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[PIR]Perfect unittest for concat / expand / embedding / matmul / reshape / scale / split / sum / uniform_random #58496

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Merged
YuanRisheng merged 4 commits into from
Nov 2, 2023
Merged

[PIR]Perfect unittest for concat / expand / embedding / matmul / reshape / scale / split / sum / uniform_random #58496

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30a1c7b
perfect unittest
YuanRisheng Oct 30, 2023
af63588
resolve conflict
YuanRisheng Oct 30, 2023
756f77c
fix mac3 bugs
YuanRisheng Oct 31, 2023
3aad71c
fix reshape
YuanRisheng Nov 2, 2023
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2 changes: 1 addition & 1 deletion paddle/fluid/framework/new_executor/pir_interpreter.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -126,7 +126,7 @@ PirInterpreter::PirInterpreter(const platform::Place& place,

std::stringstream ss;
ss << this
<< std::chrono::system_clock::to_time_t(std::chrono::system_clock::now());
<< std::chrono::high_resolution_clock::now().time_since_epoch().count();
BuildScope(*ir_block_, ss.str(), value_exe_info_.get());
}

Expand Down
3 changes: 3 additions & 0 deletions paddle/fluid/pybind/eager_utils.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,7 @@ extern PyTypeObject* p_string_tensor_type;

extern PyTypeObject* g_framework_scope_pytype;
extern PyTypeObject* g_ir_opresult_pytype;
extern PyTypeObject* g_ir_value_pytype;
extern PyTypeObject* g_vartype_pytype;
extern PyTypeObject* g_data_type_pytype;
extern PyTypeObject* g_place_pytype;
Expand Down Expand Up @@ -1521,6 +1522,8 @@ pir::Value CastPyArg2Value(PyObject* obj,
size_t arg_pos) {
if (PyObject_TypeCheck(obj, g_ir_opresult_pytype)) {
return ::pybind11::handle(obj).cast<pir::OpResult>();
} else if (PyObject_TypeCheck(obj, g_ir_value_pytype)) {
return ::pybind11::handle(obj).cast<pir::Value>();
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"%s(): argument (position %d) must be "
Expand Down
2 changes: 2 additions & 0 deletions paddle/fluid/pybind/pir.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -80,6 +80,7 @@ namespace paddle {
namespace pybind {

PyTypeObject *g_ir_opresult_pytype = nullptr;
PyTypeObject *g_ir_value_pytype = nullptr;

void BindOpsAPI(pybind11::module *module);

Expand Down Expand Up @@ -410,6 +411,7 @@ void BindValue(py::module *m) {
when build network.

)DOC");
g_ir_value_pytype = reinterpret_cast<PyTypeObject *>(value.ptr());
value
.def(
"get_defining_op",
Expand Down
10 changes: 6 additions & 4 deletions python/paddle/base/executor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@

import numpy as np

from ..pir import OpResult, translate_to_new_ir
from ..pir import OpResult, Value, translate_to_new_ir
from . import compiler, core, framework, get_flags, set_flags, unique_name
from .data_feeder import convert_dtype
from .framework import (
Expand Down Expand Up @@ -513,7 +513,7 @@ def _add_pir_fetch_ops(program, fetch_list, fetch_var_name):
with paddle.static.program_guard(program):
for i, fetch_input in enumerate(fetch_list):
assert isinstance(
fetch_input, OpResult
fetch_input, (OpResult, Value)
), f"Wrong type for fetch_list[{i}]: {type(fetch_input)}"
paddle._pir_ops.fetch(fetch_input, fetch_var_name + str(i), i)

Expand Down Expand Up @@ -1956,7 +1956,9 @@ def _run_inference(self, exe, feed):
return exe.run(feed)

def _check_fetch_list(self, fetch_list):
is_fetch_var = lambda var: isinstance(var, (Variable, str, OpResult))
is_fetch_var = lambda var: isinstance(
var, (Variable, str, OpResult, Value)
)
is_tuple_list = lambda var: isinstance(var, (tuple, list))

if fetch_list is None:
Expand All @@ -1982,7 +1984,7 @@ def _check_fetch_list(self, fetch_list):
res.append(var)
else:
raise TypeError(
"Require fetch_list[{}] 's type shall be one of (Variable, str), but received {}.".format(
"Require fetch_list[{}] 's type shall be one of (OpResult, str), but received {}.".format(
i, type(var).__name__
)
)
Expand Down
58 changes: 55 additions & 3 deletions python/paddle/tensor/manipulation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2037,6 +2037,7 @@ def split(x, num_or_sections, axis=0, name=None):
>>> print(out2.shape)
[3, 3, 5]
"""

