394 lines
15 KiB
Python
394 lines
15 KiB
Python
# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. 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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"""Testing suite for the PyTorch DBRX model."""
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import unittest
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from transformers import DbrxConfig, is_torch_available
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from transformers.testing_utils import require_torch, 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 DbrxForCausalLM, DbrxModel
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class DbrxModelTester:
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def __init__(
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self,
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parent,
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hidden_size=32,
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ffn_hidden_size=32,
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num_attention_heads=4,
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kv_n_heads=4,
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num_hidden_layers=5,
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max_position_embeddings=512,
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type_vocab_size=16,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=False,
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use_labels=True,
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use_cache=True,
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type_sequence_label_size=2,
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num_labels=3,
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num_choices=4,
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scope=None,
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clip_qkv=8,
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rope_theta=500000,
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attn_config_model_type="",
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emb_pdrop=0.0,
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moe_jitter_eps=0,
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moe_loss_weight=0.05,
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moe_num_experts=16,
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moe_top_k=4,
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ffn_config_model_type="",
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ffn_act_fn_name="gelu",
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initializer_range=0.02,
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output_router_logits=False,
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resid_pdrop=0.0,
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tie_word_embeddings=False,
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torch_dtype="bfloat16",
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vocab_size=99,
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is_decoder=True,
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pad_token_id=0,
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):
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# Parameters unique to testing
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self.batch_size = batch_size
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self.seq_length = seq_length
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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.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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self.parent = parent
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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# attn_config params
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self.clip_qkv = clip_qkv
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self.kv_n_heads = kv_n_heads
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self.rope_theta = rope_theta
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self.attn_config_model_type = attn_config_model_type
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# ffn_config params
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self.ffn_hidden_size = ffn_hidden_size
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self.moe_jitter_eps = moe_jitter_eps
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self.moe_loss_weight = moe_loss_weight
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self.moe_num_experts = moe_num_experts
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self.moe_top_k = moe_top_k
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self.ffn_config_model_type = ffn_config_model_type
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self.ffn_act_fn_name = ffn_act_fn_name
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# Other model params
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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.max_position_embeddings = max_position_embeddings
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self.vocab_size = vocab_size
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self.use_cache = use_cache
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self.initializer_range = initializer_range
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self.emb_pdrop = emb_pdrop
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self.output_router_logits = output_router_logits
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self.resid_pdrop = resid_pdrop
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self.tie_word_embeddings = tie_word_embeddings
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self.torch_dtype = torch_dtype
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self.is_decoder = is_decoder
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self.pad_token_id = pad_token_id
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# Make the dictionaries
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self.ffn_config = {
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"ffn_hidden_size": self.ffn_hidden_size,
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"moe_jitter_eps": self.moe_jitter_eps,
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"moe_loss_weight": self.moe_loss_weight,
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"moe_num_experts": self.moe_num_experts,
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"moe_top_k": self.moe_top_k,
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"model_type": self.ffn_config_model_type,
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"ffn_act_fn": {"name": self.ffn_act_fn_name},
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}
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self.attn_config = {
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"clip_qkv": self.clip_qkv,
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"kv_n_heads": self.kv_n_heads,
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"model_type": self.attn_config_model_type,
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"rope_theta": self.rope_theta,
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}
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_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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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self):
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# Behind the scenes, `DbrxConfig` maps the parameters `hidden_size`, `num_hidden_layers`,
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# `num_attention_heads`, `max_position_embeddings` to the parameters `d_model`, `n_layers`,
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# `n_heads`, `max_seq_len` respectively. We use the first group of parameters because
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# other tests expect every model to have these parameters with these specific names.
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config = DbrxConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size, # mapped to `d_model`
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num_hidden_layers=self.num_hidden_layers, # mapped to `n_layers`
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num_attention_heads=self.num_attention_heads, # mapped to `n_heads`
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max_position_embeddings=self.max_position_embeddings, # mapped to `max_seq_len`
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attn_config=self.attn_config,
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ffn_config=self.ffn_config,
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resid_pdrop=self.resid_pdrop,
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emb_pdrop=self.emb_pdrop,
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use_cache=self.use_cache,
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initializer_range=self.initializer_range,
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output_router_logits=self.output_router_logits,
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is_decoder=self.is_decoder,
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pad_token_id=self.pad_token_id,
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)
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return config
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->Dbrx
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def create_and_check_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = DbrxModel(config=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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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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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model_as_decoder with Llama->Dbrx
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def create_and_check_model_as_decoder(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = DbrxModel(config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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)
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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)
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result = model(input_ids, attention_mask=input_mask)
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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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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_for_causal_lm with Llama->Dbrx
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def create_and_check_for_causal_lm(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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model = DbrxForCausalLM(config=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, labels=token_labels)
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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_decoder_model_past_large_inputs(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.is_decoder = True
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config.add_cross_attention = True
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model = DbrxForCausalLM(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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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_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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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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output_hidden_states=True,
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)["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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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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past_key_values=past_key_values,
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output_hidden_states=True,
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)["hidden_states"][0]
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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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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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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs_for_common with Llama->Dbrx
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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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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class DbrxModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (DbrxModel, DbrxForCausalLM) if is_torch_available() else ()
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all_generative_model_classes = (DbrxForCausalLM,) if is_torch_available() else ()
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pipeline_model_mapping = {"text-generation": DbrxForCausalLM} if is_torch_available() else {}
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test_headmasking = False
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test_pruning = False
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def setUp(self):
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self.model_tester = DbrxModelTester(self)
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self.config_tester = ConfigTester(self, config_class=DbrxConfig, d_model=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_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_model(*config_and_inputs)
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def test_model_various_embeddings(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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for type in ["absolute", "relative_key", "relative_key_query"]:
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config_and_inputs[0].position_embedding_type = type
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self.model_tester.create_and_check_model(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model_name = "eitanturok/dbrx-tiny"
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model = DbrxModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@unittest.skip("Dbrx models have weight tying disabled.")
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def test_tied_weights_keys(self):
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pass
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# Offload does not work with Dbrx models because of the forward of DbrxExperts where we chunk the experts.
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# The issue is that the offloaded weights of the mlp layer are still on meta device (w1_chunked, v1_chunked, w2_chunked)
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@unittest.skip("Dbrx models do not work with offload")
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def test_cpu_offload(self):
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pass
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@unittest.skip("Dbrx models do not work with offload")
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def test_disk_offload_safetensors(self):
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pass
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@unittest.skip("Dbrx models do not work with offload")
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def test_disk_offload_bin(self):
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pass
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@require_torch
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class DbrxModelIntegrationTest(unittest.TestCase):
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@slow
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def test_tiny_model_logits(self):
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model = DbrxForCausalLM.from_pretrained("Rocketknight1/dbrx-tiny-random")
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input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
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output = model(input_ids)[0]
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vocab_size = model.vocab_size
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expected_shape = torch.Size((1, 6, vocab_size))
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self.assertEqual(output.shape, expected_shape)
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expected_slice = torch.tensor(
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[
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[
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[-1.6300e-04, 5.0118e-04, 2.5437e-04],
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[2.0422e-05, 2.7210e-04, -1.5125e-04],
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[-1.5105e-04, 4.6879e-04, 3.3309e-04],
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]
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]
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)
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self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
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