//4 channels (e.g., 32 for cam_channels=128)
    """

    def __init__(self, in_channels: int, cam_channels: int = 128):
        super().__init__()

        # Global context path: 1x1 → 1x1
        self.linear1 = nn.Conv1d(
            in_channels=in_channels,
            out_channels=cam_channels // 2,  # 128 → 64
            kernel_size=1,
            bias=True
        )

        self.linear2 = nn.Conv1d(
            in_channels=cam_channels // 2,  # 64
            out_channels=cam_channels // 4,  # 64 → 32
            kernel_size=1,
            bias=True
        )

        # Local context path: 3x3 conv
        self.linear_local = nn.Conv1d(
            in_channels=in_channels,
            out_channels=cam_channels // 4,  # 128 → 32
            kernel_size=3,
            padding=1,
            bias=False
        )

    def __call__(self, x: mx.array) -> mx.array:
        """
        Apply context-aware masking

        Args:
            x: Input (batch, length, in_channels) - channels_last format

        Returns:
            Output (batch, length, cam_channels//4)
        """
        # Global context: 1x1 → relu → 1x1
        global_context = self.linear1(x)
        global_context = nn.relu(global_context)
        global_context = self.linear2(global_context)

        # Local context: 3x3 conv
        local_context = self.linear_local(x)

        # Apply sigmoid mask
        mask = nn.sigmoid(global_context)
        output = local_context * mask

        return output


class TDNNLayerWithCAM(nn.Module):
    """
    TDNN layer with embedded CAM (verified architecture)

    Flow:
    1. Main conv: kernel_size=1 (channels projection)
    2. BatchNorm
    3. ReLU
    4. CAM: extracts features and outputs cam_channels//4

    Note: The main conv projects to a fixed channel size (e.g., 128),
    then CAM reduces to cam_channels//4 (e.g., 32) for dense connection.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int = 128,
        cam_channels: int = 128
    ):
        super().__init__()

        # Main TDNN: 1x1 conv (no temporal context)
        self.conv = nn.Conv1d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=1,
            padding=0,
            bias=False
        )

        # BatchNorm on the conv output
        self.bn = nn.BatchNorm(out_channels, affine=True)

        # ReLU activation
        self.activation = nn.ReLU()

        # Embedded CAM (takes conv output, produces cam_channels//4)
        self.cam = EmbeddedCAM(
            in_channels=out_channels,
            cam_channels=cam_channels
        )

    def __call__(self, x: mx.array) -> mx.array:
        """
        Forward pass

        Args:
            x: Input (batch, length, in_channels)

        Returns:
            CAM output (batch, length, cam_channels//4)
        """
        # Main conv + bn + relu
        out = self.conv(x)
        out = self.bn(out)
        out = self.activation(out)

        # CAM feature extraction
        out = self.cam(out)

        return out


class TransitionLayer(nn.Module):
    """
    Transition layer between dense blocks

    Reduces the accumulated channels back to base channel count.
    Architecture: BatchNorm → ReLU → 1x1 Conv
    """

    def __init__(self, in_channels: int, out_channels: int):
        super().__init__()

        self.bn = nn.BatchNorm(in_channels, affine=True)
        self.activation = nn.ReLU()
        self.conv = nn.Conv1d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=1,
            bias=False
        )

    def __call__(self, x: mx.array) -> mx.array:
        out = self.bn(x)
        out = self.activation(out)
        out = self.conv(out)
        return out


class CAMPPModelScopeV2(nn.Module):
    """
    Clean CAM++ implementation matching ModelScope architecture

    Key features:
    - Dense connections: each layer's output is concatenated
    - TDNN layers use kernel_size=1
    - CAM provides feature extraction (outputs cam_channels//4 per layer)
    - Transitions reduce accumulated channels back to base

