init

transformers/tests/models/bloom/test_modeling_bloom.py

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+# Copyright 2022 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.
+#
+
+import math
+import unittest
+
+from transformers import BloomConfig, is_torch_available
+from transformers.testing_utils import require_torch, require_torch_accelerator, slow, torch_device
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        BloomForCausalLM,
+        BloomForQuestionAnswering,
+        BloomForSequenceClassification,
+        BloomForTokenClassification,
+        BloomModel,
+        BloomTokenizerFast,
+    )
+
+
+@require_torch
+class BloomModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=14,
+        seq_length=7,
+        is_training=True,
+        use_token_type_ids=False,
+        use_input_mask=True,
+        use_labels=True,
+        use_mc_token_ids=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_token_type_ids = use_token_type_ids
+        self.use_input_mask = use_input_mask
+        self.use_labels = use_labels
+        self.use_mc_token_ids = use_mc_token_ids
+        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.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_dropout_prob = attention_dropout_prob
+        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.scope = None
+        self.bos_token_id = vocab_size - 1
+        self.eos_token_id = vocab_size - 1
+        self.pad_token_id = vocab_size - 1
+
+    def get_large_model_config(self):
+        return BloomConfig.from_pretrained("bigscience/bloom")
+
+    def prepare_config_and_inputs(self, gradient_checkpointing=False):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        sequence_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+
+        config = self.get_config(gradient_checkpointing=gradient_checkpointing)
+
+        return (config, input_ids, input_mask, sequence_labels)
+
+    def get_config(self, gradient_checkpointing=False, slow_but_exact=True):
+        return BloomConfig(
+            vocab_size=self.vocab_size,
+            seq_length=self.seq_length,
+            hidden_size=self.hidden_size,
+            n_layer=self.num_hidden_layers,
+            n_head=self.num_attention_heads,
+            hidden_dropout=self.hidden_dropout_prob,
+            attention_dropout=self.attention_dropout_prob,
+            n_positions=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            initializer_range=self.initializer_range,
+            use_cache=True,
+            bos_token_id=self.bos_token_id,
+            eos_token_id=self.eos_token_id,
+            pad_token_id=self.pad_token_id,
+            num_labels=self.num_labels,
+            gradient_checkpointing=gradient_checkpointing,
+            slow_but_exact=slow_but_exact,
+            dtype="float32",
+        )
+
+    def create_and_check_bloom_model(self, config, input_ids, input_mask, *args):
+        model = BloomModel(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(input_ids)
+
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+        self.parent.assertEqual(len(result.past_key_values), config.n_layer)
+
+    def create_and_check_bloom_model_past(self, config, input_ids, input_mask, *args):
+        model = BloomModel(config=config)
+
+        model.to(torch_device)
+        model.eval()
+
+        # first forward pass
+        outputs = model(input_ids, attention_mask=torch.ones_like(input_ids), use_cache=True)
+        outputs_use_cache_conf = model(input_ids, attention_mask=torch.ones_like(input_ids))
+        outputs_no_past = model(input_ids, use_cache=False, attention_mask=torch.ones_like(input_ids))
+
+        self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
+        self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
+
+        past = outputs["past_key_values"]
+
+        # create hypothetical next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
+
+        # append to next input_ids and token_type_ids
+        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+
+        output_from_no_past = model(next_input_ids)["last_hidden_state"]
+        output_from_past = model(next_tokens, past_key_values=past)["last_hidden_state"]
+
+        # select random slice
+        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
+        output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
+        output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
+
+        # test that outputs are equal for slice
+        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
+
+    def create_and_check_bloom_model_attention_mask_past(self, config, input_ids, input_mask, *args):
+        model = BloomModel(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        # create attention mask
+        attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
+        half_seq_length = self.seq_length // 2
+        attn_mask[:, half_seq_length:] = 0
+
+        # first forward pass
+        output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
+
+        # create hypothetical next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
+
+        # change a random masked slice from input_ids
+        random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
+        random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
+        input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
+
+        # append to next input_ids and attn_mask
+        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+        attn_mask = torch.cat(
+            [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
+            dim=1,
+        )
+
+        # get two different outputs
+        output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
+        output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]
+
+        # select random slice
+        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
+        output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
+        output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
+
+        # test that outputs are equal for slice
+        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
+
