feat(models/denoiser): Add Mamba2 model implementation

Implement a minimal, single-file implementation of the Mamba-2 model in PyTorch.
Based on the paper "Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality".

.gitignore

@@ -0,0 +1,4 @@
+__pycache__/
+*.py[cod]
+.venv/
+.idea/

src/callbacks/save_images.py

@@ -1,5 +1,6 @@
 import lightning.pytorch as pl
 from lightning.pytorch import Callback
+from lightning.pytorch.loggers import WandbLogger
 
 
 import os.path
@@ -10,15 +11,20 @@ from concurrent.futures import ThreadPoolExecutor
 
 from lightning.pytorch.utilities.types import STEP_OUTPUT
 from lightning_utilities.core.rank_zero import rank_zero_info
+try:
+    import wandb
+except ImportError:  # pragma: no cover - optional dependency
+    wandb = None
 
 def process_fn(image, path):
     Image.fromarray(image).save(path)
 
 class SaveImagesHook(Callback):
-    def __init__(self, save_dir="val", max_save_num=100, compressed=True):
+    def __init__(self, save_dir="val", max_save_num=100, compressed=True, wandb_max_save_num=None):
        self.save_dir = save_dir
        self.max_save_num = max_save_num
        self.compressed = compressed
+       self.wandb_max_save_num = wandb_max_save_num
 
     def save_start(self, target_dir):
         self.target_dir = target_dir
@@ -68,6 +74,50 @@ class SaveImagesHook(Callback):
         self._have_saved_num = 0
         self.executor_pool = None
 
+    def _get_wandb_logger(self, trainer: "pl.Trainer"):
+        if isinstance(trainer.logger, WandbLogger):
+            return trainer.logger
+        if getattr(trainer, "loggers", None):
+            for logger in trainer.loggers:
+                if isinstance(logger, WandbLogger):
+                    return logger
+        return None
+
+    def _log_wandb_samples(self, trainer: "pl.Trainer"):
+        if not trainer.is_global_zero:
+            return
+        if wandb is None:
+            return
+        if not self.samples:
+            return
+        if self.max_save_num == 0:
+            return
+        wandb_logger = self._get_wandb_logger(trainer)
+        if wandb_logger is None:
+            return
+
+        max_num = self.wandb_max_save_num if self.wandb_max_save_num is not None else self.max_save_num
+        if max_num <= 0:
+            return
+
+        images = []
+        remaining = max_num
+        for batch in self.samples:
+            if remaining <= 0:
+                break
+            take = min(batch.shape[0], remaining)
+            chunk = batch[:take]
+            if chunk.dtype != numpy.uint8:
+                chunk = numpy.clip(chunk, 0, 255).astype(numpy.uint8)
+            images.extend([wandb.Image(img) for img in chunk])
+            remaining -= take
+
+        if images:
+            wandb_logger.experiment.log(
+                {f"{self.save_dir}/samples": images},
+                step=trainer.global_step,
+            )
+
     def on_validation_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         target_dir = os.path.join(trainer.default_root_dir, self.save_dir, f"iter_{trainer.global_step}")
         self.save_start(target_dir)
@@ -84,6 +134,7 @@ class SaveImagesHook(Callback):
         return self.process_batch(trainer, pl_module, outputs, batch)
 
     def on_validation_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+        self._log_wandb_samples(trainer)
         self.save_end()
 
     def on_predict_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:

src/data/dataset/hf_image.py

@@ -0,0 +1,43 @@
+from typing import Optional
+
+import torch
+from torch.utils.data import Dataset
+from torchvision.transforms import Normalize
+from torchvision.transforms.functional import to_tensor
+from PIL import Image
+from datasets import load_dataset
+
+from src.data.dataset.metric_dataset import CenterCrop
+
+
+class HuggingFaceImageDataset(Dataset):
+    def __init__(
+        self,
+        name: str,
+        split: str = "train",
+        image_column: str = "image",
+        label_column: Optional[str] = None,
+        resolution: int = 256,
+        cache_dir: Optional[str] = None,
+    ) -> None:
+        self.dataset = load_dataset(name, split=split, cache_dir=cache_dir)
+        self.image_column = image_column
+        self.label_column = label_column
+        self.transform = CenterCrop(resolution)
+        self.normalize = Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    def __len__(self) -> int:
+        return len(self.dataset)
+
+    def __getitem__(self, idx: int):
+        item = self.dataset[idx]
+        image = item[self.image_column]
+        if not isinstance(image, Image.Image):
+            image = Image.fromarray(image)
+        image = image.convert("RGB")
+        image = self.transform(image)
+
+        raw_image = to_tensor(image)
+        normalized_image = self.normalize(raw_image)
+        label = 0 if self.label_column is None else int(item[self.label_column])
+        return raw_image, normalized_image, label

