# Copyright 2020 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 copy import unittest from transformers import IBertConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device 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 torch import nn from transformers import ( IBertForMaskedLM, IBertForMultipleChoice, IBertForQuestionAnswering, IBertForSequenceClassification, IBertForTokenClassification, IBertModel, ) from transformers.models.ibert.modeling_ibert import ( IBertEmbeddings, IntGELU, IntLayerNorm, IntSoftmax, QuantAct, QuantEmbedding, QuantLinear, create_position_ids_from_input_ids, ) class IBertModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=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_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels 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 = scope def prepare_config_and_inputs(self): 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) 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() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return IBertConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, quant_mode=True, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 return config def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = IBertModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, 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(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = IBertForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = IBertForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = IBertForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = IBertForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class IBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): test_resize_embeddings = False all_model_classes = ( ( IBertForMaskedLM, IBertModel, IBertForSequenceClassification, IBertForTokenClassification, IBertForMultipleChoice, IBertForQuestionAnswering, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": IBertModel, "fill-mask": IBertForMaskedLM, "question-answering": IBertForQuestionAnswering, "text-classification": IBertForSequenceClassification, "token-classification": IBertForTokenClassification, "zero-shot": IBertForSequenceClassification, } if is_torch_available() else {} ) def setUp(self): self.model_tester = IBertModelTester(self) self.config_tester = ConfigTester(self, config_class=IBertConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "kssteven/ibert-roberta-base" model = IBertModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_create_position_ids_respects_padding_index(self): """This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is IBertEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] model = IBertEmbeddings(config=config) input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]]) expected_positions = torch.as_tensor( [[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]] ) position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) def test_create_position_ids_from_inputs_embeds(self): """This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is IBertEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] embeddings = IBertEmbeddings(config=config) inputs_embeds = torch.empty(2, 4, 30) expected_single_positions = [ 0 + embeddings.padding_idx + 1, 1 + embeddings.padding_idx + 1, 2 + embeddings.padding_idx + 1, 3 + embeddings.padding_idx + 1, ] expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions]) position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) # Override def test_model_get_set_embeddings(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), QuantEmbedding) model.set_input_embeddings(nn.Embedding(10, 10)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) # Override def test_feed_forward_chunking(self): pass # I-BERT does not support chunking # Override def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) if not self.is_encoder_decoder: input_ids = inputs["input_ids"] del inputs["input_ids"] else: encoder_input_ids = inputs["input_ids"] decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids) del inputs["input_ids"] inputs.pop("decoder_input_ids", None) wte = model.get_input_embeddings() if not self.is_encoder_decoder: embed, embed_scaling_factor = wte(input_ids) inputs["inputs_embeds"] = embed