+    def create_and_check_bloom_model_past_large_inputs(self, config, input_ids, input_mask, *args):
+        model = BloomModel(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        # first forward pass
+        outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
+
+        output, past = outputs.to_tuple()
+
+        # create hypothetical next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
+        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
+
+        # append to next input_ids and token_type_ids
+        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
+
+        output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
+        output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past)[
+            "last_hidden_state"
+        ]
+        self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
+
+        # 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()
+
+        # test that outputs are equal for slice
+        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
+
+    def create_and_check_lm_head_model(self, config, input_ids, input_mask, *args):
+        model = BloomForCausalLM(config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(input_ids, labels=input_ids)
+        self.parent.assertEqual(result.loss.shape, ())
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_sequence_classification_model(self, config, input_ids, input_mask, *args):
+        config.num_labels = self.num_labels
+        model = BloomForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(input_ids, attention_mask=input_mask)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_token_classification_model(self, config, input_ids, input_mask, *args):
+        model = BloomForTokenClassification(config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(input_ids, attention_mask=input_mask)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
+    def create_and_check_forward_and_backwards(
+        self, config, input_ids, input_mask, *args, gradient_checkpointing=False
+    ):
+        model = BloomForCausalLM(config)
+        model.to(torch_device)
+        if gradient_checkpointing:
+            model.gradient_checkpointing_enable()
+
+        result = model(input_ids, labels=input_ids)
+        self.parent.assertEqual(result.loss.shape, ())
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+        result.loss.backward()
+
+    def create_and_check_bloom_weight_initialization(self, config, *args):
+        model = BloomModel(config)
+        model_std = model.config.initializer_range / math.sqrt(2 * model.config.n_layer)
+        for key in model.state_dict():
+            if "c_proj" in key and "weight" in key:
+                self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001)
+                self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01)
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+
+        config, input_ids, input_mask, sequence_labels = config_and_inputs
+
+        inputs_dict = {"input_ids": input_ids}
+
+        return config, inputs_dict
+
+
+@require_torch
+class BloomModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (
+            BloomModel,
+            BloomForCausalLM,
+            BloomForSequenceClassification,
+            BloomForTokenClassification,
+            BloomForQuestionAnswering,
+        )
+        if is_torch_available()
+        else ()
+    )
+
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": BloomModel,
+            "question-answering": BloomForQuestionAnswering,
+            "text-classification": BloomForSequenceClassification,
+            "text-generation": BloomForCausalLM,
+            "token-classification": BloomForTokenClassification,
+            "zero-shot": BloomForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    fx_compatible = True
+    test_missing_keys = False
+    test_pruning = False
+    test_torchscript = True  # torch.autograd functions seems not to be supported
+
+    def setUp(self):
+        self.model_tester = BloomModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=BloomConfig, n_embd=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_bloom_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_bloom_model(*config_and_inputs)
+
+    def test_bloom_model_past(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_bloom_model_past(*config_and_inputs)
+
+    def test_bloom_model_att_mask_past(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_bloom_model_attention_mask_past(*config_and_inputs)
+
+    def test_bloom_model_past_large_inputs(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_bloom_model_past_large_inputs(*config_and_inputs)
+
+    def test_bloom_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)
+
+    def test_bloom_sequence_classification_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_sequence_classification_model(*config_and_inputs)
+
+    def test_bloom_token_classification_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_token_classification_model(*config_and_inputs)
+
+    def test_bloom_gradient_checkpointing(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
+
+    def test_bloom_weight_initialization(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_bloom_weight_initialization(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "bigscience/bigscience-small-testing"
+        model = BloomModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    @slow
+    @require_torch_accelerator
+    def test_simple_generation(self):
+        # This test is a bit flaky. For some GPU architectures, pytorch sets by default allow_fp16_reduced_precision_reduction = True and some operations
+        # do not give the same results under this configuration, especially torch.baddmm and torch.bmm. https://pytorch.org/docs/stable/notes/numerical_accuracy.html#fp16-on-mi200
+        # As we leave the default value (True) for allow_fp16_reduced_precision_reduction, the tests failed when running in half-precision with smaller models (560m)
+        # Please see: https://pytorch.org/docs/stable/notes/cuda.html#reduced-precision-reduction-in-fp16-gemms
+        # This discrepancy is observed only when using small models and seems to be stable for larger models.
