Add bloom flax (#25094)
* First commit * step 1 working * add alibi * placeholder for `scan` * add matrix mult alibi * beta scaling factor for bmm * working v1 - simple forward pass * move layer_number from attribute to arg in call * partial functioning scan * hacky working scan * add more modifs * add test * update scan for new kwarg order * fix position_ids problem * fix bug in attention layer * small fix - do the alibi broadcasting only once * prelim refactor * finish refactor * alibi shifting * incorporate dropout_add to attention module * make style * make padding work again * update * remove bogus file * up * get generation to work * clean code a bit * added small tests * adding albii test * make CI tests pass: - change init weight - add correct tuple for output attention - add scan test - make CI tests work * fix few nits * fix nit onnx * fix onnx nit * add missing dtype args to nn.Modules * remove debugging statements * fix scan generate * Update modeling_flax_bloom.py * Update test_modeling_flax_bloom.py * Update test_modeling_flax_bloom.py * Update test_modeling_flax_bloom.py * fix small test issue + make style * clean up * Update tests/models/bloom/test_modeling_flax_bloom.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * fix function name * small fix test * forward contrib credits from PR17761 * Fix failing test * fix small typo documentation * fix non passing test - remove device from build alibi * refactor call - refactor `FlaxBloomBlockCollection` module * make style * upcast to fp32 * cleaner way to upcast * remove unused args * remove layer number * fix scan test * make style * fix i4 casting * fix slow test * Update src/transformers/models/bloom/modeling_flax_bloom.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * remove `layer_past` * refactor a bit * fix `scan` slow test * remove useless import * major changes - remove unused code - refactor a bit - revert import `torch` * major refactoring - change build alibi * remove scan * fix tests * make style * clean-up alibi * add integration tests * up * fix batch norm conversion * style * style * update pt-fx cross tests * update copyright * Update src/transformers/modeling_flax_pytorch_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * per-weight check * style * line formats --------- Co-authored-by: younesbelkada <younesbelkada@gmail.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: haileyschoelkopf <haileyschoelkopf@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Sanchit Gandhi
251
tests/models/bloom/test_modeling_flax_bloom.py
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251
tests/models/bloom/test_modeling_flax_bloom.py
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# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from transformers import BloomConfig, BloomTokenizerFast, is_flax_available
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from transformers.testing_utils import require_flax, slow
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from ...generation.test_flax_utils import FlaxGenerationTesterMixin
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from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
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if is_flax_available():
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import os
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# The slow tests are often failing with OOM error on GPU
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# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
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# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
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os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"
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import jax.numpy as jnp
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from transformers import FlaxBloomForCausalLM, FlaxBloomModel
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def prepare_bloom_inputs_dict(config, input_ids, attention_mask=None):
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if attention_mask is None:
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attention_mask = np.where(input_ids != config.pad_token_id, 1, 0)
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return {"input_ids": input_ids, "attention_mask": attention_mask}
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@require_flax
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class FlaxBloomModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_labels=False,
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vocab_size=99,
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hidden_size=16,
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n_layer=2,
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n_head=4,
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hidden_act="gelu",
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hidden_dropout=0.1,
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attention_probs_dropout_prob=0.1,
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eos_token_id=2,
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pad_token_id=1,
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bos_token_id=0,
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initializer_range=0.02,
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apply_residual_connection_post_layernorm=False,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = n_layer
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self.num_attention_heads = n_head
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self.hidden_act = hidden_act
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self.hidden_dropout = hidden_dropout
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.initializer_range = initializer_range
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self.is_encoder_decoder = False
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self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
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def prepare_config_and_inputs(self):
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input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size)
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input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1)
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config = BloomConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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n_layer=self.num_hidden_layers,
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n_head=self.num_attention_heads,
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hidden_dropout=self.hidden_dropout,
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attention_dropout=self.attention_probs_dropout_prob,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.bos_token_id,
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pad_token_id=self.pad_token_id,
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is_encoder_decoder=False,
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use_cache=False,
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)
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inputs_dict = prepare_bloom_inputs_dict(config, input_ids)
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return config, inputs_dict
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def prepare_config_and_inputs_for_common(self):
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config, inputs_dict = self.prepare_config_and_inputs()
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return config, inputs_dict
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def check_use_cache_forward(self, model_class_name, config, inputs_dict):
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max_length = 20
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model = model_class_name(config)
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input_ids = inputs_dict["input_ids"]
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attention_mask = jnp.ones((input_ids.shape[0], max_length), dtype="i4")
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past_key_values = model.init_cache(input_ids.shape[0], max_length)
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outputs_cache = model(
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input_ids[:, :-1],
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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)
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outputs_cache_next = model(
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input_ids[:, -1:],
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attention_mask=attention_mask,
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past_key_values=outputs_cache.past_key_values,
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)
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outputs = model(input_ids)
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diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
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self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
