add blip2 layer under torchmm/models (#484)

Peng Chen · facebook-github-bot · commit d6d3d759fe39 · 2023-10-11T18:11:33.000-07:00
Summary: Pull Request resolved: #484 as title Differential Revision: D50145708 fbshipit-source-id: 938c266c880b29e14b2b2a954d54c67e92dddb1b
diff --git a/tests/models/blip2/test_blip2.py b/tests/models/blip2/test_blip2.py
@@ -0,0 +1,137 @@
+# 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
+import torch.nn as nn
+from tests.test_utils import assert_expected, init_weights_with_constant
+from torchmultimodal.models.blip2.blip2 import BLIP2
+from torchmultimodal.models.blip2.qformer_model import QformerForCLM
+from torchmultimodal.modules.encoders.vision_transformer import VisionTransformer
+from torchmultimodal.modules.layers.patch_embedding import PatchEmbeddings
+from torchmultimodal.modules.layers.transformer import TransformerEncoder
+
+
+@pytest.fixture
+def dim_q():
+    return 4
+
+
+@pytest.fixture
+def dim_kv():
+    return 2
+
+
+@pytest.fixture
+def dim_feedforward():
+    return 6
+
+
+@pytest.fixture
+def num_hidden_layers():
+    return 2
+
+
+@pytest.fixture
+def num_heads():
+    return 2
+
+
+@pytest.fixture
+def vocab_size():
+    return 20
+
+
+@pytest.fixture
+def qformer_model_for_clm(
+    dim_q,
+    dim_kv,
+    dim_feedforward,
+    num_hidden_layers,
+    num_heads,
+    vocab_size,
+):
+    qformer_for_clm = QformerForCLM(
+        dim_q=dim_q,
+        dim_kv=dim_kv,
+        dim_feedforward=dim_feedforward,
+        num_heads=num_heads,
+        attn_dropout=0.0,
+        dropout=0.0,
+        num_hidden_layers=num_hidden_layers,
+        max_position_embeddings=512,
+        vocab_size=vocab_size,
+    )
+    return qformer_for_clm
+
+
+@pytest.fixture
+def vit():
+    embedding = PatchEmbeddings(image_size=2, patch_size=1, hidden_size=2)
+    encoder = TransformerEncoder(
+        n_layer=1,
+        d_model=2,
+        n_head=1,
+        dim_feedforward=1,
+        activation=nn.GELU,
+        norm_first=True,
+        final_layer_norm_eps=1e-5,
+    )
+    image_encoder = VisionTransformer(
+        embeddings=embedding,
+        encoder=encoder,
+    )
+    init_weights_with_constant(image_encoder)
+    image_encoder.eval()
+    return image_encoder
+
+
+@pytest.fixture
+def blip2(dim_q, dim_kv, qformer_model_for_clm, vit):
+    blip2 = BLIP2(
+        dim_q=dim_q,
+        image_encoder_embedding_dim=dim_kv,
+        qformer=qformer_model_for_clm,
+        vision_encoder=vit,
+        embedding_dim=4,
+        decoder_bos_token_id=19,
+    )
+    init_weights_with_constant(blip2)
+    blip2.eval()
+    return blip2
+
+
+@pytest.fixture
+def attn_mask():
+    return torch.Tensor([[1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 1.0, 1.0]])
+
+
+class TestBLIP2:
+    def test_blip2(self, blip2, attn_mask):
+        image = torch.ones(2, 3, 2, 2)
+        input_ids = torch.ones(2, 4).long()
+        output = blip2(image, input_ids, attn_mask)
+        assert_expected(
+            output.image_features, torch.ones([2, 32, 4]) * 0.5, rtol=0, atol=1e-4
+        )
+        assert_expected(
+            output.text_features, torch.ones([2, 4]) * 0.5, rtol=0, atol=1e-4
+        )
+        assert_expected(
+            output.image_embeddings, torch.ones([2, 5, 2]), rtol=0, atol=1e-4
+        )
+        assert_expected(
+            output.prediction_scores, torch.ones([2, 4, 20]) * 5, rtol=0, atol=1e-4
+        )
+
+    def test_blip2_scripting(self, blip2, attn_mask):
+        image = torch.ones(2, 3, 2, 2)
+        input_ids = torch.ones(2, 4).long()
+        scripted_model = torch.jit.script(blip2)
+        actual = scripted_model(image, input_ids, attn_mask)
+        expected = blip2(image, input_ids, attn_mask)
+        assert_expected(actual, expected)
diff --git a/torchmultimodal/models/blip2/blip2.py b/torchmultimodal/models/blip2/blip2.py
@@ -0,0 +1,157 @@
+# 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 NamedTuple, Optional
+
+import torch
+
+from torch import nn, Tensor
+from torch.nn import functional as F
+from torchmultimodal.modules.layers.transformer import TransformerOutput
+
+
+class Blip2Output(NamedTuple):
+    """
+    BLIP2 model output for loss computation.