+        # Our conclusion is that these operations are flaky for small inputs but seems to be stable for larger inputs (for the functions `baddmm` and `bmm`), and therefore for larger models.
+
+        # Here is a summary of an ablation study of our observations
+        # EXPECTED_OUTPUT = "I enjoy walking with my cute dog, and I love to watch the kids play. I am a very active person, and I am a very good listener. I am a very good person, and I am a very good person. I am a"
+        # 560m + allow_fp16_reduced_precision_reduction = False  + torch.bmm  ==> PASS
+        # 560m + allow_fp16_reduced_precision_reduction = False  + torch.baddm  ==> PASS
+        # 560m + allow_fp16_reduced_precision_reduction = True  + torch.baddm  ==> PASS
+        # 560m + allow_fp16_reduced_precision_reduction = True  + torch.bmm  ==> FAIL
+
+        # EXPECTED_OUTPUT = "I enjoy walking with my cute dog, but I also enjoy hiking, biking, and swimming. I love to cook and bake. I love to cook and bake. I love to cook and bake. I love to cook and bake. I love"
+        # >=1b1 + allow_fp16_reduced_precision_reduction = True  + torch.baddm  ==> PASS  (for use_cache=True and use_cache=False)
+        # >=1b1 + allow_fp16_reduced_precision_reduction = True  + torch.bmm  ==> PASS
+        # >=1b1 + allow_fp16_reduced_precision_reduction = False  + torch.bmm  ==> PASS
+
+        path_560m = "bigscience/bloom-560m"
+        model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device)
+        model = model.eval()
+        tokenizer = BloomTokenizerFast.from_pretrained(path_560m)
+
+        input_sentence = "I enjoy walking with my cute dog"
+        # This output has been obtained using fp32 model on the huggingface DGX workstation - NVIDIA A100 GPU
+        EXPECTED_OUTPUT = (
+            "I enjoy walking with my cute dog, and I love to watch the kids play with the kids. I am a very "
+            "active person, and I enjoy working out, and I am a very active person. I am a very active person, and I"
+        )
+
+        input_ids = tokenizer.encode(input_sentence, return_tensors="pt")
+        greedy_output = model.generate(input_ids.to(torch_device), max_length=50)
+
+        self.assertEqual(tokenizer.decode(greedy_output[0], skip_special_tokens=True), EXPECTED_OUTPUT)
+
+    @slow
+    @require_torch_accelerator
+    def test_batch_generation(self):
+        path_560m = "bigscience/bloom-560m"
+        model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device)
+        model = model.eval()
+        tokenizer = BloomTokenizerFast.from_pretrained(path_560m, padding_side="left")
+
+        input_sentence = ["I enjoy walking with my cute dog", "I enjoy walking with my cute dog"]
+
+        inputs = tokenizer.batch_encode_plus(input_sentence, return_tensors="pt", padding=True)
+        input_ids = inputs["input_ids"].to(torch_device)
+        attention_mask = inputs["attention_mask"]
+        greedy_output = model.generate(input_ids, attention_mask=attention_mask, max_length=50, do_sample=False)
+
+        self.assertEqual(
+            tokenizer.decode(greedy_output[0], skip_special_tokens=True),
+            tokenizer.decode(greedy_output[1], skip_special_tokens=True),
+        )
+
+    @slow
+    @require_torch_accelerator
+    def test_batch_generation_padding(self):
+        path_560m = "bigscience/bloom-560m"
+        model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device)
+        model = model.eval()
+        tokenizer = BloomTokenizerFast.from_pretrained(path_560m, padding_side="left")
+
+        input_sentence = ["I enjoy walking with my cute dog", "Hello my name is"]
+        input_sentence_without_pad = "Hello my name is"
+
+        input_ids = tokenizer.batch_encode_plus(input_sentence, return_tensors="pt", padding=True)
+        input_ids_without_pad = tokenizer.encode(input_sentence_without_pad, return_tensors="pt")
+
