517 lines
21 KiB
Python
517 lines
21 KiB
Python
# coding=utf-8
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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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#
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import math
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import unittest
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from transformers import MptConfig, is_torch_available
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from transformers.testing_utils import require_bitsandbytes, require_torch, require_torch_gpu, slow, torch_device
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import (
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AutoTokenizer,
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MptForCausalLM,
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MptForQuestionAnswering,
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MptForSequenceClassification,
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MptForTokenClassification,
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MptModel,
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)
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@require_torch
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class MptModelTester:
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def __init__(
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self,
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parent,
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batch_size=14,
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seq_length=7,
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is_training=True,
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use_token_type_ids=False,
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use_input_mask=True,
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use_labels=True,
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use_mc_token_ids=True,
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vocab_size=99,
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hidden_size=48,
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num_hidden_layers=2,
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num_attention_heads=4,
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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_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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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_token_type_ids = use_token_type_ids
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self.use_input_mask = use_input_mask
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self.use_labels = use_labels
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self.use_mc_token_ids = use_mc_token_ids
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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.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_dropout_prob = attention_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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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 get_large_model_config(self):
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return MptConfig.from_pretrained("mosaicml/mpt-7b")
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def prepare_config_and_inputs(self, gradient_checkpointing=False):
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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 = random_attention_mask([self.batch_size, self.seq_length])
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sequence_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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config = self.get_config(gradient_checkpointing=gradient_checkpointing)
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return (config, input_ids, input_mask, sequence_labels)
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def get_config(self, gradient_checkpointing=False):
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return MptConfig(
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vocab_size=self.vocab_size,
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seq_length=self.seq_length,
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hidden_size=self.hidden_size,
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n_layers=self.num_hidden_layers,
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n_heads=self.num_attention_heads,
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hidden_dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_dropout_prob,
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n_positions=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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use_cache=True,
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bos_token_id=self.bos_token_id,
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eos_token_id=self.eos_token_id,
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pad_token_id=self.pad_token_id,
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num_labels=self.num_labels,
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gradient_checkpointing=gradient_checkpointing,
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dtype="float32",
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)
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def create_and_check_mpt_model(self, config, input_ids, input_mask, *args):
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model = MptModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(len(result.past_key_values), config.n_layers)
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def create_and_check_mpt_model_past(self, config, input_ids, input_mask, *args):
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model = MptModel(config=config)
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model.to(torch_device)
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model.eval()
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# first forward pass
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outputs = model(input_ids, attention_mask=torch.ones_like(input_ids), use_cache=True)
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outputs_use_cache_conf = model(input_ids, attention_mask=torch.ones_like(input_ids))
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outputs_no_past = model(input_ids, use_cache=False, attention_mask=torch.ones_like(input_ids))
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self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
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self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
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past = outputs["past_key_values"]
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
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# append to next input_ids and token_type_ids
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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output_from_no_past = model(next_input_ids)["last_hidden_state"]
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output_from_past = model(next_tokens, past_key_values=past)["last_hidden_state"]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_mpt_model_attention_mask_past(self, config, input_ids, input_mask, *args):
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model = MptModel(config=config)
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model.to(torch_device)
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model.eval()
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# create attention mask
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attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
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half_seq_length = self.seq_length // 2
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attn_mask[:, half_seq_length:] = 0
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# first forward pass
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output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
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# change a random masked slice from input_ids
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random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
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random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
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input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
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# append to next input_ids and attn_mask
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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attn_mask = torch.cat(
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[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
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dim=1,
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)
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# get two different outputs
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output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
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output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_mpt_model_past_large_inputs(self, config, input_ids, input_mask, *args):
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model = MptModel(config=config)
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model.to(torch_device)
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model.eval()
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# first forward pass
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outputs = model(
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input_ids,
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attention_mask=input_mask,
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use_cache=True,
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)
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past_key_values = outputs.past_key_values
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