src/models/denoiser/mamba2.py

@@ -0,0 +1,463 @@
+"""
+mamba2-minimal
+==============
+
+A minimal, single-file implementation of the Mamba-2 model in PyTorch.
+
+> **Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality**
+> Authors: Tri Dao, Albert Gu
+> Paper: https://arxiv.org/abs/2405.21060
+"""
+
+import json
+import math
+from dataclasses import dataclass
+from typing import Iterable, NamedTuple, TypeAlias, cast
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from torch import LongTensor, Tensor, nn
+
+Device: TypeAlias = str | torch.device | None
+
+
+@dataclass
+class Mamba2Config:
+    d_model: int  # model dimension (D)
+    n_layer: int = 24  # number of Mamba-2 layers in the language model
+    d_state: int = 128  # state dimension (N)
+    d_conv: int = 4  # convolution kernel size
+    expand: int = 2  # expansion factor (E)
+    headdim: int = 64  # head dimension (P)
+    chunk_size: int = 64  # matrix partition size (Q)
+    vocab_size: int = 50277
+    pad_vocab_size_multiple: int = 16
+
+    def __post_init__(self):
+        self.d_inner = self.expand * self.d_model
+        assert self.d_inner % self.headdim == 0
+        self.nheads = self.d_inner // self.headdim
+        if self.vocab_size % self.pad_vocab_size_multiple != 0:
+            self.vocab_size += (
+                self.pad_vocab_size_multiple
+                - self.vocab_size % self.pad_vocab_size_multiple
+            )
+
+
+class InferenceCache(NamedTuple):
+    conv_state: Tensor  # (batch, d_inner + 2 * d_state, d_conv)
+    ssm_state: Tensor  # (batch, nheads, headdim, d_state)
+
+    @staticmethod
+    def alloc(batch_size: int, args: Mamba2Config, device: Device = None):
+        return InferenceCache(
+            torch.zeros(
+                batch_size, args.d_inner + 2 * args.d_state, args.d_conv, device=device
+            ),
+            torch.zeros(
+                batch_size, args.nheads, args.headdim, args.d_state, device=device
+            ),
+        )
+
+
+class Mamba2LMHeadModel(nn.Module):
+    def __init__(self, args: Mamba2Config, device: Device = None):
+        super().__init__()
+        self.args = args
+        self.device = device
+
+        self.backbone = nn.ModuleDict(
+            dict(
+                embedding=nn.Embedding(args.vocab_size, args.d_model, device=device),
+                layers=nn.ModuleList(
+                    [
+                        nn.ModuleDict(
+                            dict(
+                                mixer=Mamba2(args, device=device),
+                                norm=RMSNorm(args.d_model, device=device),
+                            )
+                        )
+                        for _ in range(args.n_layer)
+                    ]
+                ),
+                norm_f=RMSNorm(args.d_model, device=device),
+            )
+        )
+        self.lm_head = nn.Linear(
+            args.d_model, args.vocab_size, bias=False, device=device
+        )
+        self.lm_head.weight = self.backbone.embedding.weight
+
+    @staticmethod
+    def from_pretrained(huggingface_model_id: str, device: Device = None):
+        from transformers.utils import CONFIG_NAME, WEIGHTS_NAME
+        from transformers.utils.hub import cached_file
+
+        config_path = cached_file(huggingface_model_id, CONFIG_NAME)
+        assert config_path, "Failed to get huggingface config file"
+        state_dict_path = cached_file(huggingface_model_id, WEIGHTS_NAME)
+        assert state_dict_path, "Failed to get huggingface state dict file"
+
+        config = json.load(open(config_path))
+        args = Mamba2Config(
+            d_model=config["d_model"],
+            n_layer=config["n_layer"],
+            vocab_size=config["vocab_size"],
+            pad_vocab_size_multiple=config["pad_vocab_size_multiple"],
+        )
+
+        map_location = "cpu" if device is None else device
+        state_dict = torch.load(
+            state_dict_path, weights_only=True, map_location=map_location, mmap=True
+        )
+        model = Mamba2LMHeadModel(args, device=device)
+        model.load_state_dict(state_dict)
+        model.eval()
+        return model
+
+    def forward(
+        self, input_ids: LongTensor, h: list[InferenceCache] | list[None] | None = None
+    ) -> tuple[LongTensor, list[InferenceCache]]:
