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[XPU] add bfloat16 support for gaussian and uniform #58662

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merged 3 commits into from
Nov 6, 2023
Merged

[XPU] add bfloat16 support for gaussian and uniform #58662

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cdb8e2c
[XPU] add bfloat16 support for gaussian and uniform
houj04 Nov 3, 2023
1d530a7
fix zero dim.
houj04 Nov 3, 2023
7c1c751
Merge branch 'develop' into 20231103-gaussian-uniform-bfloat16
houj04 Nov 6, 2023
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2 changes: 1 addition & 1 deletion cmake/external/xpu.cmake
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@ set(XPU_XFT_LIB_NAME "libxft.so")
set(XPU_XPTI_LIB_NAME "libxpti.so")

if(NOT DEFINED XPU_BASE_DATE)
set(XPU_BASE_DATE "20231101")
set(XPU_BASE_DATE "20231103")
endif()
set(XPU_XCCL_BASE_VERSION "1.0.53.6")
if(NOT DEFINED XPU_XFT_BASE_VERSION)
Expand Down
9 changes: 7 additions & 2 deletions paddle/phi/backends/xpu/xpu2_op_list.cc
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Expand Up @@ -446,7 +446,9 @@ XPUOpMap& get_kl2_ops() {
phi::DataType::INT64,
phi::DataType::BOOL})},
{"gaussian_random",
XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
XPUKernelSet({phi::DataType::FLOAT32,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16})},
{"gelu_grad",
XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
{"gelu", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
Expand Down Expand Up @@ -977,7 +979,10 @@ XPUOpMap& get_kl2_ops() {
{"update_loss_scaling",
XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
{"unbind", XPUKernelSet({phi::DataType::FLOAT32})},
{"uniform_random", XPUKernelSet({phi::DataType::FLOAT32})},
{"uniform_random",
XPUKernelSet({phi::DataType::FLOAT32,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16})},
{"unique",
XPUKernelSet({phi::DataType::FLOAT32,
phi::DataType::INT32,
Expand Down
9 changes: 7 additions & 2 deletions paddle/phi/backends/xpu/xpu3_op_list.cc
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Expand Up @@ -416,7 +416,9 @@ XPUOpMap& get_kl3_ops() {
phi::DataType::INT64,
phi::DataType::BOOL})},
{"gaussian_random",
XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
XPUKernelSet({phi::DataType::FLOAT32,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16})},
{"gelu_grad",
XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
{"gelu", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
Expand Down Expand Up @@ -942,7 +944,10 @@ XPUOpMap& get_kl3_ops() {
{"update_loss_scaling",
XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
{"unbind", XPUKernelSet({phi::DataType::FLOAT32})},
{"uniform_random", XPUKernelSet({phi::DataType::FLOAT32})},
{"uniform_random",
XPUKernelSet({phi::DataType::FLOAT32,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16})},
{"unique",
XPUKernelSet({phi::DataType::FLOAT32,
phi::DataType::INT32,
Expand Down
3 changes: 2 additions & 1 deletion paddle/phi/kernels/xpu/gaussian_kernel.cc
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Expand Up @@ -50,4 +50,5 @@ PD_REGISTER_KERNEL(gaussian,
ALL_LAYOUT,
phi::GaussianKernel,
float,
phi::dtype::float16) {}
phi::dtype::float16,
phi::dtype::bfloat16) {}
65 changes: 22 additions & 43 deletions paddle/phi/kernels/xpu/uniform_kernel.cc
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Expand Up @@ -14,12 +14,9 @@ limitations under the License. */

#include "paddle/phi/kernels/uniform_kernel.h"

#include <string>

#include "paddle/phi/backends/xpu/xpu_context.h"
#include "paddle/phi/common/memory_utils.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/generator.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/uniform_real_distribution.h"

namespace phi {

Expand All @@ -31,49 +28,31 @@ void UniformKernel(const Context &dev_ctx,
const Scalar &max,
int seed,
DenseTensor *out) {
int diag_num = 0;
int diag_step = 0;
float diag_val = 0.0f;
out->Resize(phi::make_ddim(shape.GetData()));
T *data = dev_ctx.template Alloc<T>(out);
int64_t size = out->numel();

std::unique_ptr<T[]> data_cpu(new T[size]);