+        input_ids, attention_mask = input_ids["input_ids"].to(torch_device), input_ids["attention_mask"]
+        greedy_output = model.generate(input_ids, attention_mask=attention_mask, max_length=50, do_sample=False)
+        greedy_output_without_pad = model.generate(
+            input_ids_without_pad.to(torch_device), max_length=50, do_sample=False
+        )
+
+        # test token values
+        self.assertEqual(greedy_output[-1, 3:].tolist(), greedy_output_without_pad[0, :-3].tolist())
+
+        # test reconstructions
+        self.assertEqual(
+            tokenizer.decode(greedy_output[-1, 3:], skip_special_tokens=True),
+            tokenizer.decode(greedy_output_without_pad[0, :-3], skip_special_tokens=True),
+        )
+
+    @slow
+    @require_torch_accelerator
+    def test_batch_generated_text(self):
+        path_560m = "bigscience/bloom-560m"
+
+        model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device)
+        model = model.eval()
+        tokenizer = BloomTokenizerFast.from_pretrained(path_560m, padding_side="left")
+
+        input_sentences = [
+            "Hello what is",
+            "Running a quick test with the",
+        ]
+        inputs = tokenizer(input_sentences, return_tensors="pt", padding=True, truncation=True)
+        generated_ids = model.generate(
+            inputs["input_ids"].to(torch_device), attention_mask=inputs["attention_mask"], max_length=20
+        )
+        generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+
+        # these generations match those of the PyTorch model
+        EXPECTED_GENERATIONS = [
+            "Hello what is the best way to get the data from the server? I have tried",
+            "Running a quick test with the following command:\nsudo apt-get install python3\nsudo apt-get install python2",
+        ]
+
+        self.assertListEqual(generated_text, EXPECTED_GENERATIONS)
+
+    @unittest.skip("Bloom needs a 2D attention for alibi")
+    def test_custom_4d_attention_mask(self):
+        pass
+
+
+@require_torch
+class BloomEmbeddingTest(unittest.TestCase):
+    """
+    The goal here is to compare the embeddings generated by the model trained
+    using Megatron-LM with the one from the transformers library, with a small GPT2-like model
+    to ensure that the conversion from Megatron-LM to transformers has been done successfully.
+    The script compares the logits of the embedding layer and the transformer layers.
+
+    WARNING: It is expected that these logits will not have exactly the same statistics when running
+    the code on CPU or GPU. For more info, please visit:
+      - https://github.com/pytorch/pytorch/issues/76052#issuecomment-1103193548
+      - https://discuss.pytorch.org/t/reproducibility-issue-between-intel-and-amd-cpus/144779/9
+
+
+    You need to install tokenizers following this readme:
+        - https://huggingface.co/bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
+
+    Tokenizer used during training:
+        - https://huggingface.co/bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
+
+    # TODO change the script (or just add skip) when building the env with tokenizers 0.12.0
+    """
+
+    def setUp(self):
+        super().setUp()
+        self.path_bigscience_model = "bigscience/bigscience-small-testing"
+
+    @require_torch
+    def test_embeddings(self):
+        # The config in this checkpoint has `bfloat16` as `dtype` -> model in `bfloat16`
+        model = BloomForCausalLM.from_pretrained(self.path_bigscience_model, dtype="auto")
+        model.eval()
+
+        EMBEDDINGS_DS_BEFORE_LN_BF_16_MEAN = {
+            3478: 0.0002307891845703125,
+            368: -0.000568389892578125,
+            109586: -0.0003910064697265625,
+            35433: -0.000194549560546875,
+            2: 0.0004138946533203125,
+            77: 0.000659942626953125,
+            132619: -0.00031280517578125,
+            2175: 0.000457763671875,