+        """
+        Arguments
+            input_ids: (batch, seqlen) tokens from `EleutherAI/gpt-neox-20b` tokenizer
+            h: hidden states for inference step. If present the constant-time
+               (wrt sequence length) inference path will be taken, input_ids
+               should have shape (batch, 1) containing the next batch of prompt
+               token.
+
+        Return (logits, h)
+            logits: (batch, seqlen, vocab_size)
+            h: updated inference cache after processing `input_ids`
+        """
+        seqlen = input_ids.shape[1]
+
+        if h is None:
+            h = [None for _ in range(self.args.n_layer)]
+
+        x = self.backbone.embedding(input_ids)
+        for i, layer in enumerate(self.backbone.layers):
+            y, h[i] = layer.mixer(layer.norm(x), h[i])
+            x = y + x
+
+        x = self.backbone.norm_f(x)
+        logits = self.lm_head(x)
+        return logits[:, :seqlen], cast(list[InferenceCache], h)
+
+    def generate(
+        self,
+        input_ids: LongTensor,
+        max_new_length: int = 20,
+        temperature: float = 1.0,
+        top_k: int = 50,
+        top_p: float = 1.0,
+        eos_token_id: int = 0,
+    ) -> Iterable[tuple[int, list[InferenceCache]]]:
+        prefix, tokens = input_ids[:-1], input_ids[-1:].unsqueeze(0)
+        cache_device = input_ids.device
+
+        # Process prompt
+        # The input sequence to forward (non-inference path) must have length multiple that of chunk_size.
+        # We split out excess tokens so that n_chunked tokens can be processed by one forward call and
+        # process the rest in multiple inference steps.
+        n_chunked = (prefix.shape[0] // self.args.chunk_size) * self.args.chunk_size
+        if n_chunked > 0:
+            _, h = self(prefix[:n_chunked].unsqueeze(0), None)
+        else:
+            h = [
+                InferenceCache.alloc(1, self.args, device=cache_device)
+                for _ in range(self.args.n_layer)
+            ]
+        for i in range(n_chunked, prefix.shape[0]):
+            _, h = self(prefix[i : i + 1].unsqueeze(0), h)
+
+        # Generate
+        for _ in range(max_new_length):
+            with torch.no_grad():
+                out, h = self(tokens, h)
+            logits = out[0, -1]
+            if temperature != 1.0:
+                logits = logits / temperature
+            if top_k > 0:
+                indices_to_remove = logits < torch.topk(logits, k=top_k)[0][-1]
+                logits[indices_to_remove] = -torch.inf
+            if top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                cum_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+                sorted_indices_to_remove = cum_probs > 0.5
+                sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].clone()
+                sorted_indices_to_remove[0] = False
+                indices_to_remove = sorted_indices[sorted_indices_to_remove]
+                logits[indices_to_remove] = -torch.inf
+            probs = F.softmax(logits, dim=-1)
+            next_token = torch.multinomial(probs, num_samples=1)
+            if next_token.item() == eos_token_id:
+                return
+            tokens = next_token.unsqueeze(0)
+            yield cast(int, next_token.item()), h
+
+
+class Mamba2(nn.Module):
+    def __init__(self, args: Mamba2Config, device: Device = None):
+        super().__init__()
+        self.args = args
+        self.device = device
+
+        # Order: (z, x, B, C, dt)
+        d_in_proj = 2 * args.d_inner + 2 * args.d_state + args.nheads
+        self.in_proj = nn.Linear(args.d_model, d_in_proj, bias=False, device=device)
+
+        conv_dim = args.d_inner + 2 * args.d_state
+        self.conv1d = nn.Conv1d(
+            in_channels=conv_dim,
+            out_channels=conv_dim,
+            kernel_size=args.d_conv,
+            groups=conv_dim,
+            padding=args.d_conv - 1,
+            device=device,
+        )
+
+        self.dt_bias = nn.Parameter(torch.empty(args.nheads, device=device))
+        self.A_log = nn.Parameter(torch.empty(args.nheads, device=device))
+        self.D = nn.Parameter(torch.empty(args.nheads, device=device))
+        self.norm = RMSNorm(args.d_inner, device=device)