std::shared_ptr<std::mt19937_64> engine;
if (seed) {
engine = std::make_shared<std::mt19937_64>();
engine->seed(seed);
} else {
engine = dev_ctx.GetGenerator()->GetCPUEngine();
}
UniformRealDistribution<T>(
data_cpu.get(), size, min.to<float>(), max.to<float>(), engine);
if (diag_num > 0) {
PADDLE_ENFORCE_GT(
size,
(diag_num - 1) * (diag_step + 1),
phi::errors::InvalidArgument(
"ShapeInvalid: the diagonal's elements is equal (num-1) "
"* (step-1) with num %d, step %d,"
"It should be smaller than %d, but received %d",
diag_num,
diag_step,
(diag_num - 1) * (diag_step + 1),
size));
for (int64_t i = 0; i < diag_num; ++i) {
int64_t pos = i * diag_step + i;
data_cpu[pos] = diag_val;
}
if (out->numel() == 0) {
return;
}

memory_utils::Copy(dev_ctx.GetPlace(),
data,
phi::CPUPlace(),
reinterpret_cast<void *>(data_cpu.get()),
size * sizeof(T));
using XPUType = typename XPUTypeTrait<T>::Type;
int64_t real_seed = seed != 0 ? seed : dev_ctx.GetGenerator()->Random64();

// int random(Context* ctx, T* x, int64_t len, T min, T max, int64_t seed);
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cpu的分支是否需要保留？
一个需求是用相同的初始化seed，然后xpu/cpu对比输出，xpu::random的结果和cpu能对齐？

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@houj04 houj04 Nov 6, 2023 •
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这种场景下面用export XPU_BLACK_LIST="uniform_random,gaussian_random"的办法解决？
目前gaussian_random也没有保留“从CPU拷贝”的功能。

int r = xpu::random<XPUType>(dev_ctx.x_context(),
reinterpret_cast<XPUType *>(data),
out->numel(),
static_cast<XPUType>(min.to<float>()),
static_cast<XPUType>(max.to<float>()),
real_seed);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "random");
}

} // namespace phi

PD_REGISTER_KERNEL(uniform, XPU, ALL_LAYOUT, phi::UniformKernel, float) {}
PD_REGISTER_KERNEL(uniform,
XPU,
ALL_LAYOUT,
phi::UniformKernel,
float,
phi::dtype::float16,
phi::dtype::bfloat16) {}
34 changes: 34 additions & 0 deletions test/xpu/test_gaussian_random_op_xpu.py
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Expand Up @@ -26,8 +26,23 @@
from paddle import base

paddle.enable_static()
from paddle.base import core
from paddle.tensor import random

typeid_dict = {
'int32': int(core.VarDesc.VarType.INT32),
'int64': int(core.VarDesc.VarType.INT64),
'float32': int(core.VarDesc.VarType.FP32),
'float16': int(core.VarDesc.VarType.FP16),
'bfloat16': int(core.VarDesc.VarType.BF16),
'bool': int(core.VarDesc.VarType.BOOL),
'int8': int(core.VarDesc.VarType.INT8),
'uint8': int(core.VarDesc.VarType.UINT8),
'float64': int(core.VarDesc.VarType.FP64),
}

from op_test import convert_uint16_to_float


class XPUTestGaussianRandomOp(XPUOpTestWrapper):
def __init__(self):
Expand All @@ -52,6 +67,7 @@ def setUp(self):
"std": self.std,
"seed": 10,
"use_mkldnn": self.use_mkldnn,
"dtype": typeid_dict[self.in_type_str],
}
paddle.seed(10)

Expand All @@ -67,6 +83,10 @@ def test_check_output(self):
)

def verify_output(self, outs):
# special for bf16
if self.in_type_str == "bfloat16":
outs = convert_uint16_to_float(outs)

self.assertEqual(outs[0].shape, (123, 92))
hist, _ = np.histogram(outs[0], range=(-3, 5))
hist = hist.astype("float32")
Expand Down Expand Up @@ -100,6 +120,7 @@ def setUp(self):
'std': self.std,
'seed': self.seed,
'use_mkldnn': self.use_mkldnn,
"dtype": typeid_dict[self.in_type_str],
}

self.inputs = {"ShapeTensorList": shape_tensor_list}
Expand Down Expand Up @@ -165,6 +186,7 @@ def setUp(self):
'std': self.std,
'seed': self.seed,
'use_mkldnn': self.use_mkldnn,
"dtype": typeid_dict[self.in_type_str],
}
self.outputs = {'Out': np.zeros((123, 92), dtype=self.dtype)}