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output_from_no_past = model(
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next_input_ids,
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attention_mask=next_attention_mask,
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output_hidden_states=True,
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)
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hidden_states_from_no_past = output_from_no_past["hidden_states"][0]
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output_from_past = model(
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next_tokens,
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attention_mask=next_attention_mask,
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past_key_values=past_key_values,
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output_hidden_states=True,
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)
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hidden_states_from_past = output_from_past["hidden_states"][0]
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# select random slice
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random_slice_idx = ids_tensor((1,), hidden_states_from_past.shape[-1]).item()
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output_from_no_past_slice = hidden_states_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = hidden_states_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_lm_head_model(self, config, input_ids, input_mask, *args):
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model = MptForCausalLM(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, labels=input_ids)
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self.parent.assertEqual(result.loss.shape, ())
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_sequence_classification_model(self, config, input_ids, input_mask, *args):
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config.num_labels = self.num_labels
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model = MptForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def create_and_check_token_classification_model(self, config, input_ids, input_mask, *args):
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model = MptForTokenClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def create_and_check_question_answering_model(self, config, input_ids, input_mask, *args):
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model = MptForQuestionAnswering(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def create_and_check_forward_and_backwards(
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self, config, input_ids, input_mask, *args, gradient_checkpointing=False
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):
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model = MptForCausalLM(config)
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model.to(torch_device)
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if gradient_checkpointing:
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model.gradient_checkpointing_enable()
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result = model(input_ids, labels=input_ids)
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self.parent.assertEqual(result.loss.shape, ())
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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result.loss.backward()
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def create_and_check_mpt_weight_initialization(self, config, *args):
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model = MptModel(config)
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model_std = model.config.initializer_range / math.sqrt(2 * model.config.n_layers)
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for key in model.state_dict().keys():
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if "c_proj" in key and "weight" in key:
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self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001)
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self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01)
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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, input_mask, sequence_labels = config_and_inputs
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inputs_dict = {"input_ids": input_ids}
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return config, inputs_dict
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class MptConfigTester(ConfigTester):
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def __init__(self, parent, config_class=None, has_text_modality=True, common_properties=None, **kwargs):
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super().__init__(parent, config_class, has_text_modality, common_properties, **kwargs)
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def test_attn_config_as_dict(self):
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config = self.config_class(**self.inputs_dict, attn_config={"attn_impl": "flash", "softmax_scale": None})
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self.parent.assertTrue(config.attn_config.attn_impl == "flash")
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self.parent.assertTrue(config.attn_config.softmax_scale is None)
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def run_common_tests(self):
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self.test_attn_config_as_dict()
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return super().run_common_tests()
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@require_torch
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class MptModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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MptModel,
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MptForCausalLM,
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MptForSequenceClassification,
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MptForTokenClassification,
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MptForQuestionAnswering,
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)
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if is_torch_available()
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else ()
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)
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all_generative_model_classes = (MptForCausalLM,) if is_torch_available() else ()
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fx_compatible = False
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test_missing_keys = False
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test_pruning = False
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test_torchscript = False
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test_head_masking = False
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pipeline_model_mapping = (
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{
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"feature-extraction": MptModel,
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"question-answering": MptForQuestionAnswering,
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"text-classification": MptForSequenceClassification,
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"text-generation": MptForCausalLM,
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"token-classification": MptForTokenClassification,
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"zero-shot": MptForSequenceClassification,
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}
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if is_torch_available()
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else {}
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)
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def setUp(self):
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self.model_tester = MptModelTester(self)
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self.config_tester = MptConfigTester(self, config_class=MptConfig, n_embd=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_mpt_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_mpt_model(*config_and_inputs)
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def test_mpt_model_alibi_tensor(self):
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# test creation of alibi tensor when num heads is not a power of two
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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config_and_inputs[0].n_heads = 6
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self.model_tester.create_and_check_mpt_model(*config_and_inputs)
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def test_mpt_model_past(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_mpt_model_past(*config_and_inputs)
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def test_mpt_model_att_mask_past(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_mpt_model_attention_mask_past(*config_and_inputs)
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def test_mpt_model_past_large_inputs(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_mpt_model_past_large_inputs(*config_and_inputs)
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def test_mpt_lm_head_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
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def test_mpt_sequence_classification_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_sequence_classification_model(*config_and_inputs)
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def test_mpt_token_classification_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_token_classification_model(*config_and_inputs)
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def test_mpt_gradient_checkpointing(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
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def test_mpt_weight_initialization(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_mpt_weight_initialization(*config_and_inputs)
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@unittest.skip("For backward compatibility the lm_head is not in the model's state dict on the Hub.")
