[ gemma] Adds support for Gemma 💎 (#29167)
* inital commit * update * update conversion checkpoint * update conversion script * nits * some fixes * nits * merge * fix permute * nits * fix * nits * nits * nits * fix rope * fix both rope * nites * style * make sure flax works * fix flax init code * fix foward * nits * print flax generation out * current code * nits * SIIIIIIIIIIIIIIIIIII * update * add new tokenizer * correct fast tokenizer * fix conversion * more comments * fix modeling and conversion * nits and nits * nits testing * add some tokenization tests * add some edge cases * add slow tests and fix them * fixup * fix copies for modeling * fix copies * add 7B slow tests * fix * fix * fix tests * make tokenizer cis go green * styling * last tokenizer nits * update jax tests * fix flax for 7b * add jit testing 🤗 * cleanups * isolated nit, inv_freq for rotary_emb.inv_freq * propagate to jax * Apply suggestions from code review Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * adjust test * fix conversion script * change name * correct file names * update conversion script * Fix bos and eos token ids in the model configuration (#3) * update modelling * update conversion script * add static cache for gemma * fix sdpa generate * fix batched * multiple fixes * fix FA2 * final fix * Rename a few missing strings and filenames (#4) * merge with upstream main * fix copies * fix copies * fix fixup * fix fixup * fix * fix * final tests * fix fx gemma tests * fix fx bf16/fp16 tests * update slow fx tests * fx slow tests: one logits, one generation * move jit test standalone * Apply suggestions from code review * nits * tokenizer updates * more tokenization updates: custom GemmaSentencepieceExtrator * style * Update src/transformers/cache_utils.py * Update src/transformers/models/gemma/__init__.py * Update tests/models/gemma/test_modeling_flax_gemma.py * small nits * style * update tokenization test * fix the rotary embedding * with style * fix slow tests * WARNING this commit might be very important for precisions * Update tests/models/gemma/test_modeling_flax_gemma.py * Update src/transformers/models/gemma/configuration_gemma.py Co-authored-by: Lysandre Debut <hi@lysand.re> * Update src/transformers/models/gemma/modeling_flax_gemma.py Co-authored-by: Lysandre Debut <hi@lysand.re> * small nits here and there! * forgotten nit * remove on the fly computation of inv_freq * revert previous change, let's be safe and for now re-compute freq cis to make sure it's in float * Apply suggestions from code review Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update src/transformers/models/gemma/convert_gemma_weights_to_hf.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update src/transformers/models/gemma/convert_gemma_weights_to_hf.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_modeling_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_modeling_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_modeling_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_modeling_flax_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_modeling_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_modeling_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_tokenization_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_tokenization_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_tokenization_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_tokenization_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_modeling_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_modeling_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_modeling_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_modeling_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update tests/models/gemma/test_modeling_gemma.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * nit conversion script link * fix some tests * add not doctest and pr doctest * repo consistency * fix last CIs 🚀 * update all readmes --------- Co-authored-by: younesbelkada <younesbelkada@gmail.com> Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: sanchit-gandhi <sanchit@huggingface.co> Co-authored-by: Lysandre Debut <hi@lysand.re>
This commit is contained in:
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tests/models/gemma/test_modeling_flax_gemma.py
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tests/models/gemma/test_modeling_flax_gemma.py
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# Copyright 2024 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 AutoTokenizer, GemmaConfig, 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 jax
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import jax.numpy as jnp
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from transformers.models.gemma.modeling_flax_gemma import (
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FlaxGemmaForCausalLM,
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FlaxGemmaModel,
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)
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class FlaxGemmaModelTester:
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def __init__(
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self,
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parent,
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batch_size=2,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=False,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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num_key_value_heads=2,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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initializer_range=0.02,
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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_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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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 = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.scope = None
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self.bos_token_id = vocab_size - 1
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self.eos_token_id = vocab_size - 1
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self.pad_token_id = vocab_size - 1
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = np.tril(np.ones((self.batch_size, self.seq_length)))
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config = GemmaConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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num_key_value_heads=self.num_key_value_heads,
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head_dim=self.hidden_size // self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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use_cache=True,
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is_decoder=False,
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initializer_range=self.initializer_range,
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)
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return config, input_ids, input_mask
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, input_ids, attention_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
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return config, inputs_dict
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def check_use_cache_forward(self, model_class_name, config, input_ids, attention_mask):
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max_decoder_length = 20
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model = model_class_name(config)
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past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length)
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attention_mask = jnp.ones((input_ids.shape[0], max_decoder_length), dtype="i4")
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position_ids = jnp.broadcast_to(
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jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1)
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)
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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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position_ids=position_ids,
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)
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position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
