mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
126 lines
5.1 KiB
Python
126 lines
5.1 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import unittest
|
|
|
|
from tensorrt_llm.layers import ColumnLinear, RowLinear
|
|
from tensorrt_llm.mapping import Mapping
|
|
from tensorrt_llm.models import (GPTLMHeadModel, smooth_quantize,
|
|
weight_only_quantize)
|
|
from tensorrt_llm.quantization import QuantMode
|
|
from tensorrt_llm.quantization.layers import (SmoothQuantAttention,
|
|
SmoothQuantLayerNorm,
|
|
SmoothQuantMLP,
|
|
WeightOnlyQuantColumnLinear,
|
|
WeightOnlyQuantRowLinear)
|
|
|
|
|
|
class TestQuant(unittest.TestCase):
|
|
|
|
def test_weight_only_quant(self):
|
|
mode = QuantMode.use_weight_only()
|
|
|
|
model = GPTLMHeadModel(num_layers=2,
|
|
num_heads=12,
|
|
hidden_size=768,
|
|
vocab_size=51200,
|
|
hidden_act='relu',
|
|
max_position_embeddings=1024,
|
|
dtype='float16')
|
|
|
|
quant_model = weight_only_quantize(model, mode)
|
|
|
|
self.assertTrue(hasattr(quant_model, 'quant_mode'))
|
|
|
|
self.assertTrue(
|
|
isinstance(quant_model.layers[0].attention.qkv,
|
|
WeightOnlyQuantColumnLinear))
|
|
self.assertTrue(
|
|
isinstance(quant_model.layers[0].attention.dense,
|
|
WeightOnlyQuantRowLinear))
|
|
self.assertTrue(
|
|
isinstance(quant_model.layers[0].mlp.fc,
|
|
WeightOnlyQuantColumnLinear))
|
|
self.assertTrue(
|
|
isinstance(quant_model.layers[0].mlp.proj,
|
|
WeightOnlyQuantRowLinear))
|
|
|
|
self.assertTrue(
|
|
isinstance(quant_model.layers[1].attention.qkv,
|
|
WeightOnlyQuantColumnLinear))
|
|
self.assertTrue(
|
|
isinstance(quant_model.layers[1].attention.dense,
|
|
WeightOnlyQuantRowLinear))
|
|
self.assertTrue(
|
|
isinstance(quant_model.layers[1].mlp.fc,
|
|
WeightOnlyQuantColumnLinear))
|
|
self.assertTrue(
|
|
isinstance(quant_model.layers[1].mlp.proj,
|
|
WeightOnlyQuantRowLinear))
|
|
|
|
self.assertTrue(isinstance(quant_model.lm_head, ColumnLinear))
|
|
|
|
def test_weight_only_quant_exclude_modules(self):
|
|
mode = QuantMode.use_weight_only()
|
|
|
|
model = GPTLMHeadModel(num_layers=1,
|
|
num_heads=12,
|
|
hidden_size=768,
|
|
vocab_size=51200,
|
|
hidden_act='relu',
|
|
max_position_embeddings=1024,
|
|
dtype='float16')
|
|
|
|
quant_model = weight_only_quantize(model,
|
|
mode,
|
|
exclude_modules=['fc', 'dense'])
|
|
|
|
self.assertTrue(hasattr(quant_model, 'quant_mode'))
|
|
|
|
self.assertTrue(
|
|
isinstance(quant_model.layers[0].attention.qkv,
|
|
WeightOnlyQuantColumnLinear))
|
|
self.assertTrue(
|
|
isinstance(quant_model.layers[0].attention.dense, RowLinear))
|
|
self.assertTrue(isinstance(quant_model.layers[0].mlp.fc, ColumnLinear))
|
|
self.assertTrue(
|
|
isinstance(quant_model.layers[0].mlp.proj,
|
|
WeightOnlyQuantRowLinear))
|
|
self.assertTrue(
|
|
isinstance(quant_model.lm_head, WeightOnlyQuantColumnLinear))
|
|
|
|
def test_convert_GPT_to_smooth_quant(self):
|
|
gpt = GPTLMHeadModel(num_layers=1,
|
|
num_heads=1,
|
|
hidden_size=128,
|
|
vocab_size=1024,
|
|
hidden_act='gelu',
|
|
max_position_embeddings=256,
|
|
dtype='float16',
|
|
mapping=Mapping(world_size=1, rank=0, tp_size=1))
|
|
|
|
quant_mode = QuantMode.use_smooth_quant()
|
|
sq_gpt = smooth_quantize(gpt, quant_mode)
|
|
for layer in sq_gpt.layers:
|
|
assert isinstance(layer.input_layernorm, SmoothQuantLayerNorm)
|
|
assert isinstance(layer.post_layernorm, SmoothQuantLayerNorm)
|
|
assert isinstance(layer.mlp, SmoothQuantMLP)
|
|
assert isinstance(layer.attention, SmoothQuantAttention)
|
|
|
|
assert sq_gpt.quant_mode == quant_mode
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|