518 lines
21 KiB
Python
518 lines
21 KiB
Python
# coding=utf-8
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# Copyright 2021 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 datetime
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import gc
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import math
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import unittest
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from transformers import XGLMConfig, is_torch_available
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from transformers.testing_utils import (
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require_torch,
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require_torch_accelerator,
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require_torch_fp16,
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slow,
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torch_device,
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)
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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, floats_tensor, 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 XGLMForCausalLM, XGLMModel, XGLMTokenizer
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class XGLMModelTester:
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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_input_mask=True,
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use_labels=True,
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vocab_size=99,
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d_model=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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ffn_dim=37,
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activation_function="gelu",
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activation_dropout=0.1,
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attention_dropout=0.1,
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max_position_embeddings=512,
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initializer_range=0.02,
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scope=None,
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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_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = d_model
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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.ffn_dim = ffn_dim
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self.activation_function = activation_function
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self.activation_dropout = activation_dropout
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self.attention_dropout = attention_dropout
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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 = 0
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self.eos_token_id = 2
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self.pad_token_id = 1
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def get_large_model_config(self):
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return XGLMConfig.from_pretrained("facebook/xglm-564M")
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def prepare_config_and_inputs(
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self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
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):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(3)
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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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config = self.get_config(gradient_checkpointing=gradient_checkpointing)
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head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
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return (
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config,
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input_ids,
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input_mask,
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head_mask,
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)
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def get_config(
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self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
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):
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return XGLMConfig(
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vocab_size=self.vocab_size,
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d_model=self.hidden_size,
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num_layers=self.num_hidden_layers,
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attention_heads=self.num_attention_heads,
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ffn_dim=self.ffn_dim,
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activation_function=self.activation_function,
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activation_dropout=self.activation_dropout,
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attention_dropout=self.attention_dropout,
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max_position_embeddings=self.max_position_embeddings,
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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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gradient_checkpointing=gradient_checkpointing,
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)
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def prepare_config_and_inputs_for_decoder(self):
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(
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config,
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input_ids,
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input_mask,
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head_mask,
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) = self.prepare_config_and_inputs()
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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return (
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config,
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input_ids,
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input_mask,
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head_mask,
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encoder_hidden_states,
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encoder_attention_mask,
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)
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def create_and_check_xglm_model(self, config, input_ids, input_mask, head_mask, *args):
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model = XGLMModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, head_mask=head_mask)
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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.num_hidden_layers)
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def create_and_check_xglm_model_past(self, config, input_ids, input_mask, head_mask, *args):
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model = XGLMModel(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, use_cache=True)
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outputs_no_past = model(input_ids, use_cache=False)
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self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
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output, past = outputs.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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# 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_xglm_model_attention_mask_past(self, config, input_ids, input_mask, head_mask, *args):
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model = XGLMModel(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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# 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.zeros((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_xglm_model_past_large_inputs(self, config, input_ids, input_mask, head_mask, *args):
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model = XGLMModel(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=input_mask, use_cache=True)
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output, past = outputs.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, 3), config.vocab_size)
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next_mask = ids_tensor((self.batch_size, 3), vocab_size=1)
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# append to next input_ids
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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(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
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output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past)[
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"last_hidden_state"
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]
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self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
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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[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, 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_lm_head_model(self, config, input_ids, input_mask, head_mask, *args):
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model = XGLMForCausalLM(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_forward_and_backwards(
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self, config, input_ids, input_mask, head_mask, *args, gradient_checkpointing=False
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):
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model = XGLMForCausalLM(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_xglm_weight_initialization(self, config, *args):
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model = XGLMModel(config)
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model_std = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_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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(
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config,
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input_ids,
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input_mask,
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head_mask,
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) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"head_mask": head_mask,
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}
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return config, inputs_dict
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@require_torch
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class XGLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (XGLMModel, XGLMForCausalLM) if is_torch_available() else ()
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all_generative_model_classes = (XGLMForCausalLM,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"feature-extraction": XGLMModel, "text-generation": XGLMForCausalLM} if is_torch_available() else {}
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)
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fx_compatible = True
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test_missing_keys = False
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test_pruning = False
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def setUp(self):
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self.model_tester = XGLMModelTester(self)
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self.config_tester = ConfigTester(self, config_class=XGLMConfig, 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_xglm_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_xglm_model(*config_and_inputs)
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def test_xglm_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_xglm_model_past(*config_and_inputs)
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def test_xglm_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_xglm_model_attention_mask_past(*config_and_inputs)
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def test_xglm_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_xglm_model_past_large_inputs(*config_and_inputs)
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def test_xglm_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_xglm_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_xglm_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_xglm_weight_initialization(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model_name = "facebook/xglm-564M"
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model = XGLMModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@unittest.skip("Does not work on the tiny model as we keep hitting edge cases.")
