Merge pull request #2 from pytorch/main

Vector Quantized Embeddings (pytorch#2040)

tests/torchtune/modules/test_vq_embeddings.py

@@ -0,0 +1,114 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import pytest
+
+import torch
+from tests.test_utils import assert_expected
+from torch import tensor
+from torchtune.modules.vq_embeddings import VectorQuantizedEmbeddings
+
+
+@pytest.fixture(autouse=True)
+def random_seed():
+    torch.manual_seed(4)
+
+
+class TestVectorQuantizedEmbeddings:
+    @pytest.fixture
+    def num_embeddings(self):
+        return 4
+
+    @pytest.fixture
+    def embedding_dim(self):
+        return 5
+
+    @pytest.fixture
+    def embedding_weights(self):
+        # This is 4x5
+        return tensor(
+            [
+                [1.0, 0.0, -1.0, -1.0, 2.0],
+                [2.0, -2.0, 0.0, 0.0, 1.0],
+                [2.0, 1.0, 0.0, 1.0, 1.0],
+                [-1.0, -2.0, 0.0, 2.0, 0.0],
+            ]
+        )
+
+    @pytest.fixture
+    def codebook(self, num_embeddings, embedding_dim, embedding_weights):
+        vq = VectorQuantizedEmbeddings(
+            num_embeddings=num_embeddings,
+            embedding_dim=embedding_dim,
+        )
+        vq.embedding.data = embedding_weights
+        return vq
+
+    @pytest.fixture
+    def encoded(self):
+        # This is 2x3x5
+        encoded = tensor(
+            [
+                [
+                    [-1.0, 2.0, 0.0, 0.0, -2.0],
+                    [0.0, 1.0, -1.0, 2.0, -1.0],
+                    [1.0, 0.0, -1.0, -1.0, 1.0],
+                ],
+                [
+                    [2.0, 1.0, 0.0, 1.0, 1.0],
+                    [2.0, -1.0, 0.0, 2.0, 0.0],
+                    [-1.0, -2.0, 0.0, 1.0, 0.0],
+                ],
+            ]
+        )
+        encoded.requires_grad_()
+
+        return encoded
+
+    def test_quantized_output(self, codebook, encoded):
+        actual = codebook(encoded)
+
+        expected_quantized = tensor(
+            [
+                [
+                    [2.0, 1.0, 0.0, 1.0, 1.0],
+                    [2.0, 1.0, 0.0, 1.0, 1.0],
+                    [1.0, 0.0, -1.0, -1.0, 2.0],
+                ],
+                [
+                    [2.0, 1.0, 0.0, 1.0, 1.0],
+                    [2.0, -2.0, 0.0, 0.0, 1.0],
+                    [-1.0, -2.0, 0.0, 2.0, 0.0],
+                ],
+            ]
+        )
+        expected_token_ids = tensor([[2.0, 2.0, 0.0], [2.0, 1.0, 3.0]]).type(
+            torch.LongTensor
+        )
+
+        assert_expected(actual[0], expected_quantized)
+        assert_expected(actual[1], expected_token_ids)
+
+    def test_decode(self, codebook):
+        indices_flat = tensor([[0, 1]])  # (b, seq_len)
+        indices_shaped = tensor([[[0, 1], [2, 3]]])  # (b, shape)
+        actual_quantized_flat = codebook.decode(indices_flat)
+        actual_quantized = codebook.decode(indices_shaped)
+        expected_quantized_flat = tensor(
+            [[[1.0, 0.0, -1.0, -1.0, 2.0], [2.0, -2.0, 0.0, 0.0, 1.0]]]
+        )
+        expected_quantized = tensor(
+            [
+                [
+                    [[1.0, 0.0, -1.0, -1.0, 2.0], [2.0, -2.0, 0.0, 0.0, 1.0]],
+                    [[2.0, 1.0, 0.0, 1.0, 1.0], [-1.0, -2.0, 0.0, 2.0, 0.0]],
+                ]
+            ]
+        )
+        assert_expected(
+            actual_quantized_flat, expected_quantized_flat, rtol=0.0, atol=1e-4
+        )
+        assert_expected(actual_quantized, expected_quantized, rtol=0.0, atol=1e-4)

torchtune/modules/__init__.py

@@ -29,6 +29,7 @@
     TransformerSelfAttentionLayer,
 )
 from .vision_transformer import VisionTransformer
+from .vq_embeddings import VectorQuantizedEmbeddings
 
