mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 13:20:12 +06:00
0106a50a6b
* fix a bunch of XPU UT failures on stock PyTorch 2.7 and 2.8 Signed-off-by: YAO Matrix <matrix.yao@intel.com> * qwen3 Signed-off-by: YAO Matrix <matrix.yao@intel.com> * quanto Signed-off-by: YAO Matrix <matrix.yao@intel.com> * models Signed-off-by: YAO Matrix <matrix.yao@intel.com> * fix style Signed-off-by: YAO Matrix <matrix.yao@intel.com> * idefics2 Signed-off-by: YAO Matrix <matrix.yao@intel.com> --------- Signed-off-by: YAO Matrix <matrix.yao@intel.com>
232 lines
9.4 KiB
Python
232 lines
9.4 KiB
Python
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""Testing suite for the PyTorch Mixtral model."""
|
|
|
|
import unittest
|
|
|
|
import pytest
|
|
|
|
from transformers import MixtralConfig, is_torch_available
|
|
from transformers.testing_utils import (
|
|
Expectations,
|
|
require_flash_attn,
|
|
require_torch,
|
|
require_torch_accelerator,
|
|
require_torch_gpu,
|
|
slow,
|
|
torch_device,
|
|
)
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
from transformers import (
|
|
MixtralForCausalLM,
|
|
MixtralForQuestionAnswering,
|
|
MixtralForSequenceClassification,
|
|
MixtralForTokenClassification,
|
|
MixtralModel,
|
|
)
|
|
|
|
from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
|
|
|
|
|
|
class MixtralModelTester(CausalLMModelTester):
|
|
config_class = MixtralConfig
|
|
if is_torch_available():
|
|
base_model_class = MixtralModel
|
|
causal_lm_class = MixtralForCausalLM
|
|
sequence_class = MixtralForSequenceClassification
|
|
token_class = MixtralForTokenClassification
|
|
question_answering_class = MixtralForQuestionAnswering
|
|
|
|
|
|
@require_torch
|
|
class MistralModelTest(CausalLMModelTest, unittest.TestCase):
|
|
all_model_classes = (
|
|
(
|
|
MixtralModel,
|
|
MixtralForCausalLM,
|
|
MixtralForSequenceClassification,
|
|
MixtralForTokenClassification,
|
|
MixtralForQuestionAnswering,
|
|
)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
pipeline_model_mapping = (
|
|
{
|
|
"feature-extraction": MixtralModel,
|
|
"text-classification": MixtralForSequenceClassification,
|
|
"token-classification": MixtralForTokenClassification,
|
|
"text-generation": MixtralForCausalLM,
|
|
"question-answering": MixtralForQuestionAnswering,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
|
|
test_headmasking = False
|
|
test_pruning = False
|
|
model_tester_class = MixtralModelTester
|
|
|
|
# TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146
|
|
def is_pipeline_test_to_skip(
|
|
self,
|
|
pipeline_test_case_name,
|
|
config_class,
|
|
model_architecture,
|
|
tokenizer_name,
|
|
image_processor_name,
|
|
feature_extractor_name,
|
|
processor_name,
|
|
):
|
|
return True
|
|
|
|
@require_flash_attn
|
|
@require_torch_gpu
|
|
@pytest.mark.flash_attn_test
|
|
@slow
|
|
def test_flash_attn_2_inference_equivalence_right_padding(self):
|
|
self.skipTest(reason="Mistral flash attention does not support right padding")
|
|
|
|
# Ignore copy
|
|
def test_load_balancing_loss(self):
|
|
r"""
|
|
Let's make sure we can actually compute the loss and do a backward on it.
|
|
"""
|
|
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.num_labels = 3
|
|
config.num_local_experts = 8
|
|
config.output_router_logits = True
|
|
input_ids = input_dict["input_ids"]
|
|
attention_mask = input_ids.ne(1).to(torch_device)
|
|
model = MixtralForCausalLM(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(input_ids, attention_mask=attention_mask)
|
|
self.assertEqual(result.router_logits[0].shape, (91, config.num_local_experts))
|
|
torch.testing.assert_close(result.aux_loss.cpu(), torch.tensor(2, dtype=torch.float32), rtol=1e-2, atol=1e-2)
|
|
|
|
# First, we make sure that adding padding tokens doesn't change the loss
|
|
# loss(input_ids, attention_mask=None) == loss(input_ids + padding, attention_mask=attention_mask_with_padding)
|
|
pad_length = 1000
|
|
# Add padding tokens (assume that pad_token_id=1) to input_ids
|
|
padding_block = torch.ones(input_ids.shape[0], pad_length, dtype=torch.int32).to(torch_device)
|
|
padded_input_ids = torch.cat((padding_block, input_ids), dim=1) # this is to simulate padding to the left
|
|
padded_attention_mask = padded_input_ids.ne(1).to(torch_device)
|
|
|
|
padded_result = model(padded_input_ids, attention_mask=padded_attention_mask)
|
|
torch.testing.assert_close(result.aux_loss.cpu(), padded_result.aux_loss.cpu(), rtol=1e-4, atol=1e-4)
|
|
|
|
# We make sure that the loss of including padding tokens != the loss without padding tokens
|
|
# if attention_mask=None --> we don't exclude padding tokens
|
|
include_padding_result = model(padded_input_ids, attention_mask=None)
|
|
|
|
# This is to mimic torch.testing.assert_not_close
|
|
self.assertNotAlmostEqual(include_padding_result.aux_loss.item(), result.aux_loss.item())
|
|
|
|
|
|
@require_torch
|
|
class MixtralIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
@require_torch_accelerator
|
|
def test_small_model_logits(self):
|
|
model_id = "hf-internal-testing/Mixtral-tiny"
|
|
dummy_input = torch.LongTensor([[0, 1, 0], [0, 1, 0]]).to(torch_device)
|
|
|
|
model = MixtralForCausalLM.from_pretrained(
|
|
model_id,
|
|
torch_dtype=torch.bfloat16,
|
|
).to(torch_device)
