BLOOM (#17474)

* adding template

* update model

* model update

* update conf for debug model

* update conversion

* update conversion script

* update conversion script

* fix missing keys check

* add tests to test the tokenizer in the local machine

* Change variable name

* add tests on xnli dataset

* add more description

* add descriptions + clearer code

* clearer code

* adding new tests + skipping few tests because of env problems

* change comment

* add dtype on the configuration

* add test embeddings

* add hardcoded test

* fix dtype issue

* adding torch.float16 to config

* adding more metrics (min, max, mean)

* add sum

* now the test passes with almost equal

* add files for conversion - test passes on cpu  gpu

* add final changes

* cleaning code

* add new args in the docstring

* fix one liner function

* remove macros

* remove forward attention

* clean up init funtion

* add comments on the issue

* rm scale mask softmax

* do make style

* fix dtype in init

* fixing for loop on att probs

* fix style with black

* fix style + doc error

* fix and debug CI errors (docs + style)

* some updates

- change new operations
- finally add scaled softmax
- added new args in the config

* make use cache working

* add changes

- save sharded models
- final changes on the modeling script

* add changes

- comment on alibi
- add TODO on seq length

* test commit

- added a text to test the commit
Co-authored-by: thomasw21 <24695242+thomasw21@users.noreply.github.com>

* final changes

- attention mask change
- generation works on BS176b
Co-authored-by: thomasw21 <24695242+thomasw21@users.noreply.github.com>

* changes - model + conversion

* move to correct dir

* put ,

* fex fixes

* fix tokenizer autodoc

* fix minor CI issues

* fix minor CI issues

* fix minor CI issues

* fix style issue

* fix minor import issues

* fix few issues

* remove def main on the test

* add require torch

* replace decorator with 'with'

* fix style

* change to bloom

* add quick fix tokenizer

* fix tokenizer file

* fix tokenizer

- merge tests
- small fixes

* fix import issue

* add bloom to readme

* fix consistency

* Update docs/source/en/model_doc/bloom.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Apply suggestions from code review

fix comment issues on file headers
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix doc issue

* small fix - modeling test

* some changes

- refactor some code
- taking into account reviews
- more tests should pass
- removed pruning tests

* remove useless division

* more tests should pass

* more tests should pass

* more tests should pass

* let's try this one

-add alibi offset
- remove all permutes to make the grad operations work
- finger crossed

* refactor

- refactor code
- style changes
- add new threshold for test

* major changes

- change BLOOM to Bloom
- add quick doc on bloom.mdx
- move embeddings test on modeling test

* modify readme

* small fixes

* small fix

- better threshold for a test

* remove old test file from fetcher

* fix small typo

* major change

- change BloomLMHead to BloomForCausalLM

* remove onnx config

* major changes

- refactor the code
- remove asserts
- change tol for test

* make style

* small change

* adding a slow test + commenting old ones for now

* make style

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* make style

* fix duplicates

* cleaning comments on config

* clean a bit conversion file

* refacor a bit modeling file

* refactor tokenizer file

* fix tokenization test issue

* fix tokenization issue #2

* fix tokenization issue second try

* fix test issue

* make style + add suggestions

* change test fetcher

* try this one

- slow tests should pass
- finger crossed

* possible final changes

* make style

* try fix padding side issue

* fix side

* fix padding issue

* fix ko-readme

* fix config auto

* cleaning modeling file

* keep bloom in caps in ko

* update config docs

* remove pretraining_pp

* remove model parallel

* update config

- add correct config files

* fix duplicates

* fix fetcher

* fix refactor issue

- remove divide function

* try to remove alibi

* small fixes

- fix alibi
- remove seq length
- refactor a bit the code

* put correct values

- fix bos and eos token ids

* fix attention mask loop
Co-authored-by: thomasw21 <24695242+thomasw21@users.noreply.github.com>

* small fixes:

- remove skip bias add

* small fixes

- fix typo in readme
- fix typos in config

* small changes

- remove a test
- add reconstruction test
- change config

* small changes

- change Scaled Softmax to BloomScaledSoftmax

* small fixes

- fix alibi dtype

* major changes

- removing explicit dtype when loading modules
- fixing test args (torch_dtype=auto)
- add dosctring

* fix readmes

* major changes

- now bloom supports alibi shifting
- refactor a bit the code
- better test tolerance now