+            23714: 0.000263214111328125,
+            73173: -0.000286102294921875,
+            144252: 0.00052642822265625,
+        }
+        EMBEDDINGS_DS_BEFORE_LN_BF_16_MIN = {
+            3478: -0.00921630859375,
+            368: -0.010009765625,
+            109586: -0.01031494140625,
+            35433: -0.01177978515625,
+            2: -0.0074462890625,
+            77: -0.00848388671875,
+            132619: -0.009521484375,
+            2175: -0.0074462890625,
+            23714: -0.0145263671875,
+            73173: -0.007415771484375,
+            144252: -0.01007080078125,
+        }
+        EMBEDDINGS_DS_BEFORE_LN_BF_16_MAX = {
+            3478: 0.0128173828125,
+            368: 0.01214599609375,
+            109586: 0.0111083984375,
+            35433: 0.01019287109375,
+            2: 0.0157470703125,
+            77: 0.0174560546875,
+            132619: 0.0078125,
+            2175: 0.0113525390625,
+            23714: 0.0146484375,
+            73173: 0.01116943359375,
+            144252: 0.01141357421875,
+        }
+        EMBEDDINGS_DS_BEFORE_LN_BF_16_SUM = {"value": 0.08203125}
+
+        EMBEDDINGS_DS_BEFORE_LN_F_16_MEAN = {
+            132619: -0.00031256675720214844,
+            3478: 0.00023090839385986328,
+            368: -0.0005702972412109375,
+            109586: -0.00039124488830566406,
+            35433: -0.000194549560546875,
+            2: 0.0004146099090576172,
+            2175: 0.0004572868347167969,
+            23714: 0.00026416778564453125,
+            73173: -0.0002865791320800781,
+            144252: 0.0005254745483398438,
+            77: 0.0006618499755859375,
+        }
+        EMBEDDINGS_DS_BEFORE_LN_F_16_MIN = {
+            3478: -0.00921630859375,
+            368: -0.010009765625,
+            109586: -0.01031494140625,
+            35433: -0.01177978515625,
+            2: -0.0074462890625,
+            77: -0.00848388671875,
+            132619: -0.009521484375,
+            2175: -0.0074462890625,
+            23714: -0.0145263671875,
+            73173: -0.007415771484375,
+            144252: -0.01007080078125,
+        }
+        EMBEDDINGS_DS_BEFORE_LN_F_16_MAX = {
+            3478: 0.0128173828125,
+            368: 0.01214599609375,
+            109586: 0.0111083984375,
+            35433: 0.01019287109375,
+            2: 0.0157470703125,
+            77: 0.0174560546875,
+            132619: 0.0078125,
+            2175: 0.0113525390625,
+            23714: 0.0146484375,
+            73173: 0.01116943359375,
+            144252: 0.01141357421875,
+        }
+        EMBEDDINGS_DS_BEFORE_LN_F_16_SUM = {"value": 0.0821533203125}
+
+        EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN = {
+            132619: -0.00031267106533050537,
+            3478: 0.00023087859153747559,
+            368: -0.0005701072514057159,
+            109586: -0.0003911703824996948,
+            35433: -0.0001944899559020996,
+            2: 0.0004146844148635864,
+            2175: 0.00045740045607089996,
+            23714: 0.0002641640603542328,
+            73173: -0.0002864748239517212,
+            144252: 0.0005256589502096176,
+            77: 0.0006617321632802486,
+        }
+        EMBEDDINGS_DS_BEFORE_LN_F_32_MIN = {
+            3478: -0.00921630859375,
+            368: -0.010009765625,
+            109586: -0.01031494140625,
+            35433: -0.01177978515625,
+            2: -0.0074462890625,
+            77: -0.00848388671875,
+            132619: -0.009521484375,
+            2175: -0.0074462890625,
+            23714: -0.0145263671875,
+            73173: -0.007415771484375,
+            144252: -0.01007080078125,
+        }
+        EMBEDDINGS_DS_BEFORE_LN_F_32_MAX = {
+            3478: 0.0128173828125,
+            368: 0.01214599609375,
+            109586: 0.0111083984375,
+            35433: 0.01019287109375,
+            2: 0.0157470703125,