|
|
# TODO: might need to tweak it in case the logits do not match on our daily runners
|
|
# these logits have been obtained with the original megablocks implementation.
|
|
# ("cuda", 8) for A100/A10, and ("cuda", 7) for T4
|
|
# considering differences in hardware processing and potential deviations in output.
|
|
# fmt: off
|
|
EXPECTED_LOGITS = Expectations(
|
|
{
|
|
("cuda", 7): torch.Tensor([[0.1640, 0.1621, 0.6093], [-0.8906, -0.1640, -0.6093], [0.1562, 0.1250, 0.7226]]).to(torch_device),
|
|
("cuda", 8): torch.Tensor([[0.1631, 0.1621, 0.6094], [-0.8906, -0.1621, -0.6094], [0.1572, 0.1270, 0.7227]]).to(torch_device),
|
|
("rocm", 9): torch.Tensor([[0.1641, 0.1621, 0.6094], [-0.8906, -0.1631, -0.6094], [0.1572, 0.1260, 0.7227]]).to(torch_device),
|
|
}
|
|
)
|
|
# fmt: on
|
|
expected_logit = EXPECTED_LOGITS.get_expectation()
|
|
|
|
with torch.no_grad():
|
|
logits = model(dummy_input).logits
|
|
|
|
logits = logits.float()
|
|
|
|
torch.testing.assert_close(logits[0, :3, :3], expected_logit, atol=1e-3, rtol=1e-3)
|
|
torch.testing.assert_close(logits[1, :3, :3], expected_logit, atol=1e-3, rtol=1e-3)
|
|
|
|
@slow
|
|
@require_torch_accelerator
|
|
def test_small_model_logits_batched(self):
|
|
model_id = "hf-internal-testing/Mixtral-tiny"
|
|
dummy_input = torch.LongTensor([[0, 0, 0, 0, 0, 0, 1, 2, 3], [1, 1, 2, 3, 4, 5, 6, 7, 8]]).to(torch_device)
|
|
attention_mask = dummy_input.ne(0).to(torch.long)
|
|
|
|
model = MixtralForCausalLM.from_pretrained(
|
|
model_id,
|
|
torch_dtype=torch.bfloat16,
|
|
).to(torch_device)
|
|
|
|
# TODO: might need to tweak it in case the logits do not match on our daily runners
|
|
#
|
|
# ("cuda", 8) for A100/A10, and ("cuda", 7) for T4.
|
|
#
|
|
# considering differences in hardware processing and potential deviations in generated text.
|
|
# fmt: off
|
|
EXPECTED_LOGITS_LEFT_UNPADDED = Expectations(
|
|
{
|
|
("xpu", 3): torch.Tensor([[0.2236, 0.5195, -0.3828], [0.8203, -0.2295, 0.6055], [0.2676, -0.7070, 0.2461]]).to(torch_device),
|
|
("cuda", 7): torch.Tensor([[0.2236, 0.5195, -0.3828], [0.8203, -0.2275, 0.6054], [0.2656, -0.7070, 0.2460]]).to(torch_device),
|
|
("cuda", 8): torch.Tensor([[0.2207, 0.5234, -0.3828], [0.8203, -0.2285, 0.6055], [0.2656, -0.7109, 0.2451]]).to(torch_device),
|
|
("rocm", 9): torch.Tensor([[0.2236, 0.5195, -0.3828], [0.8203, -0.2285, 0.6055], [0.2637, -0.7109, 0.2451]]).to(torch_device),
|
|
}
|
|
)
|
|
expected_left_unpadded = EXPECTED_LOGITS_LEFT_UNPADDED.get_expectation()
|
|
|
|
EXPECTED_LOGITS_RIGHT_UNPADDED = Expectations(
|
|
{
|
|
("xpu", 3): torch.Tensor([[0.2178, 0.1270, -0.1641], [-0.3496, 0.2988, -1.0312], [0.0693, 0.7930, 0.8008]]).to(torch_device),
|
|
("cuda", 7): torch.Tensor([[0.2167, 0.1269, -0.1640], [-0.3496, 0.2988, -1.0312], [0.0688, 0.7929, 0.8007]]).to(torch_device),
|
|
("cuda", 8): torch.Tensor([[0.2178, 0.1270, -0.1621], [-0.3496, 0.3008, -1.0312], [0.0693, 0.7930, 0.7969]]).to(torch_device),
|
|
("rocm", 9): torch.Tensor([[0.2197, 0.1250, -0.1611], [-0.3516, 0.3008, -1.0312], [0.0684, 0.7930, 0.8008]]).to(torch_device),
|
|
}
|
|
)
|
|
expected_right_unpadded = EXPECTED_LOGITS_RIGHT_UNPADDED.get_expectation()
|
|
# fmt: on
|
|
|
|
with torch.no_grad():
|
|
logits = model(dummy_input, attention_mask=attention_mask).logits
|
|
logits = logits.float()
|
|
|
|
torch.testing.assert_close(
|
|
logits[0, -3:, -3:],
|
|
expected_left_unpadded,
|
|
atol=1e-3,
|
|
rtol=1e-3,
|
|
)
|
|
torch.testing.assert_close(
|
|
logits[1, -3:, -3:],
|
|
expected_right_unpadded,
|
|
atol=1e-3,
|
|
rtol=1e-3,
|
|
)
|