* refactor a bit

* refactor a bit

* put correct name on test

* change docstring

* small changes

- fix docstring modeling
- fix test tolerance

* fix small nit

- take dtype from tensors in the conversion script

* minor fix

- fix mdx issue

* minor fix

- change config docstring

* forward contrib credits from PR14084

* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* apply modifications
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* resolve softmax upcast

* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* Update src/transformers/models/bloom/modeling_bloom.py
Co-authored-by: Niklas Muennighoff <n.muennighoff@gmail.com>

* final changes modeling
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* Merge commit 'd156898f

'

* merge commit

* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* apply suggestions

Apply suggestions from Stas comments
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* Fix gradient checkpointing
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* add slow but exact

* add accelerate compatibility
Co-authored-by: Nicolas Patry <Narsil@users.noreply.github.com>

* forward contrib credits
Co-authored-by: thomasw21 <thomasw21@users.noreply.github.com>
Co-authored-by: sgugger <sgugger@users.noreply.github.com>
Co-authored-by: patrickvonplaten <patrickvonplaten@users.noreply.github.com>
Co-authored-by: Niklas Muennighoff <n.muennighoff@gmail.com>
Co-authored-by: LysandreJik <LysandreJik@users.noreply.github.com>

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix torch device on tests

* make style

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix nits

Co-authored-by: patrickvonplaten<patrickvonplaten@users.noreply.github.com>

* remove final nits

* fix doc

- add more details on the doc
- add links to checkpoints

* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/bloom/modeling_bloom.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* apply suggestions
Co-authored-by: sgugger <sgugger@users.noreply.github.com>

* put test torchscript to false

* Update src/transformers/models/bloom/modeling_bloom.py
Co-authored-by: justheuristic <justheuristic@gmail.com>

* fix alibi

- create alibi only once

* add small doc

* make quality

* replace torch.nn

* remove token type emb

* fix fused op + output bias

* add fused op

- now can control fused operation from config

* remove fused op

* make quality

* small changes

- remove unsed args on config
- removed bias gelu file
- make the model torchscriptable
- add torchscript slow tests

* Update src/transformers/models/bloom/modeling_bloom.py

* fix slow

* make style

* add accelerate support

* add bloom to deepspeed tests

* minor changes

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* minor change

* slow tests pass

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update docs/source/en/model_doc/bloom.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* minor changes:

- change docstring
- add link to paper
Co-authored-by: Thomwolf <thomwolf@gmail.com>
Co-authored-by: Thomas Wolf <thomas@huggingface.co>
Co-authored-by: thomasw21 <24695242+thomasw21@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: sIncerass <sheng.s@berkeley.edu>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Niklas Muennighoff <n.muennighoff@gmail.com>
Co-authored-by: Nicolas Patry <Narsil@users.noreply.github.com>
Co-authored-by: thomasw21 <thomasw21@users.noreply.github.com>
Co-authored-by: sgugger <sgugger@users.noreply.github.com>
Co-authored-by: patrickvonplaten <patrickvonplaten@users.noreply.github.com>
Co-authored-by: LysandreJik <LysandreJik@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: justheuristic <justheuristic@gmail.com>
Co-authored-by: Stas Bekman <stas@stason.org>

