12 files changed, 367 insertions, 0 deletions
diff --git a/text_recognizer/network/transformer/__init__.py b/text_recognizer/network/transformer/__init__.py
new file mode 100644
index 0000000..a3f3011
--- /dev/null
+++ b/text_recognizer/network/transformer/__init__.py
@@ -0,0 +1 @@
+"""Transformer modules."""
diff --git a/text_recognizer/network/transformer/attend.py b/text_recognizer/network/transformer/attend.py
new file mode 100644
index 0000000..4e643fb
--- /dev/null
+++ b/text_recognizer/network/transformer/attend.py
@@ -0,0 +1,94 @@
+from typing import Optional
+from collections import namedtuple
+
+import torch
+from torch import Tensor, einsum, nn
+from einops import rearrange
+import torch.nn.functional as F
+
+Config = namedtuple(
+    "FlashAttentionConfig", ["enable_flash", "enable_math", "enable_mem_efficient"]
+)
+
+
+class Attend(nn.Module):
+    def __init__(self, use_flash: bool) -> None:
+        super().__init__()
+        self.use_flash = use_flash
+        self.cpu_cfg = Config(True, True, True)
+        self.cuda_cfg = None
+        if not torch.cuda.is_available():
+            return
+
+        device_properties = torch.cuda.get_device_properties(torch.device("cuda"))
+        if device_properties.major == 8 and device_properties.minor == 0:
+            self.cuda_cfg = Config(True, False, False)
+        else:
+            self.cuda_cfg = Config(False, True, True)
+
+    def flash_attn(self, q: Tensor, k: Tensor, v: Tensor, causal: bool) -> Tensor:
+        cfg = self.cuda_cfg if q.is_cuda else self.cpu_cfg
+        with torch.backends.cuda.sdp_kernel(**cfg._asdict()):
+            out = F.scaled_dot_product_attention(q, k, v, is_causal=causal)
+        return out
+
+    def atten(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        causal: bool,
+        mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        b = q.shape[0]
+        energy = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale
+
+        mask_value = -torch.finfo(energy.dtype).max
+
+        if mask is not None:
+            energy = apply_input_mask(b, k, energy, mask, mask_value)
+
+        if causal:
+            energy = apply_causal_mask(energy, mask_value)
+
+        attn = F.softmax(energy, dim=-1)
+        attn = self.dropout(attn)
+        return einsum("b h i j, b h j d -> b h i d", attn, v)
+
+    def forward(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        causal: bool,
+        mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        if self.use_flash:
+            return self.flash_attn(q, k, v, causal)
+        else:
+            return self.atten(q, k, v, causal, mask)
+
+
+def apply_input_mask(
+    b: int,
+    k: Tensor,
+    energy: Tensor,
+    mask: Optional[Tensor],
+    mask_value: float,
+) -> Tensor:
+    """Applies an input mask."""
+    k_mask = torch.ones((b, k.shape[-2]), device=energy.device).bool()
+    q_mask = rearrange(mask, "b i -> b () i ()")
+    k_mask = rearrange(k_mask, "b j -> b () () j")
+    input_mask = q_mask * k_mask
+    return energy.masked_fill_(~input_mask, mask_value)
+
+
+def apply_causal_mask(
+    energy: Tensor,
+    mask_value: float,
+) -> Tensor:
+    """Applies a causal mask to the energy tensor."""
+    i, j, device = *energy.shape[-2:], energy.device
+    causal_mask = torch.ones((i, j), device=device, dtype=torch.bool).triu(j - i + 1)
+    return energy.masked_fill(causal_mask, mask_value)
diff --git a/text_recognizer/network/transformer/attention.py b/text_recognizer/network/transformer/attention.py
new file mode 100644
index 0000000..8e18f8a
--- /dev/null
+++ b/text_recognizer/network/transformer/attention.py
@@ -0,0 +1,56 @@
+"""Implements the attention module for the transformer."""
+from typing import Optional
+from text_recognizer.network.transformer.norm import RMSNorm
+from text_recognizer.network.transformer.attend import Attend
+
+import torch
+from einops import rearrange
+from torch import Tensor, nn
+
+
+class Attention(nn.Module):
+    """Standard attention."""
+
+    def __init__(
+        self,
+        dim: int,
+        heads: int,
+        causal: bool = False,
+        dim_head: int = 64,
+        dropout_rate: float = 0.0,
+        use_flash: bool = True,
+    ) -> None:
+        super().__init__()
+        self.heads = heads
+        inner_dim = dim_head * heads
+        self.norm = nn.LayerNorm(dim)
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(dim, inner_dim, bias=False)
+        # self.q_norm = RMSNorm(heads, dim_head)
+        # self.k_norm = RMSNorm(heads, dim_head)
+        self.attend = Attend(use_flash)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+        self.scale = dim**-0.5
+        self.causal = causal
+        self.dropout_rate = dropout_rate
+        self.dropout = nn.Dropout(p=self.dropout_rate)
+
+    def forward(
+        self,
+        x: Tensor,
+        context: Optional[Tensor] = None,
+        mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        """Computes the attention."""
