Add CoCa model

tests/models/coca/__init__.py

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+# 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.

tests/models/coca/test_coca_model.py

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+# 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, init_weights_with_constant, set_rng_seed
+from torchmultimodal.models.coca.coca_model import (
+ coca_vit,
+ CoCaForPretraining,
+ CoCaModelOutput,
+)
+
+
+class TestCoCaModel:
+ @pytest.fixture(autouse=True)
+ def random(self):
+ set_rng_seed(0)
+
+ @pytest.fixture
+ def batch_size(self):
+ return 2
+
+ @pytest.fixture
+ def vocab_size(self):
+ return 50
+
+ @pytest.fixture
+ def num_text_positions(self):
+ return 11
+
+ @pytest.fixture
+ def attention_pooler_output_dim(self):
+ return 8
+
+ @pytest.fixture
+ def text_output_dim(self):
+ return 8
+
+ @pytest.fixture
+ def image_size(self):
+ return 12
+
+ @pytest.fixture
+ def coca_model(
+ self,
+ vocab_size,
+ num_text_positions,
+ attention_pooler_output_dim,
+ text_output_dim,
+ image_size,
+ ):
+ coca_model = coca_vit(
+ vision_patch_size=4,
+ vision_dim_feedforward=24,
+ vision_n_layer=2,
+ vision_n_head=2,
+ vocab_size=vocab_size,
+ num_text_positions=num_text_positions,
+ text_hidden_dim=8,
+ text_n_layer=2,
+ text_n_head=2,
+ text_dim_feedforward=32,
+ text_output_dim=text_output_dim,
+ fusion_n_layer=2,
+ fusion_n_head=2,
+ fusion_dim_feedforward=32,
+ multimodal_output_projection_dim=vocab_size,
+ pooler_input_embed_dim=6,
+ pooler_output_embed_dim=attention_pooler_output_dim,
+ image_size=image_size,
+ pooler_n_head=2,
+ cascaded_pooler=False,
+ )
+ init_weights_with_constant(coca_model)
+ coca_model.eval()
+ return coca_model
+
+ @pytest.fixture
+ def text_inputs(self):
+ return torch.LongTensor(
+ [
+ [1, 3, 4, 5, 6, 7, 8, 2, 0, 0, 0],
+ [1, 25, 28, 34, 39, 45, 40, 5, 12, 6, 2],
+ ]
+ )
+
+ @pytest.fixture
+ def image_inputs(self, batch_size, image_size):
+ return torch.randn(batch_size, 3, image_size, image_size)
+
+ @pytest.fixture
+ def expected(
+ self,
+ batch_size,
+ vocab_size,
+ num_text_positions,
+ attention_pooler_output_dim,
+ text_output_dim,
+ ):
+ pooled_val = 0.3536
+ logit_val = 8.0
+ return CoCaModelOutput(
+ image_pooled_output=pooled_val
+ * torch.ones(batch_size, attention_pooler_output_dim),
+ text_pooled_output=pooled_val * torch.ones(batch_size, text_output_dim),
+ multimodal_embeddings=logit_val
+ * torch.ones(batch_size, num_text_positions - 1, vocab_size),
+ )
+
+ @pytest.fixture
+ def coca_for_pretraining(self, coca_model):
+ coca_for_pretraining = CoCaForPretraining(coca_model)
+ init_weights_with_constant(coca_for_pretraining)
+ coca_for_pretraining.eval()
+ return coca_for_pretraining
+
+ def test_coca_model(self, text_inputs, image_inputs, coca_model, expected):
+ actual = coca_model(image_inputs, text_inputs)
+ assert_expected(actual, expected, rtol=0, atol=1e-4)
+
+ def test_scripting(self, text_inputs, image_inputs, coca_model):
+ scripted_model = torch.jit.script(coca_model)
+ assert_expected(
+ scripted_model(image_inputs, text_inputs),
+ coca_model(image_inputs, text_inputs),
+ rtol=0,
+ atol=1e-4,
+ )
+
+ def test_coca_for_pretraining(
+ self, text_inputs, image_inputs, coca_for_pretraining
+ ):
+ actual_losses = coca_for_pretraining(image_inputs, text_inputs)
+ expected_losses = {
+ "contrastive": torch.tensor(0.6931),
+ "captioning": torch.tensor(3.9120),
+ }
+ assert_expected(actual_losses, expected_losses, rtol=0, atol=1e-4)

