add unittest

paddlenlp/transformers/mixtral/modeling.py

@@ -1070,8 +1070,6 @@ def __init__(self, config: MixtralConfig):
         )
         self.norm = MixtralRMSNorm(config)
 
-        self.gradient_checkpointing = False
-
     def get_input_embeddings(self):
         return self.embed_tokens
 

tests/transformers/mixtral/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

tests/transformers/mixtral/test_modeling.py

@@ -0,0 +1,318 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+import unittest
+
+import paddle
+
+from paddlenlp.transformers import MixtralConfig, MixtralForCausalLM, MixtralModel
+from tests.transformers.test_configuration_common import ConfigTester
+from tests.transformers.test_generation_utils import GenerationTesterMixin
+from tests.transformers.test_modeling_common import (
+    ModelTesterMixin,
+    ids_tensor,
+    random_attention_mask,
+)
+
+
+class MixtralModelTester:
+    def __init__(
+        self,
+        parent,
+        vocab_size=32000,
+        hidden_size=64,
+        num_hidden_layers=2,
+        num_attention_heads=8,
+        masked_softmax_fusion=True,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        is_training=True,
+        use_cache=False,
+        bos_token_id=1,
+        eos_token_id=2,
+        apply_residual_connection_post_layernorm=False,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        attention_softmax_in_fp32=True,
+        pretraining_tp=1,  # TP rank used when training with megatron
+        dtype="bfloat16",
+        slow_but_exact=False,
+        batch_size: int = 2,
+        seq_length: int = 10,
+        type_sequence_label_size=2,
+        activation_function="gelu",
+        num_labels=3,
+        num_choices=4,
+        scope=None,
+        dropout=0.56,
+        use_input_mask: bool = False,
+        use_labels: bool = False,
+        return_dict=False,
+    ):
+        self.parent: MixtralModelTest = parent
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.masked_softmax_fusion = masked_softmax_fusion
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.is_training = is_training
+        self.use_cache = use_cache
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+        self.attention_softmax_in_fp32 = attention_softmax_in_fp32
+        self.pretraining_tp = pretraining_tp
+        self.dtype = dtype
+        self.slow_but_exact = slow_but_exact
+
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.type_sequence_label_size = type_sequence_label_size
+        self.activation_function = activation_function
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.scope = scope
+        self.dropout = dropout
+
+        self.use_input_mask = use_input_mask
+        self.use_labels = use_labels
+        self.return_dict = return_dict
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, dtype=paddle.int64)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = self.get_config()
+        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self) -> MixtralConfig:
+        return MixtralConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            masked_softmax_fusion=self.masked_softmax_fusion,
+            layer_norm_epsilon=self.layer_norm_epsilon,
+            initializer_range=self.initializer_range,
+            use_cache=self.use_cache,
+            bos_token_id=self.bos_token_id,
+            eos_token_id=self.eos_token_id,
+            apply_residual_connection_post_layernorm=self.apply_residual_connection_post_layernorm,
+            hidden_dropout=self.hidden_dropout,
+            attention_dropout=self.attention_dropout,
+            attention_softmax_in_fp32=self.attention_softmax_in_fp32,
+            pretraining_tp=self.pretraining_tp,
+            dtype=self.dtype,
+            slow_but_exact=self.slow_but_exact,
+            activation_function=self.activation_function,
+        )
+
+    def create_and_check_model(
+        self, config: MixtralConfig, input_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = MixtralModel(config)
+        model.eval()
+        result = model(input_ids)
+        self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size])
+
+    def create_and_check_model_attention_mask(
+        self, config: MixtralConfig, input_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = MixtralModel(config)
+        model.eval()
+        attn_mask_2d = random_attention_mask([self.batch_size, self.seq_length])
+        result_2d = model(input_ids, attention_mask=attn_mask_2d)[0]
+        batch, seq_length = input_ids.shape
+        causal_mask = paddle.tril(paddle.ones((batch, seq_length, seq_length), dtype=attn_mask_2d.dtype))
+        attn_mask_3d = causal_mask & attn_mask_2d.unsqueeze(-1)
+        result_3d = model(input_ids, attention_mask=attn_mask_3d)[0]
+        attn_mask_4d = attn_mask_3d.unsqueeze(1)
+        result_4d = model(input_ids, attention_mask=attn_mask_4d)[0]
+        result_no_attention_mask = model(input_ids, attention_mask=None)[0]
+        # Assert non-padding tokens have the same logits with different attention_mask shape
+        self.parent.assertTrue((result_2d[attn_mask_2d] == result_3d[attn_mask_2d]).all())