tests/models/bloom/test_modeling_bloom.py

+# coding=utf-8
+# 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_gpu, slow, torch_device
+
+from ...generation.test_generation_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST, BloomForCausalLM, 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=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_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_probs_dropout_prob = attention_probs_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])
+
+        config = self.get_config(gradient_checkpointing=gradient_checkpointing)
+
+        return (config, input_ids, input_mask)
+
+    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,
+            resid_pdrop=self.hidden_dropout_prob,
+            attn_pdrop=self.attention_probs_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,
+            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_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().keys():
+            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 = config_and_inputs
+
+        inputs_dict = {"input_ids": input_ids}
+
+        return config, inputs_dict
+
+
+@require_torch
+class BloomModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+
+    all_model_classes = (BloomModel, BloomForCausalLM) if is_torch_available() else ()
+    all_generative_model_classes = (BloomForCausalLM,) if is_torch_available() else ()
+    fx_compatible = False
+    test_missing_keys = False
+    test_pruning = False
+    test_torchscript = True  # torch.autograd functions seems to be not 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_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):
+        for model_name in BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = BloomModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+    @slow
+    @require_torch_gpu
+    def test_simple_generation(self):
+        path_350m = "bigscience/bloom-350m"
+        model = BloomForCausalLM.from_pretrained(path_350m, torch_dtype="auto", use_cache=True).cuda()
+        model = model.eval()
+        tokenizer = BloomTokenizerFast.from_pretrained(path_350m)
+
+        input_sentence = "I enjoy walking with my cute dog"
+        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"
+        )
+
+        input_ids = tokenizer.encode(input_sentence, return_tensors="pt")
+        greedy_output = model.generate(input_ids.cuda(), max_length=50)
+
+        self.assertEqual(tokenizer.decode(greedy_output[0], skip_special_tokens=True), EXPECTED_OUTPUT)
+
+    @slow
+    @require_torch_gpu
+    def test_batch_generation(self):
+        path_350m = "bigscience/bloom-350m"
+        model = BloomForCausalLM.from_pretrained(path_350m, torch_dtype="auto", use_cache=True).cuda()
+        model = model.eval()
+        tokenizer = BloomTokenizerFast.from_pretrained(path_350m, padding_side="left")
+
+        input_sentence = ["I enjoy walking with my cute dog", "I enjoy walking with my cute dog"]
+
+        input_ids = tokenizer.batch_encode_plus(input_sentence, return_tensors="pt", padding=True)
+        greedy_output = model.generate(
+            input_ids["input_ids"].cuda(), attention_mask=input_ids["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_gpu
+    def test_batch_generation_padd(self):
+        path_350m = "bigscience/bloom-350m"
+        model = BloomForCausalLM.from_pretrained(path_350m, torch_dtype="auto", use_cache=True).cuda()
+        model = model.eval()
+        tokenizer = BloomTokenizerFast.from_pretrained(path_350m, 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")
+
+        greedy_output = model.generate(
+            input_ids["input_ids"].cuda(), attention_mask=input_ids["attention_mask"], max_length=50, do_sample=False
+        )
+        greedy_output_without_pad = model.generate(input_ids_without_pad.cuda(), 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),
+        )
+
+
+@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):
+        model = BloomForCausalLM.from_pretrained(self.path_bigscience_model, torch_dtype="auto")  # load in fp32
+        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,
+            },
+        }
+
+        # fmt: off
+        EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478]
+        # fmt: on
+
+        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)].keys():
+            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):
+        cuda_available = torch.cuda.is_available()
+        model = BloomModel.from_pretrained(self.path_bigscience_model, use_cache=False, torch_dtype="auto").to(
+            torch_device
+        )
+        model.eval()
+
+        # fmt: off
+        EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478]
+        # fmt: on
+
+        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 cuda_available:
+            self.assertEqual(MEAN_VALUE_LAST_LM, logits.last_hidden_state.mean().item())
+        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):
+        cuda_available = torch.cuda.is_available()
+        model = BloomForCausalLM.from_pretrained(self.path_bigscience_model, use_cache=False, torch_dtype="auto").to(
+            torch_device
+        )  # load in bf16
+        model.eval()
+
+        # fmt: off
+        EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478]
+        # fmt: on
+
+        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)
+        if cuda_available:
+            self.assertEqual(output_gpu_1.mean().item(), MEAN_LOGITS_GPU_1)
+            self.assertEqual(output_gpu_2.mean().item(), MEAN_LOGITS_GPU_2)
+        else:
+            self.assertAlmostEqual(output_gpu_1.mean().item(), MEAN_LOGITS_GPU_1, places=6)  # 1e-06 precision!!
+            self.assertAlmostEqual(output_gpu_2.mean().item(), MEAN_LOGITS_GPU_2, places=6)

tests/models/bloom/test_tokenization_bloom.py

+# coding=utf-8
+# 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 unittest
+
+from datasets import load_dataset
+
+from transformers import BloomTokenizerFast
+from transformers.testing_utils import require_tokenizers
+
+from ...test_tokenization_common import TokenizerTesterMixin
+
+
+@require_tokenizers
+class BloomTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+
+    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>"}
+
+    def setUp(self):
+        super().setUp()
+        tokenizer = BloomTokenizerFast.from_pretrained("bigscience/tokenizer")
+        tokenizer.save_pretrained(self.tmpdirname)
+
+    def get_rust_tokenizer(self, **kwargs):
+        kwargs.update(self.special_tokens_map)
+        return BloomTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
+
+    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.rust_tokenizer_class.from_pretrained(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("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 = list(map(lambda x: tokenizer.decode(x, clean_up_tokenization_spaces=False), output_tokens))
+        self.assertListEqual(predicted_text, input_text)