 __all__ = [
     "MultiHeadAttention",
@@ -38,6 +39,7 @@
     "KVCache",
     "RotaryPositionalEmbeddings",
     "VisionRotaryPositionalEmbeddings",
+    "VectorQuantizedEmbeddings",
     "RMSNorm",
     "TiedLinear",
     "Fp32LayerNorm",

torchtune/modules/vq_embeddings.py

@@ -0,0 +1,86 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Tuple
+
+import torch
+from torch import nn, Tensor
+from torch.nn import functional as F
+
+
+class VectorQuantizedEmbeddings(nn.Module):
+    """
+    Vector quantized embedding layer that takes in the output of an encoder
+    and performs a nearest-neighbor lookup in the embedding space.
+    Vector quantization was introduced in Oord et al. 2017 (https://arxiv.org/pdf/1711.00937.pdf)
+    to generate high-fidelity images, videos, and audio data.
+
+    This module currently does not support pre-training of the embeddings via EMA.
+
+    Code was adapted from torchmultimodal's `Codebook module
+    <https://github.com/facebookresearch/multimodal/blob/main/torchmultimodal/modules/layers/codebook.py>`_.
+
+    Args:
+        num_embeddings (int): Number of vectors in the embedding space.
+        embedding_dim (int): Dimensionality of the embedding vectors.
+    """
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+    ) -> None:
+        super().__init__()
+        self.embedding = nn.Parameter(torch.empty(num_embeddings, embedding_dim))
+        self.num_embeddings = num_embeddings
+        self.embedding_dim = embedding_dim
+
+    def forward(self, z: Tensor) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+            z (Tensor): Tensor containing a batch of encoder outputs of shape ``(b, s, d)``, where
+                b is batch size, s is sequence length or time, and d is ``embedding_dim``.
+
+        Returns:
+            Tuple[Tensor, Tensor]: The quantized input and the embedding vector ids that were used.
+
+        Raises:
+            ValueError: if input embedding dimension does not match embedding dimension of module
+        """
+        bsz, seq_len, z_embed_dim = z.shape
+        if z_embed_dim != self.embedding_dim:
+            raise ValueError(
+                f"Expected last dimension of input tensor ({z_embed_dim}) to be embedding size of {self.embedding_dim}"
+            )
+
+        # Flatten into batch dimension
+        z_flat = z.view(-1, z_embed_dim)
+        # Calculate distances from each encoder, E(x), output vector to each embedding vector, e, ||E(x) - e||^2
+        distances = torch.cdist(z_flat, self.embedding, p=2.0) ** 2
+
+        # Encoding - select closest embedding vectors, shape [b * s, ]
+        token_ids_flat = torch.argmin(distances, dim=1)
+
+        # Quantize - shape [b * s, d]
+        quantized_flat = self.decode(token_ids_flat)
+
+        # Straight through estimator
+        quantized_flat = z_flat + (quantized_flat - z_flat).detach()
+
+        # Reshape to original - [b, s, d] and [b, s]
+        quantized = quantized_flat.view(bsz, seq_len, z_embed_dim)
+        token_ids = token_ids_flat.view(bsz, seq_len)
+
+        return quantized, token_ids
+
+    def extra_repr(self) -> str:
+        return "num_embeddings={}, embedding_dim={}".format(
+            self.num_embeddings, self.embedding_dim
+        )
+
+    def decode(self, token_ids: Tensor) -> Tensor:
+        # Returns the embeddings of shape [b, s, d]
+        return F.embedding(token_ids, self.embedding)