Add the support of fp16 in fusion_group (#22239)
Yiqun Liu
6 years ago
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/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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namespace paddle {
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namespace framework {
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namespace ir {
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namespace fusion_group {
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static constexpr char predefined_cuda_functions_fp32[] = R"(
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__device__ inline float real_exp(float x) { return ::expf(x); }
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__device__ inline float real_log(float x) { return ::logf(x); }
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)";
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static constexpr char predefined_cuda_functions_fp64[] = R"(
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__device__ inline double real_exp(double x) { return ::exp(x); }
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__device__ inline double real_log(double x) { return ::log(x); }
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)";
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static constexpr char predefined_cuda_functions_fp16[] = R"(
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__device__ inline float real_exp(float x) { return ::expf(x); }
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__device__ inline float real_log(float x) { return ::logf(x); }
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#define __HALF_TO_US(var) *(reinterpret_cast<unsigned short *>(&(var)))
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#define __HALF_TO_CUS(var) *(reinterpret_cast<const unsigned short *>(&(var)))
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struct __align__(2) __half {
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__device__ __half() { }
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protected:
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unsigned short __x;
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};
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__device__ __half __float2half(const float f) {
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__half val;
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asm("{ cvt.rn.f16.f32 %0, %1; }\n" : "=h"(__HALF_TO_US(val)
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) : "f"(f));
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return val;
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}
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__device__ float __half2float(const __half h) {
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float val;
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asm("{ cvt.f32.f16 %0, %1; }\n" : "=f"(val) : "h"(__HALF_TO_CUS(h)));
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return val;
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}
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#undef __HALF_TO_US
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#undef __HALF_TO_CUS
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typedef __half float16;
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)";
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static constexpr char cuda_kernel_template_1d[] = R"(
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extern "C" __global__ void $func_name($parameters) {
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for(int idx = blockIdx.x * blockDim.x + threadIdx.x;
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idx < N;
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idx += gridDim.x * blockDim.x) {
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$compute_body
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}
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}
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)";
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} // namespace fusion_group
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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@ -1,109 +0,0 @@
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# Copyright (c) 2020 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 unittest
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import numpy as np
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from pass_test import PassTest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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class FusionGroupPassTest(PassTest):
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def setUp(self):
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with fluid.program_guard(self.main_program, self.startup_program):
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data1 = fluid.data(name="data1", shape=[32, 128], dtype="float32")
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data2 = fluid.data(name="data2", shape=[32, 128], dtype="float32")
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data3 = fluid.data(name="data3", shape=[32, 128], dtype="float32")
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tmp_1 = fluid.layers.elementwise_add(data1, data2)
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tmp_2 = fluid.layers.elementwise_mul(data3, tmp_1)
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self.feeds = {
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"data1": np.random.random((32, 128)).astype("float32"),
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"data2": np.random.random((32, 128)).astype("float32"),
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"data3": np.random.random((32, 128)).astype("float32")
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}
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self.fetch_list = [tmp_1, tmp_2]
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self.pass_names = "fusion_group_pass"
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self.fused_op_type = "fusion_group"
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self.num_fused_ops = 1
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def test_check_output(self):
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use_gpu_set = []
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if core.is_compiled_with_cuda():
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use_gpu_set.append(True)
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for use_gpu in use_gpu_set:
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self.pass_attrs = {"fusion_group_pass": {"use_gpu": use_gpu}}
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place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
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self.check_output_with_place(place, startup_on_cpu=False)
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class FusionGroupPassTest1(FusionGroupPassTest):
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def setUp(self):
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with fluid.program_guard(self.main_program, self.startup_program):
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data = []
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for i in range(5):
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data.append(
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fluid.data(
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name=("data" + str(i)),
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shape=[32, 128],
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dtype="float32"))
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tmp_1 = (
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fluid.layers.assign(data[0]) * fluid.layers.sigmoid(data[1])
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) + (fluid.layers.sigmoid(data[2]) * fluid.layers.tanh(data[3]))
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tmp_2 = fluid.layers.tanh(tmp_1) + fluid.layers.sigmoid(data[4])
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self.feeds = {}
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for i in range(5):
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self.feeds["data" + str(i)] = np.random.random(
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(32, 128)).astype("float32")
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self.fetch_list = [tmp_1, tmp_2]
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self.pass_names = "fusion_group_pass"
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self.fused_op_type = "fusion_group"
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self.num_fused_ops = 1
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class FusionGroupPassTest2(FusionGroupPassTest):
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def setUp(self):
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with fluid.program_guard(self.main_program, self.startup_program):
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data = []
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for i in range(3):
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data.append(
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fluid.data(
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name=("data" + str(i)),
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shape=[32, 128],
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dtype="float32"))
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data.append(
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fluid.data(
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name="data3", shape=[128, 32], dtype="float32"))
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tmp_1 = fluid.layers.relu((data[0] - data[1]) * data[2])
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tmp_2 = fluid.layers.sigmoid(data[3])
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tmp_3 = fluid.layers.relu(tmp_2)