+        self.parent.assertTrue((result_2d[attn_mask_2d] == result_4d[attn_mask_2d]).all())
+        self.parent.assertTrue((result_2d[attn_mask_2d] == result_no_attention_mask[attn_mask_2d]).all())
+
+    def create_and_check_model_past_large_inputs(
+        self,
+        config: MixtralConfig,
+        input_ids,
+        input_mask,
+        sequence_labels,
+        token_labels,
+        choice_labels,
+    ):
+        model = MixtralModel(config)
+        model.eval()
+
+        # first forward pass
+        outputs = model(input_ids, attention_mask=input_mask, use_cache=True, return_dict=self.return_dict)
+        past_key_values = outputs.past_key_values if self.return_dict else outputs[2]
+
+        # create hypothetical multiple next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 3), self.vocab_size)
+        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
+
+        # append to next input_ids and
+        next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1)
+        next_attention_mask = paddle.concat([input_mask, next_mask], axis=-1)
+
+        outputs = model(
+            next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True, return_dict=self.return_dict
+        )
+
+        output_from_no_past = outputs[2][0]
+
+        outputs = model(
+            next_tokens,
+            attention_mask=next_attention_mask,
+            past_key_values=past_key_values,
+            output_hidden_states=True,
+            return_dict=self.return_dict,
+        )
+
+        output_from_past = outputs[2][0]
+
+        # select random slice
+        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
+        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
+        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
+
+        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
+
+        # test that outputs are equal for slice
+        self.parent.assertTrue(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+    def create_and_check_lm_head_model(self, config, input_ids, input_mask, *args):
+        model = MixtralForCausalLM(config)
+        model.eval()
+
+        result = model(
+            input_ids,
+            use_cache=True,
+            labels=input_ids if self.parent.use_labels else None,
+            return_dict=self.parent.return_dict,
+        )
+        if self.parent.use_labels:
+            self.parent.assertIsInstance(result[0].item(), float)
+            self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length, self.vocab_size])
+        else:
+            self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.vocab_size])
+
+    def check_model_position_ids(self, config, input_ids, input_mask, *args):
+        model = MixtralForCausalLM(config)
+        model.eval()
+
+        result_no_position_id = model(
+            input_ids,
+            labels=input_ids if self.parent.use_labels else None,
+            return_dict=self.parent.return_dict,
+        )
+        batch_size, seq_len = input_ids.shape
+        position_ids = paddle.arange(seq_len).expand((batch_size, seq_len))
+        result_position_id = model(
+            input_ids,
+            position_ids,
+            labels=input_ids if self.parent.use_labels else None,
+            return_dict=self.parent.return_dict,
+        )
+        if self.parent.use_labels:
+            self.parent.assertTrue((result_position_id[1] == result_no_position_id[1]).all())
+        else:
+            self.parent.assertTrue((result_position_id[0] == result_no_position_id[0]).all())
+
+
+class MixtralModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+    base_model_class = MixtralModel
+    return_dict = False
+    use_labels = False
+    use_test_model_name_list = False
+
+    all_model_classes = (MixtralModel, MixtralForCausalLM)
+    all_generative_model_classes = {MixtralForCausalLM: (MixtralModel, "mixtral")}
+
+    def setUp(self):
+        super().setUp()
+
+        self.model_tester = MixtralModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=MixtralConfig, vocab_size=256, hidden_size=24)
+
+    def _get_input_ids_and_config(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        input_ids = inputs_dict[self.input_name]
+        attention_mask = paddle.ones_like(input_ids, dtype=paddle.int64)
+
+        max_batch_size = 2
+        sequence_length = input_ids.shape[-1] // 2
+        input_ids = input_ids[:max_batch_size, :sequence_length]
+        attention_mask = attention_mask[:max_batch_size, :sequence_length]
+        max_length = 3
+
+        return config, input_ids, attention_mask, max_length
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_model_attention_mask(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model_attention_mask(*config_and_inputs)
+
+    def test_model_position_ids(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_model_position_ids(*config_and_inputs)
+
+    def test_generate_without_input_ids(self):
+        # this requires 4-D attention mask logic, which is not supported yet
+        pass
+
+    def test_mixtral_lm_head_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
+
+
+if __name__ == "__main__":
+    unittest.main()