+
+    image_embeddings(Tensor): normalized image embeddings returned by the visual encoder
+        with shape [bsz x seq_len x embed_dim].
+    image_features(Tensor): Image features after qformer and projection (for stage 1 training)
+        with shape [bsz, num_query_tokens, embed_dim]
+    image_qformer_output(Tensor) : last hidden state for qformer output by given image input
+    text_features(Optional[Tensor]): Text features after qformer and projection if text input is provided
+        with shape [bsz, embed_dim]
+    prediction_scores (Optional[Tensor]): computed for next word prediction
+        with shape of [bsz, seq_len, vocab_size]
+    """
+
+    image_embeddings: Tensor
+    image_features: Tensor
+    image_qformer_output: Tensor
+    text_features: Optional[Tensor] = None
+    prediction_scores: Optional[Tensor] = None
+
+
+class BLIP2(nn.Module):
+    """
+    BLIP2(https://arxiv.org/pdf/2301.12597.pdf) provides a pre-training strategy to bootstrap vision-language
+    pre-training from frozen image encoders and frozen large language models(LLM). BLIP-2 bridges the modality gap
+    and facilitates cross-modal alignment via Querying Transformer (Q-former). Q-former is a lightweight transformer
+    which has a set of learnable query vectors to extract visual features from the frozen image encoder.
+
+    Args:
+        qformer(nn.Module): Querying Transformer (Q-former)
+        visual_encoder(nn.Module): Frozen image encoder
+        dim_q(int) : Dimension of query tensor, this value should be the same as dim_q in qformer.
+        image_encoder_embedding_dim(int): Embedding dimension for image encoder,
+            this value should be the same as dim_kv in qformer.
+        freeze_visual_encoder(bool): Whether to freeze the visual encoder, default to True
+        cross_attention_freq(int): Frequency of adding cross-attention block in Qformer, default to 2
+        embedding_dim(int): Embedding dimension
+        num_query_token(int): Number of query tokens in Qformer, default to 32
+        init_query_tokens(bool): whether init query token params, default to True
+        decoder_bos_token_id(Optional[int]): bos_token_id used in decoder, default to None
+    """
+
+    def __init__(
+        self,
+        qformer: nn.Module,
+        vision_encoder: nn.Module,
+        dim_q: int,
+        image_encoder_embedding_dim: int,
+        freeze_vision_encoder: bool = True,
+        cross_attention_freq: int = 2,
+        embedding_dim: int = 256,
+        num_query_token: int = 32,
+        init_query_tokens: bool = True,
+        decoder_bos_token_id: Optional[int] = None,
+    ):
+        super().__init__()
+        self.vision_encoder = vision_encoder
+        if freeze_vision_encoder:
+            for param in self.vision_encoder.parameters():
+                param.requires_grad = False
+            self.vision_encoder = self.vision_encoder.eval()
+
+        self.qformer = qformer
+        self.decoder_bos_token_id = decoder_bos_token_id
+        self.dim_q = dim_q
+        self.query_tokens = nn.Parameter(torch.zeros(1, num_query_token, self.dim_q))
+        if init_query_tokens:
+            self.query_tokens.data.normal_(mean=0.0, std=0.02)
+
+        self.vision_proj = nn.Linear(self.dim_q, embedding_dim)
+        self.text_proj = nn.Linear(self.dim_q, embedding_dim)
+        self.ln_vision = nn.LayerNorm(image_encoder_embedding_dim)
+
+    def forward(
+        self,
+        image: Tensor,
+        input_ids: Optional[Tensor] = None,
+        attention_mask: Optional[Tensor] = None,
+    ) -> Blip2Output:
+        """
+        Args:
+            image(Tensor): Image input tensor with shape [B, C, H, W]
+            input_ids(Optional[Tensor]): Text input tensor with shape [bsz, seq_len]
+            attention_mask(Optional[Tensor]): Attention mask tensor with shape [bsz, seq_len]
+
+        Returns:
+            return BLIP2 model output(Blip2Output).