+            77: 0.0174560546875,
+            132619: 0.0078125,
+            2175: 0.0113525390625,
+            23714: 0.0146484375,
+            73173: 0.01116943359375,
+            144252: 0.01141357421875,
+        }
+        EMBEDDINGS_DS_BEFORE_LN_F_32_SUM = {"value": 0.08217757940292358}
+
+        TEST_EMBEDDINGS = {
+            "torch.bfloat16": {
+                "mean": EMBEDDINGS_DS_BEFORE_LN_BF_16_MEAN,
+                "max": EMBEDDINGS_DS_BEFORE_LN_BF_16_MAX,
+                "min": EMBEDDINGS_DS_BEFORE_LN_BF_16_MIN,
+                "sum": EMBEDDINGS_DS_BEFORE_LN_BF_16_SUM,
+            },
+            "torch.float32": {
+                "mean": EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN,
+                "max": EMBEDDINGS_DS_BEFORE_LN_F_32_MAX,
+                "min": EMBEDDINGS_DS_BEFORE_LN_F_32_MIN,
+                "sum": EMBEDDINGS_DS_BEFORE_LN_F_32_SUM,
+            },
+            "torch.float": {
+                "mean": EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN,
+                "max": EMBEDDINGS_DS_BEFORE_LN_F_32_MAX,
+                "min": EMBEDDINGS_DS_BEFORE_LN_F_32_MIN,
+                "sum": EMBEDDINGS_DS_BEFORE_LN_F_32_SUM,
+            },
+            "torch.float16": {
+                "mean": EMBEDDINGS_DS_BEFORE_LN_F_16_MEAN,
+                "max": EMBEDDINGS_DS_BEFORE_LN_F_16_MAX,
+                "min": EMBEDDINGS_DS_BEFORE_LN_F_16_MIN,
+                "sum": EMBEDDINGS_DS_BEFORE_LN_F_16_SUM,
+            },
+        }
+
+        EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478]  # fmt: skip
+
+        EMBEDDINGS_DS_AFTER_LN_MEAN = {
+            3478: -6.580352783203125e-05,
+            368: 0.0001316070556640625,
+            109586: -0.00030517578125,
+            35433: 4.00543212890625e-05,
+            2: -7.2479248046875e-05,
+            77: -8.96453857421875e-05,
+            132619: 0.0001583099365234375,
+            2175: 2.1219253540039062e-05,
+            23714: -0.000247955322265625,
+            73173: -0.00021839141845703125,
+            144252: -0.0001430511474609375,
+        }
+        EMBEDDINGS_DS_AFTER_LN_MIN = {
+            3478: -1.6953125,
+            368: -1.6875,
+            109586: -1.6875,
+            35433: -2.125,
+            2: -1.390625,
+            77: -1.5390625,
+            132619: -1.875,
+            2175: -1.4609375,
+            23714: -2.296875,
+            73173: -1.3515625,
+            144252: -1.78125,
+        }
+        EMBEDDINGS_DS_AFTER_LN_MAX = {
+            3478: 2.265625,
+            368: 2.28125,
+            109586: 1.953125,
+            35433: 1.90625,
+            2: 2.703125,
+            77: 2.828125,
+            132619: 1.65625,
+            2175: 2.015625,
+            23714: 2.234375,
+            73173: 2.171875,
+            144252: 1.828125,
+        }
+
+        EMBEDDINGS_DS_AFTER_LN = {
+            "mean": EMBEDDINGS_DS_AFTER_LN_MEAN,
+            "min": EMBEDDINGS_DS_AFTER_LN_MIN,
+            "max": EMBEDDINGS_DS_AFTER_LN_MAX,
+        }
+
+        tensor_ids = torch.LongTensor([EXAMPLE_IDS])
+        with torch.no_grad():
+            embeddings = model.transformer.word_embeddings(tensor_ids)
+            embeddings_ln = model.transformer.word_embeddings_layernorm(embeddings)
+        # first check the embeddings before LN
+        output_dict = {"min": {}, "max": {}, "mean": {}, "sum": {"value": embeddings.sum().item()}}
+        for i, idx in enumerate(EXAMPLE_IDS):
+            output_dict["min"][idx] = embeddings.min(dim=-1).values[0][i].item()
+            output_dict["max"][idx] = embeddings.max(dim=-1).values[0][i].item()
+            output_dict["mean"][idx] = embeddings.mean(dim=-1)[0][i].item()
+
+        for key in TEST_EMBEDDINGS[str(model.dtype)]:
+            self.assertDictEqual(TEST_EMBEDDINGS[str(model.dtype)][key], output_dict[key])
+
+        output_dict_norm = {"min": {}, "max": {}, "mean": {}}