    Args:
        input_dim: Input feature dimension (e.g., 80 or 320)
        channels: Base channel count (e.g., 128 or 512)
        block_layers: Layers per block (e.g., [12, 24, 16])
        embedding_dim: Output embedding dimension (e.g., 192)
        cam_channels: CAM channel count (e.g., 128)
        input_kernel_size: Input layer kernel size (e.g., 5)
    """

    def __init__(
        self,
        input_dim: int = 80,
        channels: int = 512,
        block_layers: List[int] = None,
        embedding_dim: int = 192,
        cam_channels: int = 128,
        input_kernel_size: int = 5
    ):
        super().__init__()

        if block_layers is None:
            block_layers = [4, 9, 16]

        self.input_dim = input_dim
        self.channels = channels
        self.block_layers = block_layers
        self.embedding_dim = embedding_dim
        self.cam_channels = cam_channels
        self.growth_rate = cam_channels // 4  # Each layer adds this many channels

        # Input layer
        self.input_conv = nn.Conv1d(
            in_channels=input_dim,
            out_channels=channels,
            kernel_size=input_kernel_size,
            padding=input_kernel_size // 2,
            bias=False
        )
        self.input_bn = nn.BatchNorm(channels, affine=True)
        self.input_activation = nn.ReLU()

        # Dense Block 0
        for i in range(block_layers[0]):
            in_ch = channels + i * self.growth_rate
            layer = TDNNLayerWithCAM(
                in_channels=in_ch,
                out_channels=channels,
                cam_channels=cam_channels
            )
            setattr(self, f'block0_{i}', layer)
        self._block0_size = block_layers[0]

        # Transition 1 - doubles channel count
        transit1_in = channels + block_layers[0] * self.growth_rate
        transit1_out = channels * 2
        self.transit1 = TransitionLayer(transit1_in, transit1_out)

        # Dense Block 1 - starts with doubled channels
        for i in range(block_layers[1]):
            in_ch = transit1_out + i * self.growth_rate
            layer = TDNNLayerWithCAM(
                in_channels=in_ch,
                out_channels=channels,
                cam_channels=cam_channels
            )
            setattr(self, f'block1_{i}', layer)
        self._block1_size = block_layers[1]

        # Transition 2 - doubles channel count again
        transit2_in = transit1_out + block_layers[1] * self.growth_rate
        transit2_out = transit1_out * 2  # 4x original channels
        self.transit2 = TransitionLayer(transit2_in, transit2_out)

        # Dense Block 2 - starts with quadrupled channels
        for i in range(block_layers[2]):
            in_ch = transit2_out + i * self.growth_rate
            layer = TDNNLayerWithCAM(
                in_channels=in_ch,
                out_channels=channels,
                cam_channels=cam_channels
            )
            setattr(self, f'block2_{i}', layer)
        self._block2_size = block_layers[2]

        # Final dense layer
        dense_in = transit2_out + block_layers[2] * self.growth_rate
        self.dense = nn.Conv1d(
            in_channels=dense_in,
            out_channels=embedding_dim,
            kernel_size=1,
            bias=False
        )

    def __call__(self, x: mx.array) -> mx.array:
        """
        Forward pass

        Args:
            x: Input (batch, length, in_channels) - channels_last format

        Returns:
            Embeddings (batch, length, embedding_dim)
        """
        # Handle input format
        if x.ndim == 2:
            x = mx.expand_dims(x, axis=0)

        # MLX Conv1d expects (batch, length, in_channels)
        if x.shape[2] != self.input_dim:
            x = mx.transpose(x, (0, 2, 1))

        # Input layer
        out = self.input_conv(x)
        out = self.input_bn(out)
        out = self.input_activation(out)

        # Dense Block 0 (with concatenation)
        for i in range(self._block0_size):
            layer = getattr(self, f'block0_{i}')
            layer_out = layer(out)
            out = mx.concatenate([out, layer_out], axis=2)

        # Transition 1
        out = self.transit1(out)

        # Dense Block 1
        for i in range(self._block1_size):
            layer = getattr(self, f'block1_{i}')
            layer_out = layer(out)
            out = mx.concatenate([out, layer_out], axis=2)