+        self.out_proj = nn.Linear(args.d_inner, args.d_model, bias=False, device=device)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        # Mamba-style parameter init for stable SSM dynamics.
+        dt_min, dt_max = 1e-3, 1e-1
+        device = self.dt_bias.device
+        dtype = self.dt_bias.dtype
+        dt = torch.exp(
+            torch.rand(self.args.nheads, device=device, dtype=dtype)
+            * (math.log(dt_max) - math.log(dt_min))
+            + math.log(dt_min)
+        )
+        with torch.no_grad():
+            self.dt_bias.copy_(torch.log(torch.expm1(dt)))
+            self.A_log.copy_(
+                torch.log(
+                    torch.arange(
+                        1, self.args.nheads + 1, device=device, dtype=self.A_log.dtype
+                    )
+                )
+            )
+            self.D.fill_(1.0)
+
+    def forward(self, u: Tensor, h: InferenceCache | None = None):
+        """
+        Arguments
+            u: (batch, seqlen, d_model) input. seqlen should be a multiple of chunk_size.
+            h: hidden states for inference step. Initialized to 0s if not present.
+
+        Return (y, h)
+            y: (batch, seqlen, d_model) output
+            h: updated inference cache after processing `u`
+        """
+        if h:
+            return self.step(u, h)
+
+        A = -torch.exp(self.A_log)  # (nheads,)
+        zxbcdt = self.in_proj(u)  # (batch, seqlen, d_in_proj)
+        z, xBC, dt = torch.split(
+            zxbcdt,
+            [
+                self.args.d_inner,
+                self.args.d_inner + 2 * self.args.d_state,
+                self.args.nheads,
+            ],
+            dim=-1,
+        )
+        dt = F.softplus(dt + self.dt_bias)  # (batch, seqlen, nheads)
+
+        # Pad or truncate xBC seqlen to d_conv
+        conv_state = F.pad(
+            rearrange(xBC, "b l d -> b d l"), (self.args.d_conv - u.shape[1], 0)
+        )
+
+        xBC = silu(
+            self.conv1d(xBC.transpose(1, 2)).transpose(1, 2)[:, : u.shape[1], :]
+        )  # (batch, seqlen, d_inner + 2 * d_state))
+        x, B, C = torch.split(
+            xBC, [self.args.d_inner, self.args.d_state, self.args.d_state], dim=-1
+        )
+        x = rearrange(x, "b l (h p) -> b l h p", p=self.args.headdim)
+        y, ssm_state = ssd(
+            x * dt.unsqueeze(-1),
+            A * dt,
+            rearrange(B, "b l n -> b l 1 n"),
+            rearrange(C, "b l n -> b l 1 n"),
+            self.args.chunk_size,
+            device=x.device,
+        )
+        y = y + x * self.D.unsqueeze(-1)
+        y = rearrange(y, "b l h p -> b l (h p)")
+        y = self.norm(y, z)
+        y = self.out_proj(y)
+
+        h = InferenceCache(conv_state, ssm_state)
+        return y, h
+
+    def step(self, u: Tensor, h: InferenceCache) -> tuple[Tensor, InferenceCache]:
+        """Take a single inference step for the current input and hidden state
+
+        Unlike attention-based models, RNN-based models (eg Mamba) does not need
+        to look back at all the past tokens to generate a new token. Instead a
+        hidden state (initialized to 0s initially) is updated for each input and
+        passed to the next inference step. This means that the total inference
+        time is linear with respect to the sequence length instead of quadratic
+        in attention's case.
+
+        Arguments
+            u: (batch, 1, d_model)
+            h: initial/running hidden state
+
+        Return (y, h)
+            y: (batch, 1, d_model)
+            h: updated hidden state
+        """
+        assert u.shape[1] == 1, "Only one token can be decoded per inference step"
+
+        zxbcdt = self.in_proj(u.squeeze(1))  # (batch, d_in_proj)
+        z, xBC, dt = torch.split(
+            zxbcdt,
+            [
+                self.args.d_inner,
+                self.args.d_inner + 2 * self.args.d_state,
+                self.args.nheads,
+            ],
+            dim=-1,
+        )
+
+        # Advance convolution input
+        h.conv_state.copy_(torch.roll(h.conv_state, shifts=-1, dims=-1))
+        h.conv_state[:, :, -1] = xBC
+        # Convolution step
+        xBC = torch.sum(
+            h.conv_state * rearrange(self.conv1d.weight, "d 1 w -> d w"), dim=-1
+        )
+        xBC += self.conv1d.bias
+        xBC = silu(xBC)
+
+        x, B, C = torch.split(