+        for i, idx in enumerate(EXAMPLE_IDS):
+            output_dict_norm["min"][idx] = embeddings_ln.min(dim=-1).values[0][i].item()
+            output_dict_norm["max"][idx] = embeddings_ln.max(dim=-1).values[0][i].item()
+            output_dict_norm["mean"][idx] = embeddings_ln.mean(dim=-1)[0][i].item()
+
+        # This test does not pass when places = 2
+        for i, key in enumerate(output_dict_norm.keys()):
+            for j, idx in enumerate(output_dict[key].keys()):
+                self.assertAlmostEqual(EMBEDDINGS_DS_AFTER_LN[key][idx], output_dict_norm[key][idx], places=1)
+
+    @require_torch
+    def test_hidden_states_transformers(self):
+        model = BloomModel.from_pretrained(self.path_bigscience_model, use_cache=False, dtype="auto").to(torch_device)
+        model.eval()
+
+        EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478]  # fmt: skip
+
+        MEAN_VALUE_LAST_LM = -4.3392181396484375e-05
+        MIN_MAX_DICT = {"min": -2.0625, "max": 2.75}
+        tensor_ids = torch.LongTensor([EXAMPLE_IDS])
+
+        with torch.no_grad():
+            logits = model(tensor_ids.to(torch_device))
+        output_dict = {
+            "min": logits.last_hidden_state.min(dim=-1).values[0][0].item(),
+            "max": logits.last_hidden_state.max(dim=-1).values[0][0].item(),
+        }
+
+        if torch_device == "cuda":
+            self.assertAlmostEqual(MEAN_VALUE_LAST_LM, logits.last_hidden_state.mean().item(), places=4)
+        else:
+            self.assertAlmostEqual(MEAN_VALUE_LAST_LM, logits.last_hidden_state.mean().item(), places=3)
+
+        self.assertDictEqual(MIN_MAX_DICT, output_dict)
+
+    @require_torch
+    def test_logits(self):
+        model = BloomForCausalLM.from_pretrained(self.path_bigscience_model, use_cache=False, dtype="auto").to(
+            torch_device
+        )  # load in bf16
+        model.eval()
+
+        EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478]  # fmt: skip
+
+        MEAN_LOGITS_GPU_1 = -1.823902130126953e-05
+        MEAN_LOGITS_GPU_2 = 1.9431114196777344e-05
+
+        tensor_ids = torch.LongTensor([EXAMPLE_IDS]).to(torch_device)
+        with torch.no_grad():
+            output = model(tensor_ids).logits
+
+        output_gpu_1, output_gpu_2 = output.split(125440, dim=-1)
+        self.assertAlmostEqual(output_gpu_1.mean().item(), MEAN_LOGITS_GPU_1, places=6)
+        self.assertAlmostEqual(output_gpu_2.mean().item(), MEAN_LOGITS_GPU_2, places=6)

transformers/tests/models/bloom/test_tokenization_bloom.py

@@ -0,0 +1,167 @@
+# Copyright 2022 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.
+
+import copy
+import unittest
+
+from datasets import load_dataset
+
+from transformers import BloomTokenizerFast
+from transformers.testing_utils import require_jinja, require_tokenizers
+
+from ...test_tokenization_common import TokenizerTesterMixin
+
+
+@require_tokenizers
+class BloomTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+    from_pretrained_id = "bigscience/tokenizer"
+    slow_tokenizer_class = None
+    rust_tokenizer_class = BloomTokenizerFast
+    tokenizer_class = BloomTokenizerFast
+    test_rust_tokenizer = True
+    test_slow_tokenizer = False
+    from_pretrained_vocab_key = "tokenizer_file"
+    special_tokens_map = {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        tokenizer = BloomTokenizerFast.from_pretrained("bigscience/tokenizer")
+        tokenizer.save_pretrained(cls.tmpdirname)
+
+    @classmethod
+    def get_rust_tokenizer(cls, pretrained_name=None, **kwargs):
+        _kwargs = copy.deepcopy(cls.special_tokens_map)
+        _kwargs.update(kwargs)
+        kwargs = _kwargs
+        pretrained_name = pretrained_name or cls.tmpdirname
+        return BloomTokenizerFast.from_pretrained(pretrained_name, **kwargs)
+
+    @unittest.skip(reason="This needs a slow tokenizer. Bloom does not have one!")