Expand Down Expand Up @@ -265,6 +287,11 @@ def test_default_fp16():
out = paddle.tensor.random.gaussian([2, 3])
self.assertEqual(out.dtype, base.core.VarDesc.VarType.FP16)

def test_default_bf16():
paddle.framework.set_default_dtype('bfloat16')
out = paddle.tensor.random.gaussian([2, 3])
self.assertEqual(out.dtype, base.core.VarDesc.VarType.BF16)

def test_default_fp32():
paddle.framework.set_default_dtype('float32')
out = paddle.tensor.random.gaussian([2, 3])
Expand All @@ -278,6 +305,7 @@ def test_default_fp64():
test_default_fp64()
test_default_fp32()
test_default_fp16()
test_default_bf16()

paddle.enable_static()

Expand All @@ -291,6 +319,11 @@ def test_default_fp16():
out = paddle.tensor.random.standard_normal([2, 3])
self.assertEqual(out.dtype, base.core.VarDesc.VarType.FP16)

def test_default_bf16():
paddle.framework.set_default_dtype('bfloat16')
out = paddle.tensor.random.standard_normal([2, 3])
self.assertEqual(out.dtype, base.core.VarDesc.VarType.BF16)

def test_default_fp32():
paddle.framework.set_default_dtype('float32')
out = paddle.tensor.random.standard_normal([2, 3])
Expand All @@ -304,6 +337,7 @@ def test_default_fp64():
test_default_fp64()
test_default_fp32()
test_default_fp16()
test_default_bf16()

paddle.enable_static()

Expand Down
99 changes: 82 additions & 17 deletions test/xpu/test_uniform_random_op_xpu.py
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@@ -1,4 +1,4 @@
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2023 PaddlePaddle Authors. 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.
Expand All @@ -16,32 +16,97 @@
import unittest

import numpy as np
from test_uniform_random_op import (
TestUniformRandomOp,
TestUniformRandomOpSelectedRows,
from get_test_cover_info import (
XPUOpTestWrapper,
create_test_class,
get_xpu_op_support_types,
)
from op_test_xpu import XPUOpTest

import paddle

paddle.enable_static()
from paddle.base import core

typeid_dict = {
'int32': int(core.VarDesc.VarType.INT32),
'int64': int(core.VarDesc.VarType.INT64),
'float32': int(core.VarDesc.VarType.FP32),
'float16': int(core.VarDesc.VarType.FP16),
'bfloat16': int(core.VarDesc.VarType.BF16),
'bool': int(core.VarDesc.VarType.BOOL),
'int8': int(core.VarDesc.VarType.INT8),
'uint8': int(core.VarDesc.VarType.UINT8),
'float64': int(core.VarDesc.VarType.FP64),
}

class TestXPUUniformRandomOp(TestUniformRandomOp):
def test_check_output(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
outs = self.calc_output(place)
outs = [np.array(out) for out in outs]
outs.sort(key=len)
self.verify_output(outs)

def output_hist(out):
if out.dtype == np.uint16:
out = convert_uint16_to_float(out)
hist, _ = np.histogram(out, range=(-5, 10))
hist = hist.astype("float32")
hist /= float(out.size)
prob = 0.1 * np.ones(10)
return hist, prob

class TestXPUUniformRandomOpSelectedRows(TestUniformRandomOpSelectedRows):
def test_check_output(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_with_place(place)

from op_test import convert_uint16_to_float


class XPUTestUniformRandomOp(XPUOpTestWrapper):
def __init__(self):
self.op_name = 'uniform_random'
self.use_dynamic_create_class = False

class TestUniformRandomOp(XPUOpTest):
def init(self):
self.dtype = self.in_type
self.place = paddle.XPUPlace(0)
self.op_type = "uniform_random"
self.python_api = paddle.uniform

def setUp(self):
self.init()
self.inputs = {}
self.use_mkldnn = False
self.set_attrs()
paddle.seed(10)

self.outputs = {"Out": np.zeros((1000, 784), dtype=self.dtype)}

def set_attrs(self):
self.attrs = {
"shape": [1000, 784],
"min": -5.0,
"max": 10.0,
"dtype": typeid_dict[self.in_type_str],
}
self.output_hist = output_hist

def test_check_output(self):
self.check_output_with_place_customized(
self.verify_output, self.place
)

def verify_output(self, outs):
hist, prob = self.output_hist(np.array(outs[0]))
np.testing.assert_allclose(hist, prob, rtol=0, atol=0.01)

class TestMaxMinAreInt(TestUniformRandomOp):
def set_attrs(self):
self.attrs = {
"shape": [1000, 784],
"min": -5,
"max": 10,
"dtype": typeid_dict[self.in_type_str],
}
self.output_hist = output_hist


support_types = get_xpu_op_support_types('uniform_random')
for stype in support_types:
create_test_class(globals(), XPUTestUniformRandomOp, stype)

if __name__ == "__main__":
unittest.main()