+        x = self.norm(x)
+        q = self.to_q(x)
+        k = self.to_k(x if context is None else context)
+        v = self.to_v(x if context is None else context)
+        q, k, v = map(
+            lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads), (q, k, v)
+        )
+        out = self.attend(q, k, v, self.causal, mask)
+        out = rearrange(out, "b h n d -> b n (h d)")
+        out = self.to_out(out)
+        return out
diff --git a/text_recognizer/network/transformer/decoder.py b/text_recognizer/network/transformer/decoder.py
new file mode 100644
index 0000000..06925ba
--- /dev/null
+++ b/text_recognizer/network/transformer/decoder.py
@@ -0,0 +1,57 @@
+"""Transformer decoder module."""
+from typing import Optional
+from torch import Tensor, nn
+
+from text_recognizer.network.transformer.attention import Attention
+from text_recognizer.network.transformer.ff import FeedForward
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        inner_dim: int,
+        heads: int,
+        dim_head: int,
+        depth: int,
+        dropout_rate: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(dim)
+        self.layers = nn.ModuleList(
+            [
+                nn.ModuleList(
+                    [
+                        Attention(
+                            dim,
+                            heads,
+                            True,
+                            dim_head,
+                            dropout_rate,
+                        ),
+                        FeedForward(dim, inner_dim, dropout_rate),
+                        Attention(
+                            dim,
+                            heads,
+                            False,
+                            dim_head,
+                            dropout_rate,
+                        ),
+                    ]
+                )
+                for _ in range(depth)
+            ]
+        )
+
+    def forward(
+        self,
+        x: Tensor,
+        context: Tensor,
+        mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        """Applies decoder block on input signals."""
+        for self_attn, ff, cross_attn in self.layers:
+            x = x + self_attn(x, mask=mask)
+            x = x + ff(x)
+            x = x + cross_attn(x, context=context)
+        return self.norm(x)
diff --git a/text_recognizer/network/transformer/embedding/__init__.py b/text_recognizer/network/transformer/embedding/__init__.py
new file mode 100644
index 0000000..bb3f904
--- /dev/null
+++ b/text_recognizer/network/transformer/embedding/__init__.py
@@ -0,0 +1 @@
+"""Positional encodings for transformers."""
diff --git a/text_recognizer/network/transformer/embedding/absolute.py b/text_recognizer/network/transformer/embedding/absolute.py
new file mode 100644
index 0000000..08b2c2a
--- /dev/null
+++ b/text_recognizer/network/transformer/embedding/absolute.py
@@ -0,0 +1,28 @@
+from typing import Optional
+
+import torch
+from torch import nn, Tensor
+from text_recognizer.network.transformer.embedding.l2_norm import l2_norm
+
+
+class AbsolutePositionalEmbedding(nn.Module):
+    def __init__(self, dim: int, max_length: int, use_l2: bool = False) -> None:
+        super().__init__()
+        self.scale = dim**-0.5 if not use_l2 else 1.0
+        self.max_length = max_length
+        self.use_l2 = use_l2
+        self.to_embedding = nn.Embedding(max_length, dim)
+        if self.use_l2:
+            nn.init.normal_(self.to_embedding.weight, std=1e-5)
+
+    def forward(self, x: Tensor, pos: Optional[Tensor] = None) -> Tensor:
+        n, device = x.shape[1], x.device
+        assert (
+            n <= self.max_length
+        ), f"Sequence length {n} is greater than the maximum positional embedding {self.max_length}"
+
+        if pos is None:
+            pos = torch.arange(n, device=device)
+
+        embedding = self.to_embedding(pos) * self.scale
+        return l2_norm(embedding) if self.use_l2 else embedding
diff --git a/text_recognizer/network/transformer/embedding/l2_norm.py b/text_recognizer/network/transformer/embedding/l2_norm.py
new file mode 100644
index 0000000..0e48bca
--- /dev/null
+++ b/text_recognizer/network/transformer/embedding/l2_norm.py
@@ -0,0 +1,9 @@
+from einops import rearrange
+import torch.nn.functional as F
+from torch import Tensor
+
+
+def l2_norm(t: Tensor, groups=1) -> Tensor:
+    t = rearrange(t, "... (g d) -> ... g d", g=groups)
+    t = F.normalize(t, p=2, dim=-1)
+    return rearrange(t, "... g d -> ... (g d)")
diff --git a/text_recognizer/network/transformer/embedding/sincos.py b/text_recognizer/network/transformer/embedding/sincos.py