else: inputs["inputs_embeds"] = wte(encoder_input_ids) inputs["decoder_inputs_embeds"] = wte(decoder_input_ids) with torch.no_grad(): model(**inputs)[0] @unittest.skip(reason="ibert overrides scaling to None if inputs_embeds") def test_inputs_embeds_matches_input_ids(self): pass @require_torch class IBertModelIntegrationTest(unittest.TestCase): def test_quant_embedding(self): weight_bit = 8 embedding = QuantEmbedding(2, 4, quant_mode=True, weight_bit=weight_bit) embedding_weight = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]]) embedding.weight = nn.Parameter(embedding_weight) expected_scaling_factor = embedding_weight.abs().max() / (2 ** (weight_bit - 1) - 1) x, x_scaling_factor = embedding(torch.tensor(0)) y, y_scaling_factor = embedding(torch.tensor(1)) # scaling factor should follow the symmetric quantization rule self.assertTrue(torch.allclose(x_scaling_factor, expected_scaling_factor, atol=1e-4)) self.assertTrue(torch.allclose(x_scaling_factor, expected_scaling_factor, atol=1e-4)) self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4)) # quantization error should not exceed the scaling factor self.assertTrue(torch.allclose(x, embedding_weight[0], atol=expected_scaling_factor)) self.assertTrue(torch.allclose(y, embedding_weight[1], atol=expected_scaling_factor)) def test_quant_act(self): def _test_range(): act = QuantAct(activation_bit, act_range_momentum, quant_mode=True) # First pass x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]]) x_scaling_factor = torch.tensor(1.0) y, y_scaling_factor = act(x, x_scaling_factor) y_int = y / y_scaling_factor # After the first pass, x_min and x_max should be initialized with x.min() and x.max() expected_x_min, expected_x_max = x.min(), x.max() self.assertTrue(torch.allclose(act.x_min, expected_x_min, atol=1e-4)) self.assertTrue(torch.allclose(act.x_max, expected_x_max, atol=1e-4)) # scaling factor should follow the symmetric quantization rule expected_range = torch.max(expected_x_min.abs(), expected_x_max.abs()) expected_scaling_factor = expected_range / (2 ** (activation_bit - 1) - 1) self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4)) # quantization error should not exceed the scaling factor self.assertTrue(torch.allclose(x, y, atol=expected_scaling_factor)) # output should be integer self.assertTrue(torch.allclose(y_int, y_int.round(), atol=1e-4)) # Second Pass x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]]) * 2 x_scaling_factor = torch.tensor(1.0) y, y_scaling_factor = act(x, x_scaling_factor) y_int = y / y_scaling_factor # From the second pass, x_min and x_max should be updated with moving average expected_x_min = expected_x_min * act_range_momentum + x.min() * (1 - act_range_momentum) expected_x_max = expected_x_max * act_range_momentum + x.max() * (1 - act_range_momentum) self.assertTrue(torch.allclose(act.x_min, expected_x_min, atol=1e-4)) self.assertTrue(torch.allclose(act.x_max, expected_x_max, atol=1e-4)) # scaling factor should follow the symmetric quantization rule expected_range = torch.max(expected_x_min.abs(), expected_x_max.abs()) expected_scaling_factor = expected_range / (2 ** (activation_bit - 1) - 1) self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4)) # quantization error should not exceed the scaling factor x = x.clamp(min=-expected_range, max=expected_range) self.assertTrue(torch.allclose(x, y, atol=expected_scaling_factor)) # output should be integer self.assertTrue(torch.allclose(y_int, y_int.round(), atol=1e-4)) # Third pass, with eval() act.eval() x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]]) * 3 # In eval mode, min/max and scaling factor must be fixed self.assertTrue(torch.allclose(act.x_min, expected_x_min, atol=1e-4)) self.assertTrue(torch.allclose(act.x_max, expected_x_max, atol=1e-4)) self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4)) def _test_identity(): # test if identity and identity_scaling_factor are given # should add the input values act = QuantAct(activation_bit, act_range_momentum, quant_mode=True) x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]]) y = torch.tensor([[6.0, -7.0, 1.0, -2.0], [3.0, -4.0, -8.0, 5.0]]) x_scaling_factor = torch.tensor(1.0) y_scaling_factor = torch.tensor(0.5) z, z_scaling_factor = act(x, x_scaling_factor, y, y_scaling_factor) z_int = z / z_scaling_factor self.assertTrue(torch.allclose(x + y, z, atol=0.1)) self.assertTrue(torch.allclose(z_int, z_int.round(), atol=1e-4)) activation_bit = 8 act_range_momentum = 0.95 _test_range() _test_identity() def test_quant_linear(self): def _test(per_channel): linear_q = QuantLinear(2, 4, quant_mode=True, per_channel=per_channel, weight_bit=weight_bit) linear_dq = QuantLinear(2, 4, quant_mode=False, per_channel=per_channel, weight_bit=weight_bit) linear_weight = torch.tensor([[-1.0, 2.0, 3.0, -4.0], [5.0, -6.0, -7.0, 8.0]]).T linear_q.weight = nn.Parameter(linear_weight) linear_dq.weight = nn.Parameter(linear_weight) q, q_scaling_factor = linear_q(x, x_scaling_factor) q_int = q / q_scaling_factor dq, dq_scaling_factor = linear_dq(x, x_scaling_factor) if per_channel: q_max = linear_weight.abs().max(dim=1).values else: q_max = linear_weight.abs().max() expected_scaling_factor = q_max / (2 ** (weight_bit - 1) - 1) # scaling factor should follow the symmetric quantization rule self.assertTrue(torch.allclose(linear_q.fc_scaling_factor, expected_scaling_factor, atol=1e-4)) # output of the normal linear layer and the quantized linear layer should be similar self.assertTrue(torch.allclose(q, dq, atol=0.5)) # output of the quantized linear layer should be integer self.assertTrue(torch.allclose(q_int, q_int.round(), atol=1e-4)) weight_bit = 8 x = torch.tensor([[2.0, -5.0], [-3.0, 4.0]]) x_scaling_factor = torch.tensor([1.0]) _test(True) _test(False) def test_int_gelu(self): gelu_q = IntGELU(quant_mode=True) gelu_dq = nn.GELU() x_int = torch.arange(-10000, 10001, 1) x_scaling_factor = torch.tensor(0.001) x = x_int * x_scaling_factor q, q_scaling_factor = gelu_q(x, x_scaling_factor) q_int = q / q_scaling_factor dq = gelu_dq(x) # output of the normal GELU and the quantized GELU should be similar self.assertTrue(torch.allclose(q, dq, atol=0.5)) # output of the quantized GELU layer should be integer self.assertTrue(torch.allclose(q_int, q_int.round(), atol=1e-4)) def test_force_dequant_gelu(self): x_int = torch.arange(-10000, 10001, 1) x_scaling_factor = torch.tensor(0.001) x = x_int * x_scaling_factor gelu_dq = IntGELU(quant_mode=False) gelu_fdqs_dict = { True: [ IntGELU(quant_mode=True, force_dequant="nonlinear"), IntGELU(quant_mode=True, force_dequant="gelu"), ], False: [ IntGELU(quant_mode=True, force_dequant="none"), IntGELU(quant_mode=True, force_dequant="softmax"), IntGELU(quant_mode=True, force_dequant="layernorm"), ], } dq, dq_scaling_factor = gelu_dq(x, x_scaling_factor) for label, gelu_fdqs in gelu_fdqs_dict.items(): for gelu_fdq in gelu_fdqs: q, q_scaling_factor = gelu_fdq(x, x_scaling_factor) if label: self.assertTrue(torch.allclose(q, dq, atol=1e-4)) else: self.assertFalse(torch.allclose(q, dq, atol=1e-4)) def test_int_softmax(self): output_bit = 8 softmax_q = IntSoftmax(output_bit, quant_mode=True) softmax_dq = nn.Softmax() def _test(array): x_int = torch.tensor(array) x_scaling_factor = torch.tensor(0.1) x = x_int * x_scaling_factor q, q_scaling_factor = softmax_q(x, x_scaling_factor) q_int = q / q_scaling_factor dq = softmax_dq(x) # output of the normal Softmax and the quantized Softmax should be similar self.assertTrue(torch.allclose(q, dq, atol=0.5)) # output of the quantized GELU layer should be integer self.assertTrue(torch.allclose(q_int, q_int.round(), atol=1e-4)) # Output of the quantize Softmax should not exceed the output_bit self.assertTrue(q.abs().max() < 2**output_bit) array = [[i + j for j in range(10)] for i in range(-10, 10)] _test(array) array = [[i + j for j in range(50)] for i in range(-10, 10)] _test(array) array = [[i + 100 * j for j in range(2)] for i in range(-10, 10)] _test(array) def test_force_dequant_softmax(self): output_bit = 8 array = [[i + j for j in range(10)] for i in range(-10, 10)] x_int = torch.tensor(array) x_scaling_factor = torch.tensor(0.1) x = x_int * x_scaling_factor softmax_dq = IntSoftmax(output_bit, quant_mode=False) softmax_fdqs_dict = { True: [ IntSoftmax(output_bit, quant_mode=True, force_dequant="nonlinear"), IntSoftmax(output_bit, quant_mode=True, force_dequant="softmax"), ], False: [ IntSoftmax(output_bit, quant_mode=True, force_dequant="none"), IntSoftmax(output_bit, quant_mode=True, force_dequant="gelu"), IntSoftmax(output_bit, quant_mode=True, force_dequant="layernorm"), ], } dq, dq_scaling_factor = softmax_dq(x, x_scaling_factor) for label, softmax_fdqs in softmax_fdqs_dict.items(): for softmax_fdq in softmax_fdqs: q, q_scaling_factor = softmax_fdq(x, x_scaling_factor) if label: self.assertTrue(torch.allclose(q, dq, atol=1e-4)) else: self.assertFalse(torch.allclose(q, dq, atol=1e-4)) def test_int_layernorm(self): output_bit = 8 # some random matrix array = [[[i * j * j + j for j in range(5, 15)]] for i in range(-10, 10)] x_int = torch.tensor(array) x_scaling_factor = torch.tensor(0.1) x = x_int * x_scaling_factor ln_q = IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit) ln_dq = nn.LayerNorm(x.shape[1:], 1e-5) ln_q.weight = nn.Parameter(torch.ones(x.shape[1:])) ln_q.bias = nn.Parameter(torch.ones(x.shape[1:])) ln_dq.weight = nn.Parameter(torch.ones(x.shape[1:])) ln_dq.bias = nn.Parameter(torch.ones(x.shape[1:])) q, q_scaling_factor = ln_q(x, x_scaling_factor) q_int = q / q_scaling_factor dq = ln_dq(x) # output of the normal LN and the quantized LN should be similar self.assertTrue(torch.allclose(q, dq, atol=0.5)) # output of the quantized GELU layer should be integer self.assertTrue(torch.allclose(q_int, q_int.round(), atol=1e-4)) def test_force_dequant_layernorm(self): output_bit = 8 array = [[[i * j * j + j for j in range(5, 15)]] for i in range(-10, 10)] x_int = torch.tensor(array) x_scaling_factor = torch.tensor(0.1) x = x_int * x_scaling_factor ln_dq = IntLayerNorm(x.shape[1:], 1e-5, quant_mode=False, output_bit=output_bit) ln_fdqs_dict = { True: [ IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="nonlinear"), IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="layernorm"), ], False: [ IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="none"), IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="gelu"), IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="softmax"), ], } ln_dq.weight = nn.Parameter(torch.ones(x.shape[1:])) ln_dq.bias = nn.Parameter(torch.ones(x.shape[1:])) dq, dq_scaling_factor = ln_dq(x, x_scaling_factor) for label, ln_fdqs in ln_fdqs_dict.items(): for ln_fdq in ln_fdqs: ln_fdq.weight = nn.Parameter(torch.ones(x.shape[1:])) ln_fdq.bias = nn.Parameter(torch.ones(x.shape[1:])) q, q_scaling_factor = ln_fdq(x, x_scaling_factor) if label: self.assertTrue(torch.allclose(q, dq, atol=1e-4)) else: self.assertFalse(torch.allclose(q, dq, atol=1e-4)) def quantize(self, model): # Helper function that quantizes the given model # Recursively convert all the `quant_mode` attributes as `True` if hasattr(model, "quant_mode"): model.quant_mode = True elif isinstance(model, nn.Sequential): for n, m in model.named_children(): self.quantize(m) elif isinstance(model, nn.ModuleList): for n in model: self.quantize(n) else: for attr in dir(model): mod = getattr(model, attr) if isinstance(mod, nn.Module) and mod != model: self.quantize(mod) @slow def test_inference_masked_lm(self): # I-BERT should be "equivalent" to RoBERTa if not quantized # Test coped from `test_modeling_roberta.py` model = IBertForMaskedLM.from_pretrained("kssteven/ibert-roberta-base") input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] expected_shape = torch.Size((1, 11, 50265)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[33.8802, -4.3103, 22.7761], [4.6539, -2.8098, 13.6253], [1.8228, -3.6898, 8.8600]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)) # I-BERT should be "similar" to RoBERTa if quantized self.quantize(model) output = model(input_ids)[0] self.assertEqual(output.shape, expected_shape) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=0.1)) @slow def test_inference_classification_head(self): # I-BERT should be "equivalent" to RoBERTa if not quantized # Test coped from `test_modeling_roberta.py` model = IBertForSequenceClassification.from_pretrained("kssteven/ibert-roberta-large-mnli") input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] expected_shape = torch.Size((1, 3)) self.assertEqual(output.shape, expected_shape) expected_tensor = torch.tensor([[-0.9469, 0.3913, 0.5118]]) self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-4)) # I-BERT should be "similar" to RoBERTa if quantized self.quantize(model) output = model(input_ids)[0] self.assertEqual(output.shape, expected_shape) self.assertTrue(torch.allclose(output, expected_tensor, atol=0.1))