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tmp_4 = fluid.layers.mul(tmp_1, tmp_3)
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self.feeds = {}
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for i in range(3):
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self.feeds["data" + str(i)] = np.random.random(
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(32, 128)).astype("float32")
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self.feeds["data3"] = np.random.random((128, 32)).astype("float32")
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self.fetch_list = [tmp_1, tmp_2, tmp_3, tmp_4]
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self.pass_names = "fusion_group_pass"
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self.fused_op_type = "fusion_group"
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self.num_fused_ops = 2
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if __name__ == "__main__":
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unittest.main()
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# Copyright (c) 2020 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 unittest
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import numpy as np
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from pass_test import PassTest
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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import paddle.fluid.core as core
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class FusionGroupPassTest(PassTest):
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def build_program(self, dtype):
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with fluid.program_guard(self.main_program, self.startup_program):
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self.feed_vars = self._prepare_feed_vars([32, 128], dtype, 2)
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self.feed_vars.append(
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fluid.data(
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name="data2", shape=[128, 128], dtype=dtype))
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# subgraph with only 1 op node
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tmp_0 = self.feed_vars[0] * self.feed_vars[1]
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tmp_1 = layers.mul(tmp_0, self.feed_vars[2])
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# subgraph with 2 op nodes
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tmp_2 = layers.relu(tmp_0 + tmp_1)
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self.fetch_list = [tmp_2]
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self.num_fused_ops = 1
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def setUp(self):
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self.build_program("float32")
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self.feeds = self._feed_random_data(self.feed_vars)
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self.pass_names = "fusion_group_pass"
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self.fused_op_type = "fusion_group"
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def _prepare_feed_vars(self, shape, dtype, num_data):
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feed_vars = []
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for i in range(num_data):
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var = fluid.data(name=("data" + str(i)), shape=shape, dtype=dtype)
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feed_vars.append(var)
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return feed_vars
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def _feed_random_data(self, feed_vars):
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feeds = {}
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for var in feed_vars:
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if var.type != fluid.core.VarDesc.VarType.LOD_TENSOR:
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raise TypeError("Feed data of non LoDTensor is not supported.")
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shape = var.shape
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if var.dtype == fluid.core.VarDesc.VarType.FP32:
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dtype = "float32"
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elif var.dtype == fluid.core.VarDesc.VarType.FP64:
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dtype = "float64"
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elif var.dtype == fluid.core.VarDesc.VarType.FP16:
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dtype = "float16"
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else:
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raise ValueError("Unsupported dtype %s" % var.dtype)
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feeds[var.name] = np.random.random(shape).astype(dtype)
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return feeds
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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self.pass_attrs = {"fusion_group_pass": {"use_gpu": True}}
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self.check_output_with_place(fluid.CUDAPlace(0))
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class FusionGroupPassTest1(FusionGroupPassTest):
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def build_program(self, dtype):
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with fluid.program_guard(self.main_program, self.startup_program):
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self.feed_vars = self._prepare_feed_vars([32, 128], dtype, 5)
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tmp_0 = layers.assign(self.feed_vars[0])
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# subgraph with 9 op nodes
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tmp_1 = tmp_0 * layers.sigmoid(self.feed_vars[1]) + layers.sigmoid(
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self.feed_vars[2]) * layers.tanh(self.feed_vars[3])
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tmp_2 = layers.tanh(tmp_1) + layers.sigmoid(self.feed_vars[4])
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self.fetch_list = [tmp_1, tmp_2]
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self.num_fused_ops = 1
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class FusionGroupPassTest2(FusionGroupPassTest):
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def build_program(self, dtype):
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with fluid.program_guard(self.main_program, self.startup_program):
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self.feed_vars = self._prepare_feed_vars([32, 128], dtype, 3)
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self.feed_vars.append(
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fluid.data(
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name="data3", shape=[128, 32], dtype=dtype))
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# subgraph with 3 op nodes
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tmp_1 = layers.relu(
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(self.feed_vars[0] - self.feed_vars[1]) * self.feed_vars[2])
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# subgraph with 2 op nodes
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tmp_2 = layers.relu(layers.sigmoid(self.feed_vars[3]))
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tmp_3 = layers.mul(tmp_1, tmp_2)
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self.fetch_list = [tmp_1, tmp_2, tmp_3]
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self.num_fused_ops = 2
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class FusionGroupPassTestFP64(FusionGroupPassTest):
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def setUp(self):
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self.build_program("float64")
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self.feeds = self._feed_random_data(self.feed_vars)
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self.pass_names = "fusion_group_pass"
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self.fused_op_type = "fusion_group"
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class FusionGroupPassTestFP16(FusionGroupPassTest):
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def build_program(self, dtype):
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with fluid.program_guard(self.main_program, self.startup_program):
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self.feed_vars = self._prepare_feed_vars([32, 128], dtype, 2)
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self.feed_vars.append(
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fluid.data(
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name="data2", shape=[128, 128], dtype=dtype))
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# subgraph with only 1 op node
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tmp_0 = self.feed_vars[0] * self.feed_vars[1]
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tmp_1 = layers.mul(tmp_0, self.feed_vars[2])
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tmp_2 = layers.cast(tmp_0, dtype="float16")
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tmp_3 = layers.cast(tmp_1, dtype="float16")
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# subgraph with 2 op nodes
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tmp_4 = layers.relu(tmp_2 + tmp_3)
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tmp_5 = layers.cast(tmp_4, dtype=dtype)
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self.fetch_list = [tmp_5]
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self.num_fused_ops = 1
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if __name__ == "__main__":
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unittest.main()
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