+            xBC, [self.args.d_inner, self.args.d_state, self.args.d_state], dim=-1
+        )
+        A = -torch.exp(self.A_log)  # (nheads,)
+
+        # SSM step
+        dt = F.softplus(dt + self.dt_bias)  # (batch, nheads)
+        dA = torch.exp(dt * A)  # (batch, nheads)
+        x = rearrange(x, "b (h p) -> b h p", p=self.args.headdim)
+        dBx = torch.einsum("bh, bn, bhp -> bhpn", dt, B, x)
+        h.ssm_state.copy_(h.ssm_state * rearrange(dA, "b h -> b h 1 1") + dBx)
+        y = torch.einsum("bhpn, bn -> bhp", h.ssm_state, C)
+        y = y + rearrange(self.D, "h -> h 1") * x
+        y = rearrange(y, "b h p -> b (h p)")
+        y = self.norm(y, z)
+        y = self.out_proj(y)
+
+        return y.unsqueeze(1), h
+
+
+def segsum(x: Tensor, device: Device = None) -> Tensor:
+    """Stable segment sum calculation.
+
+    `exp(segsum(A))` produces a 1-semiseparable matrix, which is equivalent to a scalar SSM.
+
+    Source: https://github.com/state-spaces/mamba/blob/219f03c840d5a44e7d42e4e728134834fddccf45/mamba_ssm/modules/ssd_minimal.py#L23-L32
+    """
+    if device is None:
+        device = x.device
+    T = x.size(-1)
+    x = repeat(x, "... d -> ... d e", e=T)
+    mask = torch.tril(torch.ones(T, T, dtype=torch.bool, device=device), diagonal=-1)
+    x = x.masked_fill(~mask, 0)
+    x_segsum = torch.cumsum(x, dim=-2)
+    mask = torch.tril(torch.ones(T, T, dtype=torch.bool, device=device), diagonal=0)
+    x_segsum = x_segsum.masked_fill(~mask, -torch.inf)
+    return x_segsum
+
+
+def ssd(x, A, B, C, chunk_size, initial_states=None, device: Device = None):
+    """Structed State Space Duality (SSD) - the core of Mamba-2
+
+    This is almost the exact same minimal SSD code from the blog post.
+
+    Arguments
+        x: (batch, seqlen, n_heads, d_head)
+        A: (batch, seqlen, n_heads)
+        B: (batch, seqlen, n_heads, d_state)
+        C: (batch, seqlen, n_heads, d_state)
+
+    Return
+        y: (batch, seqlen, n_heads, d_head)
+
+    Source
+     1. https://tridao.me/blog/2024/mamba2-part3-algorithm/
+     2. https://github.com/state-spaces/mamba/blob/219f03c840d5a44e7d42e4e728134834fddccf45/mamba_ssm/modules/ssd_minimal.py#L34-L78
+    """
+    assert x.shape[1] % chunk_size == 0
+
+    # Rearrange into chunks
+    # Step 1, 2 and 4 of SSD can be computed in parallel for each chunk across devices (sequence parallel)
+    # This is not implemented and left as an exercise for the reader 😜
+    x, A, B, C = [
+        rearrange(m, "b (c l) ... -> b c l ...", l=chunk_size) for m in (x, A, B, C)
+    ]
+
+    A = rearrange(A, "b c l h -> b h c l")
+    A_cumsum = torch.cumsum(A, dim=-1)
+
+    # 1. Compute the output for each intra-chunk (diagonal blocks)
+    L = torch.exp(segsum(A, device=device))
+    Y_diag = torch.einsum("bclhn, bcshn, bhcls, bcshp -> bclhp", C, B, L, x)
+
+    # 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)
+    states = torch.einsum("bclhn, bhcl, bclhp -> bchpn", B, decay_states, x)
+
+    # 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 initial_states is None:
+        initial_states = torch.zeros_like(states[:, :1])
+    states = torch.cat([initial_states, states], dim=1)
+    decay_chunk = torch.exp(segsum(F.pad(A_cumsum[:, :, :, -1], (1, 0)), device=device))
+    new_states = torch.einsum("bhzc, bchpn -> bzhpn", decay_chunk, states)
+    states, final_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)
+    Y_off = torch.einsum("bclhn, bchpn, bhcl -> bclhp", C, states, state_decay_out)
+
+    # Add output of intra-chunk and inter-chunk terms (diagonal and off-diagonal blocks)
+    Y = rearrange(Y_diag + Y_off, "b c l h p -> b (c l) h p")
+
+    return Y, final_state
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, d: int, eps: float = 1e-5, device: Device = None):
+        """Gated Root Mean Square Layer Normalization
+
+        Paper: https://arxiv.org/abs/1910.07467
+        """
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(d, device=device))
+
+    def forward(self, x, z=None):
+        if z is not None:
+            x = x * silu(z)
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight
+
+
+def silu(x):
+    """Applies the Sigmoid Linear Unit (SiLU), element-wise.
+
+    Define this manually since torch's version doesn't seem to work on MPS.
+    """
+    return x * F.sigmoid(x)