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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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position_ids=position_ids,
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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, input_ids, attention_mask):
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max_decoder_length = 20
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model = model_class_name(config)
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attention_mask_cache = jnp.concatenate(
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[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))],
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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_decoder_length)
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position_ids = jnp.broadcast_to(
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jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1)
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)
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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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position_ids=position_ids,
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)
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position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
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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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position_ids=position_ids,
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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 FlaxGemmaModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittest.TestCase):
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all_model_classes = (FlaxGemmaModel, FlaxGemmaForCausalLM) if is_flax_available() else ()
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all_generative_model_classes = (FlaxGemmaForCausalLM,) if is_flax_available() else ()
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def setUp(self):
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self.model_tester = FlaxGemmaModelTester(self)
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def test_use_cache_forward(self):
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for model_class_name in self.all_model_classes:
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config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_use_cache_forward(model_class_name, config, input_ids, attention_mask)
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def test_use_cache_forward_with_attn_mask(self):
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for model_class_name in self.all_model_classes:
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config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_use_cache_forward_with_attn_mask(
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model_class_name, config, input_ids, attention_mask
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)
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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("google/gemma-2b", from_pt=True)
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outputs = model(np.ones((1, 1)))
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self.assertIsNotNone(outputs)
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@slow
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@require_flax
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class FlaxGemmaIntegrationTest(unittest.TestCase):
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input_text = ["The capital of France is", "To play the perfect cover drive"]
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model_id = "google/gemma-2b"
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revision = "flax"
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def setUp(self):
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self.model, self.params = FlaxGemmaForCausalLM.from_pretrained(
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self.model_id, revision=self.revision, _do_init=False
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)
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
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self.tokenizer.padding_side = "left"
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def test_logits(self):
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inputs = self.tokenizer(self.input_text, return_tensors="np", padding=True)
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# fmt: off
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EXPECTED_MEAN = [
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[-16.427, -21.386, -35.491, -36.258, -31.401, -36.370, -37.598],
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[-21.386, -32.150, -33.155, -34.344, -34.706, -34.678, -38.495],
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]
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EXPECTED_SLICE = [-33.462, -16.481, -30.837, -32.195, -33.113]
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# fmt: on
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logits = self.model(**inputs, params=self.params).logits
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diff_mean = jnp.abs(logits.mean(-1) - np.array(EXPECTED_MEAN)).max()
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diff_slice = jnp.abs(logits[0, -1, 475:480] - np.array(EXPECTED_SLICE)).max()
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self.assertAlmostEqual(diff_mean, 0, places=3)
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self.assertAlmostEqual(diff_slice, 0, places=3)
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def test_generation(self):
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EXPECTED_TEXTS = [
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"The capital of France is a city of contrasts. It is a city of history, of art, of culture, of fashion",
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"To play the perfect cover drive, you need to have a good technique and a good mindset.\n\nThe cover drive is a shot",
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]
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inputs = self.tokenizer(self.input_text, return_tensors="np", padding=True)
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output = self.model.generate(**inputs, params=self.params, max_new_tokens=20, do_sample=False)
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output_text = self.tokenizer.batch_decode(output.sequences, skip_special_tokens=True)
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self.assertEqual(output_text, EXPECTED_TEXTS)
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def test_jit_generation(self):
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EXPECTED_TEXTS = [
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"The capital of France is a city of contrasts. It is a city of history, culture, and art, but it is",
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"To play the perfect cover drive, you need to have a good technique and a good mindset.\n\nThe cover drive is a shot",
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]
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inputs = self.tokenizer(self.input_text, return_tensors="np", padding=True)
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def generate(input_ids, attention_mask):
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outputs = self.model.generate(
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input_ids, attention_mask=attention_mask, params=self.params, max_new_tokens=20, do_sample=False
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)
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return outputs
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jit_generate = jax.jit(generate)
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output_sequences = jit_generate(**inputs).sequences
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output_text = self.tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
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self.assertEqual(output_text, EXPECTED_TEXTS)
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