        # Transition 2
        out = self.transit2(out)

        # Dense Block 2
        for i in range(self._block2_size):
            layer = getattr(self, f'block2_{i}')
            layer_out = layer(out)
            out = mx.concatenate([out, layer_out], axis=2)

        # Final dense layer
        embeddings = self.dense(out)

        return embeddings

    def extract_embedding(self, x: mx.array, pooling: str = "mean") -> mx.array:
        """
        Extract fixed-size speaker embedding

        Args:
            x: Input (batch, length, in_channels)
            pooling: "mean", "max", or "both"

        Returns:
            Embedding (batch, embedding_dim)
        """
        frame_embeddings = self(x)  # (batch, length, embedding_dim)

        if pooling == "mean":
            embedding = mx.mean(frame_embeddings, axis=1)
        elif pooling == "max":
            embedding = mx.max(frame_embeddings, axis=1)
        elif pooling == "both":
            mean_pool = mx.mean(frame_embeddings, axis=1)
            max_pool = mx.max(frame_embeddings, axis=1)
            embedding = mx.concatenate([mean_pool, max_pool], axis=1)
        else:
            raise ValueError(f"Unknown pooling: {pooling}")

        return embedding

    def load_weights(self, file_or_weights, strict: bool = True):
        """
        Override load_weights to handle quantized weights with dequantization

        Args:
            file_or_weights: Path to .npz file or list of (name, array) tuples
            strict: If True, all parameters must match exactly
        """
        # Load weights from file if needed
        if isinstance(file_or_weights, str):
            loaded_weights = mx.load(file_or_weights)
        else:
            loaded_weights = dict(file_or_weights)

        # Dequantize weights that have scales and biases
        dequantized_weights = {}
        quantized_names = set()

        for name, array in loaded_weights.items():
            # Check if this is a quantized weight by looking for scales/biases with metadata
            # Format: name:qSCALES_GS64_B4 or name:qBIASES_GS64_B4
            if ':qSCALES_GS' in name or ':qBIASES_GS' in name:
                # Skip, will be processed when we see the main weight
                continue

            # Check if this weight has quantization metadata
            has_quantization = any(k.startswith(f"{name}:qSCALES_GS") for k in loaded_weights.keys())

            if has_quantization:
                # Find the scales key to extract group_size and bits
                scales_key = next(k for k in loaded_weights.keys() if k.startswith(f"{name}:qSCALES_GS"))
                # Parse: name:qSCALES_GS64_B4 -> extract GS64 and B4
                import re
                match = re.search(r'GS(\d+)_B(\d+)', scales_key)
                if match:
                    group_size = int(match.group(1))
                    bits = int(match.group(2))

                    # Get scales and biases
                    biases_key = f"{name}:qBIASES_GS{group_size}_B{bits}"
                    scales = loaded_weights[scales_key]
                    biases = loaded_weights[biases_key]

                    # Dequantize the weight
                    dequantized = mx.dequantize(array, scales, biases, group_size=group_size, bits=bits)
                    dequantized_weights[name] = dequantized
                    quantized_names.add(name)
                else:
                    # Fallback: couldn't parse, keep original
                    dequantized_weights[name] = array
            else:
                # Regular weight (not quantized)
                dequantized_weights[name] = array

        # Use the parent class load_weights with dequantized weights
        super().load_weights(list(dequantized_weights.items()), strict=strict)


def load_model(weights_path: str, config_path: Optional[str] = None) -> CAMPPModelScopeV2:
    """Load model from weights and config"""
    if config_path:
        with open(config_path, 'r') as f:
            config = json.load(f)
    else:
        config = {
            'input_dim': 80,
            'channels': 512,
            'block_layers': [4, 9, 16],
            'embedding_dim': 192,
            'cam_channels': 128,
            'input_kernel_size': 5
        }

    model = CAMPPModelScopeV2(**config)
    weights = mx.load(weights_path)
    model.load_weights(list(weights.items()))

    return model