Copy transformer impl to oss folder

Differential Revision: D48132386

fbshipit-source-id: 4838e98a992313040fabde82d2082c48cb802fd5

tests/modules/layers/test_transformer.py

@@ -0,0 +1,197 @@
+# 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 torch import Tensor
+from torchmultimodal.modules.layers.transformer import (
+ TransformerEncoder,
+ TransformerEncoderLayer,
+)
+from torchmultimodal.modules.layers.transformer import TransformerOutput
+
+
+@pytest.fixture(autouse=True)
+def random():
+ set_rng_seed(4)
+
+
+class TestTransformerEncoderLayer:
+ @pytest.fixture
+ def get_encoder_layer(self):
+ def create_layer(norm_first):
+ model = TransformerEncoderLayer(
+ d_model=2,
+ n_head=1,
+ dim_feedforward=2,
+ norm_first=norm_first,
+ )
+ init_weights_with_constant(model)
+ model.eval()
+ return model
+
+ return create_layer
+
+ @pytest.fixture
+ def inputs(self):
+ return Tensor([[[1, 2], [4, 2], [1, 1]]])
+
+ @pytest.mark.parametrize(
+ "norm_first, expected_output",
+ [
+ (True, Tensor([[[15.0, 16.0], [18.0, 16.0], [15.0, 15.0]]])),
+ (False, Tensor([[[0.0, 2.0], [2.0, 0.0], [1.0, 1.0]]])),
+ ],
+ )
+ def test_forward(self, norm_first, expected_output, inputs, get_encoder_layer):
+ model = get_encoder_layer(norm_first)
+ actual = model(inputs)
+ assert_expected(actual, expected_output, rtol=0, atol=1e-4)
+
+ @pytest.mark.parametrize(
+ "norm_first",
+ [(True,), (False,)],
+ )
+ def test_scripting(self, norm_first, inputs, get_encoder_layer):
+ model = get_encoder_layer(norm_first)
+ scripted_model = torch.jit.script(model)
+ assert_expected(scripted_model(inputs), model(inputs), rtol=0, atol=1e-4)
+
+
+class TestTransformerEncoder:
+ @pytest.fixture
+ def get_encoder(self):
+ def create_encoder(norm_first, final_layer_norm_eps=None):
+ model = TransformerEncoder(
+ n_layer=2,
+ d_model=2,
+ n_head=1,
+ dim_feedforward=2,
+ norm_first=norm_first,
+ final_layer_norm_eps=final_layer_norm_eps,
+ )
+ init_weights_with_constant(model)
+ model.eval()
+ return model
+
+ return create_encoder
+
+ @pytest.fixture
+ def inputs(self):
+ return Tensor([[[2, 3], [1, 2]]])
+
+ @pytest.mark.parametrize(
+ "norm_first, return_hidden_states, expected_output",
+ [
+ (
+ True,
+ False,
+ TransformerOutput(
+ last_hidden_state=Tensor([[[30.0, 31.0], [29.0, 30.0]]])
+ ),
+ ),
+ (
+ False,
+ False,
+ TransformerOutput(last_hidden_state=Tensor([[[0.0, 2.0], [0.0, 2.0]]])),
+ ),
+ (
+ True,
+ True,
+ TransformerOutput(
+ last_hidden_state=Tensor([[[30.0, 31.0], [29.0, 30.0]]]),
+ hidden_states=[
+ Tensor([[[16.0, 17.0], [15.0, 16.0]]]),
+ Tensor([[[30.0, 31.0], [29.0, 30.0]]]),
+ ],
+ ),
+ ),
+ (
+ False,
+ True,
+ TransformerOutput(
+ last_hidden_state=Tensor([[[0.0, 2.0], [0.0, 2.0]]]),
+ hidden_states=[
+ Tensor([[[0.0, 2.0], [0.0, 2.0]]]),
+ Tensor([[[0.0, 2.0], [0.0, 2.0]]]),
+ ],
+ ),
+ ),
+ ],
+ )
+ def test_forward(
+ self, norm_first, return_hidden_states, expected_output, inputs, get_encoder
+ ):
+ model = get_encoder(norm_first)
+ actual = model(inputs, return_hidden_states=return_hidden_states)
+ if expected_output.hidden_states is None:
+ assert actual.hidden_states is None
+ else:
+ assert_expected(actual.hidden_states[0], inputs)
+ for state_1, state_2 in zip(
+ expected_output.hidden_states, actual.hidden_states[1:]
+ ):
+ assert_expected(state_1, state_2)
+
+ assert actual.attentions == expected_output.attentions
+ assert_expected(
+ actual.last_hidden_state,
+ expected_output.last_hidden_state,
+ rtol=0,
+ atol=1e-4,
+ )
+
+ @pytest.mark.parametrize(
+ "norm_first, expected_output",
+ [
+ (
+ True,
+ TransformerOutput(
+ last_hidden_state=Tensor([[[1.9073e-05, 2.0], [2.2888e-05, 2.0]]]),
+ hidden_states=[
+ Tensor([[[16.0, 17.0], [15.0, 16.0]]]),
+ Tensor([[[30.0, 31.0], [29.0, 30.0]]]),
+ ],
+ ),
+ ),
+ (
+ False,
+ TransformerOutput(
+ last_hidden_state=Tensor([[[5.0068e-06, 2.0], [5.0068e-06, 2.0]]]),
+ hidden_states=[
+ Tensor([[[0.0, 2.0], [0.0, 2.0]]]),
+ Tensor([[[0.0, 2.0], [0.0, 2.0]]]),
+ ],
+ ),
+ ),
+ ],
+ )
+ def test_forward_with_final_ln(
+ self, norm_first, expected_output, inputs, get_encoder
+ ):
+ model = get_encoder(norm_first=norm_first, final_layer_norm_eps=1e-5)
+ actual = model(inputs, return_hidden_states=True)
+ assert_expected(
+ expected_output.last_hidden_state,
+ actual.last_hidden_state,
+ rtol=0,
+ atol=1e-4,
+ )
+ for state_1, state_2 in zip(
+ expected_output.hidden_states, actual.hidden_states[1:]
+ ):
+ assert_expected(state_1, state_2)
+
+ @pytest.mark.parametrize(
+ "norm_first",
+ [(True,), (False,)],
+ )
+ def test_scripting(self, norm_first, inputs, get_encoder):
+ model = get_encoder(norm_first)
+ scripted_model = torch.jit.script(model)
+ assert_expected(scripted_model(inputs), model(inputs), rtol=0, atol=1e-4)