+        """
+        vision_encoder_output = self.vision_encoder(image)
+        if isinstance(vision_encoder_output, TransformerOutput):
+            vision_encoder_output = vision_encoder_output.last_hidden_state
+        assert vision_encoder_output is not None
+        image_embeds = self.ln_vision(vision_encoder_output)
+        # query tokens: [batch_size, num_query_token, encoder_hidden_size]
+        query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
+        query_output = self.qformer.model(
+            query_embeds=query_tokens,
+            encoder_hidden_states=image_embeds,
+            use_cache=True,
+        )
+
+        # image_feats: [batch_size, num_query_token, embedding_dim]
+        image_feats = F.normalize(self.vision_proj(query_output[0]), dim=-1)
+
+        text_feats: Optional[Tensor] = None
+        prediction_scores: Optional[Tensor] = None
+        if input_ids is not None:
+            text_output = self.qformer.model(
+                input_ids,
+                attention_mask=attention_mask,
+                use_cache=False,
+            )
+            text_feats = F.normalize(self.text_proj(text_output[0][:, 0, :]), dim=-1)
+
+            decoder_input_ids = input_ids.clone()
+            if self.decoder_bos_token_id is not None:
+                # pyre-ignore
+                decoder_input_ids[:, 0] = self.decoder_bos_token_id
+
+            query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(
+                input_ids.device
+            )
+            if attention_mask is not None:
+                attention_mask = torch.cat([query_atts, attention_mask], dim=1)
+
+            # set use_cache = False since past_key_values should be cached in previous steps.
+            prediction_scores = self.qformer(
+                input_ids=decoder_input_ids,
+                attention_mask=attention_mask,
+                past_key_values=query_output[1],
+                use_cache=False,
+            )
+
+        return Blip2Output(
+            image_embeddings=image_embeds,
+            image_features=image_feats,
+            image_qformer_output=query_output[0],
+            text_features=text_feats,
+            prediction_scores=prediction_scores,
+        )
diff --git a/torchmultimodal/models/blip2/qformer_model.py b/torchmultimodal/models/blip2/qformer_model.py
@@ -49,8 +49,8 @@ def __init__(
         activation: Callable[..., nn.Module] = nn.ReLU,
         attn_dropout: float = 0.0,
         dropout: float = 0.0,
-        cross_attention_freq=2,
-    ):
+        cross_attention_freq: int = 2,
+    ) -> None:
         super().__init__()
         self.query_length = query_length
         self.embeddings = QformerEmbedding(
@@ -220,7 +220,7 @@ def __init__(
         activation: Callable[..., nn.Module] = nn.GELU,
         attn_dropout: float = 0.0,
         dropout: float = 0.0,
-        cross_attention_freq=2,
+        cross_attention_freq: int= 2,
     ) -> None:
         super().__init__()
         self.pad_token_id = pad_token_id
