wehub-resource-sync
212 lines
6.5 KiB
Python
212 lines
6.5 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. 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 shutil
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import unittest
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import numpy as np
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from op_test import get_cuda_version
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import paddle
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from paddle.base import core
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from paddle.inference import Config, PrecisionType, create_predictor
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# define the e4m3/e5m2 constants
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E4M3_MAX_POS = 448.0
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E5M2_MAX_POS = 57344.0
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def check_fp8_support() -> bool:
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"""Return if fp8 support is available"""
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gpu_arch = (
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paddle.device.cuda.get_device_capability()[0] * 10
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+ paddle.device.cuda.get_device_capability()[1]
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)
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if gpu_arch >= 90: # hopper and above
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return True
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# Device compute capability 8.9 or higher required for FP8 execution.
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if gpu_arch < 89: # pre-ada
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return False
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if get_cuda_version() < 12010:
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return False
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return True
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class FP16TestNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, input1, input2):
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type = "float8_e4m3fn"
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output = paddle.linalg.fp8_fp8_half_gemm_fused(
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paddle.cast(input1, type),
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paddle.cast(input2, type),
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transpose_x=False,
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transpose_y=True,
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output_dtype="float16",
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)
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return paddle.cast(output, "float32")
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@unittest.skipIf(
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not core.is_compiled_with_cuda() or not check_fp8_support(),
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"Fp8 matmul requires CUDA >= 12.1 on Ada arch or hopper arch",
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)
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class TestFP8FP16Gemm(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.test_model = FP16TestNet()
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self.model_path = "./tmp_fp16_model/"
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self.path_prefix = self.model_path + "model"
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paddle.jit.save(
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self.test_model,
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self.path_prefix,
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input_spec=[
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paddle.static.InputSpec(
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shape=[16, 64], dtype='float32', name="input1"
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),
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paddle.static.InputSpec(
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shape=[32, 64], dtype='float32', name="input2"
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),
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],
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)
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self.x = np.ones([16, 64], np.float32)
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self.y = np.ones([32, 64], np.float32)
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def inference(self):
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# Config
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config = Config(self.path_prefix + ".pdmodel", "")
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config.enable_use_gpu(100, 0, PrecisionType.Float32)
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config.enable_new_executor()
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# predictor
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predictor = create_predictor(config)
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# inference
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input_names = predictor.get_input_names()
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input_tensor_0 = predictor.get_input_handle(input_names[0])
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input_tensor_0.reshape(self.x.shape)
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input_tensor_0.copy_from_cpu(self.x)
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input_tensor_1 = predictor.get_input_handle(input_names[1])
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input_tensor_1.reshape(self.y.shape)
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input_tensor_1.copy_from_cpu(self.y)
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# run
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predictor.run()
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results = []
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# get out data from output tensor
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output_names = predictor.get_output_names()
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for i, name in enumerate(output_names):
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output_tensor = predictor.get_output_handle(name)
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output_data = output_tensor.copy_to_cpu()
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results.append(output_data)
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return results[0]
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def test(self):
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paddle.device.set_device("gpu")
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fp8_out = self.inference()
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fp32_out = np.dot(self.x, np.transpose(self.y))
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np.testing.assert_allclose(fp8_out, fp32_out, rtol=1e-5, atol=1e-5)
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shutil.rmtree(self.model_path)
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class BF16TestNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, input1, input2):
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type = "float8_e4m3fn"
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output = paddle.linalg.fp8_fp8_half_gemm_fused(
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paddle.cast(input1, type),
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paddle.cast(input2, type),
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transpose_x=False,
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transpose_y=True,
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output_dtype="bfloat16",
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)
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return paddle.cast(output, "float32")
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@unittest.skipIf(
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not core.is_compiled_with_cuda() or not check_fp8_support(),
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"Fp8 matmul requires CUDA >= 12.1 on Ada arch or hopper arch",
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)
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class TestFP8BF16Gemm(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.test_model = BF16TestNet()
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self.model_path = "./tmp_fp16_model/"
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self.path_prefix = self.model_path + "model"
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paddle.jit.save(
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self.test_model,
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self.path_prefix,
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input_spec=[
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paddle.static.InputSpec(
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shape=[16, 64], dtype='float32', name="input1"
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),
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paddle.static.InputSpec(
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shape=[32, 64], dtype='float32', name="input2"
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),
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],
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)
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self.x = np.ones([16, 64], np.float32)
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self.y = np.ones([32, 64], np.float32)
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def inference(self):
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# Config
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config = Config(self.path_prefix + ".pdmodel", "")
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config.enable_use_gpu(100, 0, PrecisionType.Float32)
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config.enable_new_executor()
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# predictor
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predictor = create_predictor(config)
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# inference
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input_names = predictor.get_input_names()
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input_tensor_0 = predictor.get_input_handle(input_names[0])
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input_tensor_0.reshape(self.x.shape)
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input_tensor_0.copy_from_cpu(self.x)
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input_tensor_1 = predictor.get_input_handle(input_names[1])
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input_tensor_1.reshape(self.y.shape)
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input_tensor_1.copy_from_cpu(self.y)
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# run
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predictor.run()
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results = []
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# get out data from output tensor
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output_names = predictor.get_output_names()
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for i, name in enumerate(output_names):
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output_tensor = predictor.get_output_handle(name)
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output_data = output_tensor.copy_to_cpu()
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results.append(output_data)
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return results[0]
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def test(self):
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paddle.device.set_device("gpu")
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fp8_out = self.inference()
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fp32_out = np.dot(self.x, np.transpose(self.y))
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np.testing.assert_allclose(fp8_out, fp32_out, rtol=1e-2, atol=1e-2)
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shutil.rmtree(self.model_path)
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if __name__ == "__main__":
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unittest.main()
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