transformers/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_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 (
        BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
        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_question_answering_model(self, config, input_ids, input_mask, *args):
        model = BloomForQuestionAnswering(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().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, 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 ()
    )

    all_generative_model_classes = (BloomForCausalLM,) 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 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_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)

    @unittest.skip("Bloom has a non-standard KV cache format.")
    def test_past_key_values_format(self):
        pass

    @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):
        # 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_gpu
    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_gpu
    def test_batch_generation_padd(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_gpu
    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)


@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 `torch_dtype` -> model in `bfloat16`
        model = BloomForCausalLM.from_pretrained(self.path_bigscience_model, torch_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,
            },
        }

        # 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.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):
        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.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)
        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)