# Copyright 2023 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 parameterized import parameterized from transformers import GPTBigCodeConfig, is_torch_available from transformers.testing_utils import cleanup, require_torch, 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 ( AutoTokenizer, GPT2TokenizerFast, GPTBigCodeForCausalLM, GPTBigCodeForSequenceClassification, GPTBigCodeForTokenClassification, GPTBigCodeModel, ) from transformers.models.gpt_bigcode.modeling_gpt_bigcode import GPTBigCodeAttention class GPTBigCodeModelTester: def __init__( self, parent, batch_size=14, seq_length=7, is_training=True, use_token_type_ids=True, 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="relu", 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, multi_query=True, 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 - 2 self.pad_token_id = vocab_size - 3 self.multi_query = multi_query def prepare_config_and_inputs( self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=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]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) mc_token_ids = None if self.use_mc_token_ids: mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config( gradient_checkpointing=gradient_checkpointing, scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx, reorder_and_upcast_attn=reorder_and_upcast_attn, ) return ( config, input_ids, input_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) def get_config( self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False ): return GPTBigCodeConfig( vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, n_inner=self.intermediate_size, activation_function=self.hidden_act, 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, scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx, reorder_and_upcast_attn=reorder_and_upcast_attn, attention_softmax_in_fp32=False, scale_attention_softmax_in_fp32=False, multi_query=self.multi_query, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 return config def create_and_check_gpt_bigcode_model(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTBigCodeModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) 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_gpt_bigcode_model_past(self, config, input_ids, input_mask, token_type_ids, *args): model = GPTBigCodeModel(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True) outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids) outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) output, past = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size) # append to next input_ids and token_type_ids next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1) output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"] output_from_past = model(next_tokens, token_type_ids=next_token_types, 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_gpt_bigcode_model_attention_mask_past( self, config, input_ids, input_mask, token_type_ids, *args ): model = GPTBigCodeModel(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_gpt_bigcode_model_past_large_inputs( self, config, input_ids, input_mask, token_type_ids, *args ): model = GPTBigCodeModel(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model(input_ids, token_type_ids=token_type_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_token_types = ids_tensor([self.batch_size, 3], self.type_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_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask )["last_hidden_state"] output_from_past = model( next_tokens, token_type_ids=next_token_types, 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, token_type_ids, *args): model = GPTBigCodeForCausalLM(config) model.to(torch_device) model.eval() result = model(input_ids, token_type_ids=token_type_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, token_type_ids, *args, ): model = GPTBigCodeForCausalLM(config) model.train() model.to(torch_device) result = model(input_ids, token_type_ids=token_type_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_gpt_bigcode_for_sequence_classification( self, config, input_ids, input_mask, token_type_ids, mc_token_ids, sequence_labels, *args ): config.num_labels = self.num_labels model = GPTBigCodeForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_gpt_bigcode_for_token_classification( self, config, input_ids, input_mask, token_type_ids, mc_token_ids, sequence_labels, *args ): config.num_labels = self.num_labels model = GPTBigCodeForTokenClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_gpt_bigcode_weight_initialization(self, config, *args): model = GPTBigCodeModel(config) model_std = model.config.initializer_range / math.sqrt(2 * model.config.n_layer) for key in model.state_dict(): if "c_proj" in key and "weight" in key: self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001) self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "token_type_ids": token_type_ids, } return config, inputs_dict @require_torch class GPTBigCodeModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): # TODO: Update the tests to use valid pretrained models. all_model_classes = ( ( GPTBigCodeModel, GPTBigCodeForCausalLM, GPTBigCodeForSequenceClassification, GPTBigCodeForTokenClassification, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": GPTBigCodeModel, "text-classification": GPTBigCodeForSequenceClassification, "text-generation": GPTBigCodeForCausalLM, "token-classification": GPTBigCodeForTokenClassification, "zero-shot": GPTBigCodeForSequenceClassification, } if is_torch_available() else {} ) test_missing_keys = False multi_query = True # special case for DoubleHeads model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) return inputs_dict def setUp(self): self.model_tester = GPTBigCodeModelTester(self, multi_query=self.multi_query) self.config_tester = ConfigTester(self, config_class=GPTBigCodeConfig, n_embd=37) def tearDown(self): super().tearDown() # clean-up as much as possible GPU memory occupied by PyTorch cleanup(torch_device) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="MQA models does not support retain_grad") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="CPU offload seems to be broken for some reason - tiny models keep hitting corner cases") def test_cpu_offload(self): pass @unittest.skip(reason="Disk