new file mode 100644
index 0000000..ed6b0ab
--- /dev/null
+++ b/text_recognizer/network/transformer/embedding/sincos.py
@@ -0,0 +1,13 @@
+import torch
+
+
+def sincos_2d(h, w, dim, temperature: int = 10000, dtype=torch.float32):
+    y, x = torch.meshgrid(torch.arange(h), torch.arange(w), indexing="ij")
+    assert (dim % 4) == 0, "feature dimension must be multiple of 4 for sincos emb"
+    omega = torch.arange(dim // 4) / (dim // 4 - 1)
+    omega = 1.0 / (temperature**omega)
+
+    y = y.flatten()[:, None] * omega[None, :]
+    x = x.flatten()[:, None] * omega[None, :]
+    pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim=1)
+    return pe.type(dtype)
diff --git a/text_recognizer/network/transformer/embedding/token.py b/text_recognizer/network/transformer/embedding/token.py
new file mode 100644
index 0000000..1df2fd6
--- /dev/null
+++ b/text_recognizer/network/transformer/embedding/token.py
@@ -0,0 +1,18 @@
+from torch import nn, Tensor
+
+from text_recognizer.network.transformer.embedding.l2_norm import l2_norm
+
+
+class TokenEmbedding(nn.Module):
+    def __init__(self, num_tokens: int, dim: int, use_l2: bool = True) -> None:
+        super().__init__()
+        self.use_l2 = use_l2
+        self.to_embedding = nn.Embedding(num_tokens, dim)
+        if self.use_l2:
+            nn.init.normal_(self.to_embedding.weight, std=1e-5)
+        else:
+            nn.init.kaiming_normal_(self.to_embedding.weight)
+
+    def forward(self, x: Tensor) -> Tensor:
+        embedding = self.to_embedding(x)
+        return l2_norm(embedding) if self.use_l2 else embedding
diff --git a/text_recognizer/network/transformer/encoder.py b/text_recognizer/network/transformer/encoder.py
new file mode 100644
index 0000000..ea4b0b3
--- /dev/null
+++ b/text_recognizer/network/transformer/encoder.py
@@ -0,0 +1,46 @@
+"""Transformer encoder module."""
+from torch import Tensor, nn
+
+from text_recognizer.network.transformer.attention import Attention
+from text_recognizer.network.transformer.ff import FeedForward
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        inner_dim: int,
+        heads: int,
+        dim_head: int,
+        depth: int,
+        dropout_rate: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(dim)
+        self.layers = nn.ModuleList(
+            [
+                nn.ModuleList(
+                    [
+                        Attention(
+                            dim,
+                            heads,
+                            False,
+                            dim_head,
+                            dropout_rate,
+                        ),
+                        FeedForward(dim, inner_dim, dropout_rate),
+                    ]
+                )
+                for _ in range(depth)
+            ]
+        )
+
+    def forward(
+        self,
+        x: Tensor,
+    ) -> Tensor:
+        """Applies decoder block on input signals."""
+        for self_attn, ff in self.layers:
+            x = x + self_attn(x)
+            x = x + ff(x)
+        return self.norm(x)
diff --git a/text_recognizer/network/transformer/ff.py b/text_recognizer/network/transformer/ff.py
new file mode 100644
index 0000000..9181323
--- /dev/null
+++ b/text_recognizer/network/transformer/ff.py
@@ -0,0 +1,22 @@
+"""Feedforward layer in transformer."""
+from torch import Tensor, nn
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        inner_dim: int,
+        dropout_rate: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.ff = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, inner_dim),
+            nn.GELU(),
+            nn.Dropout(dropout_rate),
+            nn.Linear(inner_dim, dim),
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.ff(x)
diff --git a/text_recognizer/network/transformer/norm.py b/text_recognizer/network/transformer/norm.py
new file mode 100644
index 0000000..2737754
--- /dev/null
+++ b/text_recognizer/network/transformer/norm.py
@@ -0,0 +1,22 @@
+"""Normalization layers for transformers.
+
+Copied from lucidrains:
+    https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
+
+"""
+import torch
+from torch import Tensor, nn
+import torch.nn.functional as F
+
+
+class RMSNorm(nn.Module):
+    """Root mean square layer normalization."""
+
+    def __init__(self, heads: int, dim: int) -> None:
+        super().__init__()
+        self.scale = dim**-0.5
+        self.gamma = nn.Parameter(torch.ones(heads, 1, dim))
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Applies normalization."""
+        return F.normalize(x, dim=-1) * self.scale * self.gamma