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def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict):
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max_length = 20
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model = model_class_name(config)
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input_ids, attention_mask = (
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inputs_dict["input_ids"],
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inputs_dict["attention_mask"],
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)
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attention_mask_cache = jnp.concatenate(
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[
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attention_mask,
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jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])),
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],
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axis=-1,
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)
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past_key_values = model.init_cache(input_ids.shape[0], max_length)
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outputs_cache = model(
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input_ids[:, :-1],
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attention_mask=attention_mask_cache,
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past_key_values=past_key_values,
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)
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outputs_cache_next = model(
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input_ids[:, -1:],
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past_key_values=outputs_cache.past_key_values,
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attention_mask=attention_mask_cache,
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)
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outputs = model(input_ids, attention_mask=attention_mask)
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diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
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self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
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@require_flax
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class FlaxBloomModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin):
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all_model_classes = (FlaxBloomModel, FlaxBloomForCausalLM) if is_flax_available() else ()
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all_generative_model_classes = () if is_flax_available() else ()
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def setUp(self):
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self.model_tester = FlaxBloomModelTester(self)
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def test_use_cache_forward(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs()
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for model_class in self.all_model_classes:
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self.model_tester.check_use_cache_forward(model_class, config, inputs_dict)
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def test_use_cache_forward_with_attn_mask(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs()
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for model_class in self.all_model_classes:
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self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict)
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@slow
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def test_model_from_pretrained(self):
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for model_class_name in self.all_model_classes:
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model = model_class_name.from_pretrained("bigscience/bloom-560m")
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input_ids = np.ones((1, 1)) * model.config.eos_token_id
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outputs = model(input_ids)
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self.assertIsNotNone(outputs)
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@slow
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@require_flax
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class FlaxBloomGenerationTest(unittest.TestCase):
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all_model_classes = (FlaxBloomForCausalLM) if is_flax_available() else ()
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all_generative_model_classes = () if is_flax_available() else ()
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def setUp(self):
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self.model_id = "bigscience/bloom-560m"
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self.tokenizer = BloomTokenizerFast.from_pretrained(self.model_id, padding_side="left")
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self.model_tester = FlaxBloomModelTester(self)
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self.model = FlaxBloomForCausalLM.from_pretrained(self.model_id, from_pt=True, revision="gs555750")
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def test_model_batched_gen(self):
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# tests if the model outputs the same generation for the same batched input
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input_sentences = [
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"Hello there is this string is definitely longer I believe that",
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"Hello there is this string is definitely longer I believe that",
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]
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inputs = self.tokenizer(input_sentences, return_tensors="np", padding=True, truncation=True)
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sequences_fx = self.model.generate(**inputs, max_length=20).sequences
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self.assertEqual(sequences_fx[0].tolist(), sequences_fx[1].tolist())
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def test_model_batched_padding_left(self):
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# tests if the model outputs the same generation for an input that is part of a batch
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# and a single input
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input_sentences_batch = [
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"Hello there is this string is definitely longer I believe that",
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"Hi I want to order",
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]
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inputs = self.tokenizer(input_sentences_batch, return_tensors="np", padding=True, truncation=True)
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sequences_fx_batch = self.model.generate(**inputs, max_length=20).sequences
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input_sentence_simple = "Hi I want to order"
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inputs_simple = self.tokenizer(input_sentence_simple, return_tensors="np")
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sequences_fx_simple = self.model.generate(**inputs_simple, max_length=20).sequences
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self.assertEqual(sequences_fx_batch[1][6:].tolist(), sequences_fx_simple[0][:-6].tolist())
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def test_batch_generated_text(self):
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input_sentences = [
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"Hello what is",
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"Running a quick test with the",
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]
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inputs = self.tokenizer(input_sentences, return_tensors="np", padding=True, truncation=True)
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generated_ids = self.model.generate(**inputs, max_length=20).sequences
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generated_text = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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# these generations match those of the PyTorch model, ensuring correctness
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EXPECTED_GENERATIONS = [
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"Hello what is the best way to get the data from the server? I have tried",
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"Running a quick test with the following command:\nsudo apt-get install python3\nsudo apt-get install python2",
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]
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self.assertListEqual(generated_text, EXPECTED_GENERATIONS)
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