offload seems to be broken for some reason - tiny models keep hitting corner cases") def test_disk_offload(self): pass @unittest.skip(reason="BigCodeGPT has a non-standard KV cache format.") def test_past_key_values_format(self): pass @unittest.skip(reason="BigCodeGPT has a non-standard KV cache format and breaks this test.") def test_generate_continue_from_inputs_embeds(self): pass def test_gpt_bigcode_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_bigcode_model(*config_and_inputs) def test_gpt_bigcode_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_bigcode_model_past(*config_and_inputs) def test_gpt_bigcode_model_att_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_bigcode_model_attention_mask_past(*config_and_inputs) def test_gpt_bigcode_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_bigcode_model_past_large_inputs(*config_and_inputs) def test_gpt_bigcode_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_gpt_bigcode_sequence_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_bigcode_for_sequence_classification(*config_and_inputs) def test_gpt_bigcode_token_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_bigcode_for_token_classification(*config_and_inputs) def test_gpt_bigcode_scale_attn_by_inverse_layer_idx(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(scale_attn_by_inverse_layer_idx=True) self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs) def test_gpt_bigcode_reorder_and_upcast_attn(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(reorder_and_upcast_attn=True) self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs) def test_gpt_bigcode_weight_initialization(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt_bigcode_weight_initialization(*config_and_inputs) @require_torch class GPTBigCodeMHAModelTest(GPTBigCodeModelTest): # `parameterized_class` breaks with mixins, so we use inheritance instead multi_query = False @slow @require_torch class GPTBigCodeModelLanguageGenerationTest(unittest.TestCase): def test_generate_simple(self): model = GPTBigCodeForCausalLM.from_pretrained("bigcode/gpt_bigcode-santacoder").to(torch_device) tokenizer = GPT2TokenizerFast.from_pretrained("bigcode/gpt_bigcode-santacoder") input_ids = tokenizer("def print_hello_world():", return_tensors="pt").input_ids.to(torch_device) output_sequence = model.generate(input_ids) output_sentence = tokenizer.decode(output_sequence[0], skip_special_tokens=True) expected_output = 'def print_hello_world():\n print("Hello World!")\n\n\ndef print_hello_world_with_args(name' # fmt: skip self.assertEqual(output_sentence, expected_output) def test_generate_batched(self): tokenizer = GPT2TokenizerFast.from_pretrained("bigcode/gpt_bigcode-santacoder") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" model = GPTBigCodeForCausalLM.from_pretrained("bigcode/gpt_bigcode-santacoder").to(torch_device) inputs = tokenizer(["def print_hello_world():", "def say_hello():"], return_tensors="pt", padding=True).to( torch_device ) outputs = model.generate(**inputs) outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) expected_output = [ 'def print_hello_world():\n print("Hello World!")\n\n\ndef print_hello_world_with_args(name', 'def say_hello():\n print("Hello, World!")\n\n\nsay_hello()\n', ] self.assertListEqual(outputs, expected_output) def test_newline_regression(self): """Added to prevent regressions regarding attention (scaling) indicated by excessive newlines""" tokenizer = AutoTokenizer.from_pretrained("bigcode/tiny_starcoder_py") model = GPTBigCodeForCausalLM.from_pretrained("bigcode/tiny_starcoder_py").to(torch_device) input_ids = tokenizer( "Analyze the impact of the COVID-19 pandemic on global economic structures and future business models.\n", return_tensors="pt", ).input_ids.to(torch_device) output_sequence = model.generate(input_ids, max_new_tokens=20, do_sample=False) output_sentence = tokenizer.decode(output_sequence[0], skip_special_tokens=True) expected_output = 'Analyze the impact of the COVID-19 pandemic on global economic structures and future business models.\n\nThe impact of the COVID-19 pandemic on global economic structures and future business' # fmt: skip self.assertEqual(output_sentence, expected_output) @require_torch class GPTBigCodeMQATest(unittest.TestCase): def get_attention(self, multi_query): config = GPTBigCodeConfig.from_pretrained( "bigcode/gpt_bigcode-santacoder", multi_query=multi_query, attn_pdrop=0, resid_pdrop=0, ) # We need to set it here as it's normally set by the Model's __init__ config._attn_implementation = "sdpa" return GPTBigCodeAttention(config) @parameterized.expand([(seed, is_train_mode) for seed in range(5) for is_train_mode in [True, False]]) def test_mqa_reduces_to_mha(self, seed, is_train_mode=True): torch.manual_seed(seed) # CREATE MQA AND MHA ATTENTIONS attention_mqa = self.get_attention(True) attention_mha = self.get_attention(False) # ENFORCE MATCHING WEIGHTS num_heads = attention_mqa.num_heads embed_dim = attention_mqa.embed_dim head_dim = attention_mqa.head_dim with torch.no_grad(): mqa_q_weight = attention_mqa.c_attn.weight[:embed_dim, :].view(num_heads, 1, head_dim, embed_dim) mqa_kv_weight = attention_mqa.c_attn.weight[embed_dim:, :].view(1, 2, head_dim, embed_dim) mha_c_weight = torch.cat( [mqa_q_weight, mqa_kv_weight.expand(num_heads, 2, head_dim, embed_dim)], dim=1 ).view(3 * num_heads * head_dim, embed_dim) mqa_q_bias = attention_mqa.c_attn.bias[:embed_dim].view(num_heads, 1, head_dim) mqa_kv_bias = attention_mqa.c_attn.bias[embed_dim:].view(1, 2, head_dim) mha_c_bias = torch.cat([mqa_q_bias, mqa_kv_bias.expand(num_heads, 2, head_dim)], dim=1).view( 3 * num_heads * head_dim ) attention_mha.c_attn.weight.copy_(mha_c_weight) attention_mha.c_attn.bias.copy_(mha_c_bias) attention_mha.c_proj.weight.copy_(attention_mqa.c_proj.weight) attention_mha.c_proj.bias.copy_(attention_mqa.c_proj.bias) # PUT THE MODEL INTO THE CORRECT MODE attention_mha.train(is_train_mode) attention_mqa.train(is_train_mode) # RUN AN INPUT THROUGH THE MODELS num_tokens = 5 hidden_states = torch.randn(1, num_tokens, embed_dim) attention_mha_result = attention_mha(hidden_states)[0] attention_mqa_result = attention_mqa(hidden_states)[0] # CHECK THAT ALL OUTPUTS ARE THE SAME torch.testing.assert_close(attention_mha_result, attention_mqa_result, rtol=1e-5, atol=1e-5)