+    def test_encode_decode_with_spaces(self):
+        return
+
+    def test_encodings_from_sample_data(self):
+        """
+        Assert that the created tokens are the same than the hard-coded ones
+        """
+        tokenizer = self.get_rust_tokenizer()
+
+        INPUT_SENTENCES = ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
+        TARGET_TOKENS = [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]
+
+        computed_tokens = tokenizer.batch_encode_plus(INPUT_SENTENCES)["input_ids"]
+        self.assertListEqual(TARGET_TOKENS, computed_tokens)
+
+        decoded_tokens = tokenizer.batch_decode(computed_tokens)
+        self.assertListEqual(decoded_tokens, INPUT_SENTENCES)
+
+    def test_padding(self, max_length=6):
+        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
+            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
+                tokenizer_r = self.get_rust_tokenizer(pretrained_name, **kwargs)
+                # tokenizer_r.pad_token = None # Hotfixing padding = None
+                # Simple input
+                s = "This is a simple input"
+                s2 = ["This is a simple input 1", "This is a simple input 2"]
+                p = ("This is a simple input", "This is a pair")
+                p2 = [
+                    ("This is a simple input 1", "This is a simple input 2"),
+                    ("This is a simple pair 1", "This is a simple pair 2"),
+                ]
+
+                # Simple input tests
+                try:
+                    tokenizer_r.encode(s, max_length=max_length)
+                    tokenizer_r.encode_plus(s, max_length=max_length)
+
+                    tokenizer_r.batch_encode_plus(s2, max_length=max_length)
+                    tokenizer_r.encode(p, max_length=max_length)
+                    tokenizer_r.batch_encode_plus(p2, max_length=max_length)
+                except ValueError:
+                    self.fail("Bloom Tokenizer should be able to deal with padding")
+
+                tokenizer_r.pad_token = None  # Hotfixing padding = None
+                self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
+
+                # Simple input
+                self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
+
+                # Simple input
+                self.assertRaises(
+                    ValueError,
+                    tokenizer_r.batch_encode_plus,
+                    s2,
+                    max_length=max_length,
+                    padding="max_length",
+                )
+
+                # Pair input
+                self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
+
+                # Pair input
+                self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
+
+                # Pair input
+                self.assertRaises(
+                    ValueError,
+                    tokenizer_r.batch_encode_plus,
+                    p2,
+                    max_length=max_length,
+                    padding="max_length",
+                )
+
+    def test_encodings_from_xnli_dataset(self):
+        """
+        Tests the tokenizer downloaded from here:
+            - https://huggingface.co/bigscience/tokenizer/
+        """
+        tokenizer = self.get_rust_tokenizer()
+        ds = load_dataset("facebook/xnli", "all_languages", split="test", streaming=True)
+
+        sample_data = next(iter(ds))["premise"]  # pick up one data
+        input_text = list(sample_data.values())
+
+        output_tokens = list(map(tokenizer.encode, input_text))
+        predicted_text = [tokenizer.decode(x, clean_up_tokenization_spaces=False) for x in output_tokens]
+        self.assertListEqual(predicted_text, input_text)
+
+    @require_jinja
+    def test_tokenization_for_chat(self):
+        tokenizer = self.get_rust_tokenizer()
+        tokenizer.chat_template = "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}"
+        test_chats = [
+            [{"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}],
+            [
+                {"role": "system", "content": "You are a helpful chatbot."},
+                {"role": "user", "content": "Hello!"},
+                {"role": "assistant", "content": "Nice to meet you."},
+            ],
+            [{"role": "assistant", "content": "Nice to meet you."}, {"role": "user", "content": "Hello!"}],
+        ]
+        tokenized_chats = [tokenizer.apply_chat_template(test_chat) for test_chat in test_chats]
+        expected_tokens = [
+            [5448, 1306, 267, 66799, 44799, 37143, 17, 2, 59414, 4, 2],
+            [5448, 1306, 267, 66799, 44799, 37143, 17, 2, 59414, 4, 2, 229126, 427, 11890, 1152, 17, 2],
+            [229126, 427, 11890, 1152, 17, 2, 59414, 4, 2],
+        ]
+        for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens):
+            self.assertListEqual(tokenized_chat, expected_tokens)
+
+    def test_add_prefix_space_fast(self):
+        tokenizer_w_prefix = self.get_rust_tokenizer(add_prefix_space=True)
+        tokenizer_wo_prefix = self.get_rust_tokenizer(add_prefix_space=False)
+        tokens_w_prefix = tokenizer_w_prefix.tokenize("Hey")
+        tokens_wo_prefix = tokenizer_wo_prefix.tokenize("Hey")
+        self.assertNotEqual(tokens_w_prefix, tokens_wo_prefix)