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def test_model_parallelism(self):
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super().test_model_parallelism()
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@require_torch
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class XGLMModelLanguageGenerationTest(unittest.TestCase):
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def tearDown(self):
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super().tearDown()
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# clean-up as much as possible GPU memory occupied by PyTorch
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gc.collect()
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torch.cuda.empty_cache()
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def _test_lm_generate_xglm_helper(
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self,
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gradient_checkpointing=False,
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verify_outputs=True,
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):
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model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M")
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if gradient_checkpointing:
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model.gradient_checkpointing_enable()
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else:
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model.gradient_checkpointing_disable()
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model.to(torch_device)
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input_ids = torch.tensor([[2, 268, 9865]], dtype=torch.long, device=torch_device) # The dog
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# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
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expected_output_ids = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581] # fmt: skip
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output_ids = model.generate(input_ids, do_sample=False, num_beams=1)
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if verify_outputs:
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self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
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@slow
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def test_batch_generation(self):
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model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M")
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model.to(torch_device)
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tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
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tokenizer.padding_side = "left"
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# use different length sentences to test batching
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sentences = [
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"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
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"left-padding, such as in batched generation. The output for the sequence below should be the same "
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"regardless of whether left padding is applied or not. When",
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"Hello, my dog is a little",
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]
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inputs = tokenizer(sentences, return_tensors="pt", padding=True)
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input_ids = inputs["input_ids"].to(torch_device)
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outputs = model.generate(
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input_ids=input_ids, attention_mask=inputs["attention_mask"].to(torch_device), max_new_tokens=12
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)
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inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
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output_non_padded = model.generate(input_ids=inputs_non_padded, max_new_tokens=12)
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inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
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output_padded = model.generate(input_ids=inputs_padded, max_new_tokens=12)
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batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
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padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
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expected_output_sentence = [
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"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
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"left-padding, such as in batched generation. The output for the sequence below should be the same "
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"regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
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"a single",
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"Hello, my dog is a little bit of a shy one, but he is very friendly",
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]
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self.assertListEqual(expected_output_sentence, batch_out_sentence)
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self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
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@slow
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def test_lm_generate_xglm(self):
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self._test_lm_generate_xglm_helper()
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@slow
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def test_lm_generate_xglm_with_gradient_checkpointing(self):
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self._test_lm_generate_xglm_helper(gradient_checkpointing=True)
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@slow
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def test_xglm_sample(self):
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tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
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model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M")
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torch.manual_seed(0)
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tokenized = tokenizer("Today is a nice day and", return_tensors="pt")
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input_ids = tokenized.input_ids
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output_ids = model.generate(input_ids, do_sample=True, num_beams=1)
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output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
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EXPECTED_OUTPUT_STRS = [
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# TODO: remove this once we move to torch 2.0
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# torch 1.13.1 + cu116
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"Today is a nice day and the sun is shining. A nice day with warm rainy",
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# torch 2.0 + cu117
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"Today is a nice day and the water is still cold. We just stopped off for some fresh",
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]
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self.assertIn(output_str, EXPECTED_OUTPUT_STRS)
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@slow
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def test_xglm_sample_max_time(self):
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tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
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model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M")
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model.to(torch_device)
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torch.manual_seed(0)
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tokenized = tokenizer("Today is a nice day and", return_tensors="pt")
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input_ids = tokenized.input_ids.to(torch_device)
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MAX_TIME = 0.15
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start = datetime.datetime.now()
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model.generate(input_ids, do_sample=True, max_time=MAX_TIME, max_length=256)
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duration = datetime.datetime.now() - start
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self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
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self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
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start = datetime.datetime.now()
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model.generate(input_ids, do_sample=False, max_time=MAX_TIME, max_length=256)
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duration = datetime.datetime.now() - start
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self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
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self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
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start = datetime.datetime.now()
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model.generate(input_ids, do_sample=False, num_beams=2, max_time=MAX_TIME, max_length=256)
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duration = datetime.datetime.now() - start
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self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
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self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
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start = datetime.datetime.now()
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model.generate(input_ids, do_sample=True, num_beams=2, max_time=MAX_TIME, max_length=256)
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duration = datetime.datetime.now() - start
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self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
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self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
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start = datetime.datetime.now()
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model.generate(input_ids, do_sample=False, max_time=None, max_length=256)
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duration = datetime.datetime.now() - start
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self.assertGreater(duration, datetime.timedelta(seconds=1.25 * MAX_TIME))
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@require_torch_accelerator
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@require_torch_fp16
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def test_batched_nan_fp16(self):
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model_name = "facebook/xglm-564M"
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tokenizer = XGLMTokenizer.from_pretrained(model_name, use_fast=False, padding_side="left")
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model = XGLMForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, use_cache=True).to(torch_device)
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model = model.eval()
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batch = tokenizer(["Who are you?", "Joe Biden is the president of"], padding=True, return_tensors="pt")
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input_ids = batch["input_ids"].to(torch_device)
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attention_mask = batch["attention_mask"].to(torch_device)
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with torch.no_grad():
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outputs = model(input_ids, attention_mask=attention_mask)
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self.assertFalse(
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torch.isnan(outputs.logits[0]).any().item()
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) # the first logits could contain NaNs if it fails
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