tests/models/coca/test_multimodal_decoder.py

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+# 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, init_weights_with_constant
+from torch import nn, Tensor
+from torchmultimodal.models.coca.multimodal_decoder import CoCaMultimodalDecoder
+
+
+class TestCoCaMultimodalDecoder:
+ @pytest.fixture
+ def batch_size(self):
+ return 2
+
+ @pytest.fixture
+ def input_seq_len(self):
+ return 5
+
+ @pytest.fixture
+ def num_image_positions(self):
+ return 10
+
+ @pytest.fixture
+ def text_embedding_dim(self):
+ return 4
+
+ @pytest.fixture
+ def multimodal_decoder(self, input_seq_len, batch_size, text_embedding_dim):
+ decoder = CoCaMultimodalDecoder(
+ input_seq_len=input_seq_len,
+ text_embedding_dim=text_embedding_dim,
+ n_layer=2,
+ n_head=2,
+ dim_feedforward=4 * text_embedding_dim,
+ output_dim=3,
+ final_layer_norm_eps=1e-5,
+ )
+ init_weights_with_constant(decoder)
+
+ # Custom init final MLP layer weight, final LN, and text projection
+ decoder.transformer_decoder.layer[1].feedforward.model[2].weight = nn.Parameter(
+ torch.arange(
+ decoder.transformer_decoder.layer[1]
+ .feedforward.model[2]
+ .weight.numel(),
+ dtype=torch.float,
+ ).reshape(
+ decoder.transformer_decoder.layer[1].feedforward.model[2].weight.shape
+ )
+ )
+ decoder.output_projection.weight = nn.Parameter(
+ torch.arange(decoder.output_projection.weight.numel(), dtype=torch.float)
+ .reshape(decoder.output_projection.weight.T.shape)
+ .T
+ )
+ decoder.transformer_decoder.final_layer_norm.weight = nn.Parameter(
+ torch.arange(
+ decoder.transformer_decoder.final_layer_norm.weight.numel(),
+ dtype=torch.float,
+ )
+ )
+ decoder.eval()
+ return decoder
+
+ @pytest.fixture
+ def text_inputs(self, batch_size, input_seq_len, text_embedding_dim):
+ return torch.arange(0.0, 1.0, 1.0 / 40).reshape(
+ batch_size, input_seq_len, text_embedding_dim
+ )
+
+ @pytest.fixture
+ def image_inputs(self, batch_size, num_image_positions, text_embedding_dim):
+ return torch.arange(10.0, 20.0, 1.0 / 8).reshape(
+ batch_size, num_image_positions, text_embedding_dim
+ )
+
+ @pytest.fixture
+ def expected(self):
+ return Tensor(
+ [
+ [
+ [58.2492, 66.7214, 75.1935],
+ [58.2492, 66.7214, 75.1935],
+ [58.2492, 66.7214, 75.1935],
+ [58.2492, 66.7214, 75.1935],
+ [58.2492, 66.7214, 75.1935],
+ ],
+ [
+ [58.2492, 66.7214, 75.1935],
+ [58.2492, 66.7214, 75.1935],
+ [58.2492, 66.7214, 75.1935],
+ [58.2492, 66.7214, 75.1935],
+ [58.2492, 66.7214, 75.1935],
+ ],
+ ]
+ )
+
+ def test_coca_multimodal_decoder(
+ self, text_inputs, image_inputs, multimodal_decoder, expected
+ ):
+ actual = multimodal_decoder(text_inputs, image_inputs)
+ assert_expected(actual, expected, rtol=0, atol=1e-4)
+
+ def test_scripting(self, text_inputs, image_inputs, multimodal_decoder):
+ scripted_multimodal_decoder = torch.jit.script(multimodal_decoder)
+ assert_expected(
+ scripted_multimodal_decoder(text_inputs, image_inputs),
+ multimodal_decoder(text_inputs, image_inputs),
+ rtol=0,
+ atol=1e-4,
+ )