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def test_model_weights_reload_no_missing_tied_weights(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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model_name = "mosaicml/mpt-7b"
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model = MptModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@slow
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@require_torch_gpu
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@require_bitsandbytes
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class MptIntegrationTests(unittest.TestCase):
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def test_generation_8k(self):
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model_id = "mosaicml/mpt-7b-8k"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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# Load in 4bit to fit the daily CI runner GPU RAM
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model = MptForCausalLM.from_pretrained(
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model_id, torch_dtype=torch.bfloat16, device_map={"": 0}, load_in_4bit=True
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)
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input_text = "Hello"
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expected_output = "Hello, I'm a new user of the forum. I have a question about the \"Solaris"
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inputs = tokenizer(input_text, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=20)
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decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
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self.assertEqual(decoded_output, expected_output)
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def test_generation(self):
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model_id = "mosaicml/mpt-7b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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# Load in 4bit to fit the daily CI runner GPU RAM
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model = MptForCausalLM.from_pretrained(
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model_id, torch_dtype=torch.bfloat16, device_map={"": 0}, load_in_4bit=True
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)
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input_text = "Hello"
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expected_output = "Hello and welcome to the first episode of the new podcast, The Frugal Feminist.\n"
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inputs = tokenizer(input_text, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=20)
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decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
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self.assertEqual(decoded_output, expected_output)
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def test_generation_batched(self):
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model_id = "mosaicml/mpt-7b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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|
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# Load in 4bit to fit the daily CI runner GPU RAM
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model = MptForCausalLM.from_pretrained(
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model_id, torch_dtype=torch.bfloat16, device_map={"": 0}, load_in_4bit=True
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)
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input_texts = ["Hello my name is", "Today I am going at the gym and"]
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tokenizer.pad_token_id = tokenizer.eos_token_id
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tokenizer.padding_side = "left"
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inputs = tokenizer(input_texts, return_tensors="pt", padding=True).to(torch_device)
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expected_output = [
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"Hello my name is Tiffany and I am a mother of two beautiful children. I have been a nanny for the",
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"Today I am going at the gym and then I am going to go to the grocery store. I am going to buy some food and some",
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]
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outputs = model.generate(**inputs, max_new_tokens=20)
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decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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for i, predicted_output in enumerate(decoded_outputs):
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self.assertEqual(predicted_output, expected_output[i])
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def test_model_logits(self):
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model_id = "mosaicml/mpt-7b"
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# Load in 4bit to fit the daily CI runner GPU RAM
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model = MptForCausalLM.from_pretrained(
|
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model_id, torch_dtype=torch.bfloat16, device_map={"": 0}, load_in_4bit=True
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)
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|
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dummy_input = torch.LongTensor([[1, 2, 3, 4, 5]]).to(torch_device)
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outputs = model(dummy_input, output_hidden_states=True)
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expected_slice = torch.Tensor([-0.2520, -0.2178, -0.1953]).to(torch_device, torch.bfloat16)
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predicted_slice = outputs.hidden_states[-1][0, 0, :3]
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self.assertTrue(torch.allclose(expected_slice, predicted_slice, atol=1e-3, rtol=1e-3))
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