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tests/models/jetmoe/test_modeling_jetmoe.py

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+# Copyright 2024 JetMoe AI and The HuggingFace Inc. 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.
+"""Testing suite for the PyTorch JetMoe model."""
+
+import unittest
+
+import pytest
+
+from transformers import AutoTokenizer, is_torch_available
+from transformers.testing_utils import (
+    cleanup,
+    require_flash_attn,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        JetMoeForCausalLM,
+        JetMoeModel,
+    )
+
+
+class JetMoeModelTester(CausalLMModelTester):
+    if is_torch_available():
+        base_model_class = JetMoeModel
+
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=False,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_key_value_heads=2,
+        kv_channels=8,
+        intermediate_size=37,
+        hidden_act="silu",
+        num_local_experts=4,
+        num_experts_per_tok=2,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        pad_token_id=0,
+        scope=None,
+    ):
+        super().__init__(parent)
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_token_type_ids = use_token_type_ids
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.kv_channels = kv_channels
+        self.num_attention_heads = num_key_value_heads * num_experts_per_tok
+        self.num_key_value_heads = num_key_value_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.num_local_experts = num_local_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.pad_token_id = pad_token_id
+        self.scope = scope
+
+
+@require_torch
+class JetMoeModelTest(CausalLMModelTest, unittest.TestCase):
+    test_mismatched_shapes = False
+    test_cpu_offload = False
+    test_disk_offload_bin = False
+    test_disk_offload_safetensors = False
+    model_tester_class = JetMoeModelTester
+
+    @require_flash_attn
+    @require_torch_accelerator
+    @pytest.mark.flash_attn_test
+    @slow
+    def test_flash_attn_2_inference_equivalence_right_padding(self):
+        self.skipTest(reason="JetMoe flash attention does not support right padding")
+
+    @unittest.skip(reason="JetMoe has no separate base model without a head.")
+    def test_model_base_model_prefix(self):
+        pass
+
+
+@require_torch
+class JetMoeIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    def test_model_8b_logits(self):
+        input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
+        model = JetMoeForCausalLM.from_pretrained("jetmoe/jetmoe-8b", device_map="auto", torch_dtype=torch.bfloat16)
+        input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
+        with torch.no_grad():
+            out = model(input_ids).logits.float().cpu()
+        # Expected mean on dim = -1
+        EXPECTED_MEAN = torch.tensor([[0.1943, -2.7299, -1.3466, -1.9385, -1.7457, -1.7472, -1.8647, -1.8547]])
+        torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=1e-2, atol=1e-2)
+        # slicing logits[0, 0, 0:30]
+        EXPECTED_SLICE = torch.tensor([-3.4844, 6.0625, 5.8750, -1.6875, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, 3.8750, 9.3750, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812, -4.7812])  # fmt: skip
+        torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, rtol=1e-4, atol=1e-4)
+
+    @slow
+    def test_model_8b_generation(self):
+        EXPECTED_TEXT_COMPLETION = """My favourite condiment is ....\nI love ketchup. I love"""
+        prompt = "My favourite condiment is "
+        tokenizer = AutoTokenizer.from_pretrained("jetmoe/jetmoe-8b", use_fast=False)
+        model = JetMoeForCausalLM.from_pretrained("jetmoe/jetmoe-8b", device_map="auto", torch_dtype=torch.bfloat16)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
+
+        # greedy generation outputs
+        generated_ids = model.generate(input_ids, max_new_tokens=10, temperature=0)
+        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
+
+    @slow
+    def test_model_8b_batched_generation(self):
+        EXPECTED_TEXT_COMPLETION = [
+            """My favourite condiment is ....\nI love ketchup. I love""",
+            """My favourite 2018 Christmas present was a new pair""",
+        ]
+        prompt = [
+            "My favourite condiment is ",
+            "My favourite ",
+        ]
+        tokenizer = AutoTokenizer.from_pretrained("jetmoe/jetmoe-8b", use_fast=False)
+        model = JetMoeForCausalLM.from_pretrained("jetmoe/jetmoe-8b", device_map="auto", torch_dtype=torch.bfloat16)
+        input_ids = tokenizer(prompt, return_tensors="pt", padding=True).to(model.model.embed_tokens.weight.device)
+
+        # greedy generation outputs
+        generated_ids = model.generate(**input_ids, max_new_tokens=10, temperature=0)
+        text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)