input = x
dim = axis
if in_dynamic_mode():
Expand All @@ -2061,15 +2062,32 @@ def split(x, num_or_sections, axis=0, name=None):
else:
return _C_ops.split(input, num_or_sections, dim)
elif in_pir_mode():
if isinstance(dim, paddle.pir.OpResult):
dim.stop_gradient = True
if isinstance(dim, int):
assert len(input.shape) + dim >= 0, "(rank(x) + axis) must >= 0"
dim = (len(input.shape) + dim) if dim < 0 else dim

input_shape = input.shape
if isinstance(num_or_sections, int):
dim = dim if dim >= 0 else dim + len(input.shape)
assert num_or_sections > 0, 'num_or_sections must be than 0.'
if isinstance(dim, int) and input_shape[dim] > 0:
assert input_shape[dim] % num_or_sections == 0, (
"The input's size along the split dimension "
"must be evenly divisible by Attr(num_or_sections). "
"But %d is not evenly divisible by %d. "
% (num_or_sections, input_shape[dim])
)
return _C_ops.split_with_num(input, num_or_sections, dim)
else:
dim = dim if dim >= 0 else dim + len(input.shape)
if isinstance(dim, int) and input_shape[dim] > 0:
assert (
len(num_or_sections) <= input_shape[dim]
), 'len(num_or_sections) must not be more than input.shape[dim].'
if paddle.utils._contain_var(num_or_sections):
num_or_sections = paddle.utils.get_int_tensor_list(
num_or_sections
)
return _C_ops.split(input, num_or_sections, dim)

else:
Expand Down Expand Up @@ -3603,7 +3621,21 @@ def expand(x, shape, name=None):
[[1, 2, 3],
[1, 2, 3]])
"""
if in_dynamic_or_pir_mode():
if in_dynamic_mode():
return _C_ops.expand(x, shape)
elif in_pir_mode():
if convert_dtype(x.dtype) == 'bool' and not x.stop_gradient:
raise ValueError(
"When the data type of input 'x' for expand is bool, "
"you must set its stop_gradient to be False by "
"some_var.stop_gradient = True, supporting "
"some_var as the input."
)
if isinstance(shape, paddle.pir.OpResult):
shape.stop_gradient = True
elif isinstance(shape, (list, tuple)):
if paddle.utils._contain_var(shape):
shape = paddle.utils._convert_to_tensor_list(shape)
return _C_ops.expand(x, shape)
else:
if isinstance(shape, Variable):
Expand Down Expand Up @@ -3798,6 +3830,26 @@ def get_attr_shape(list_shape):
)
return out
elif in_pir_mode():
check_variable_and_dtype(
x,
'x',
[
'float16',
'float32',
'float64',
'int8',
'uint8',
'int16',
'int32',
'int64',
'bool',
'uint16',
],
'reshape',
)
check_type(
shape, 'shape', (list, tuple, paddle.pir.OpResult), 'reshape'
)
if isinstance(shape, (list, tuple)):
if paddle.utils._contain_var(shape):
new_shape = paddle.utils._convert_to_tensor_list(shape)
Expand Down
23 changes: 21 additions & 2 deletions python/paddle/tensor/random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -794,9 +794,28 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)

if in_dynamic_or_pir_mode():
if in_dynamic_mode():
shape = paddle.utils.convert_shape_to_list(shape)
if in_pir_mode() and paddle.utils._contain_var(shape):
return _C_ops.uniform(
shape,
dtype,
float(min),
float(max),
seed,
_current_expected_place(),
)
elif in_pir_mode():
check_type(
shape, 'shape', (list, tuple, paddle.pir.OpResult), 'uniform/rand'
)
check_dtype(dtype, 'dtype', supported_dtypes, 'uniform/rand')
check_type(
min, 'min', (float, int, paddle.pir.OpResult), 'uniform/rand'
)
check_type(
max, 'max', (float, int, paddle.pir.OpResult), 'uniform/rand'
)
if paddle.utils._contain_var(shape):
shape = paddle.utils.get_int_tensor_list(
shape, _current_expected_place()
)
Expand Down
147 changes: 80 additions & 67 deletions test/legacy_test/test_concat_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@
import paddle
from paddle import base
from paddle.base import Program, core, program_guard
from paddle.pir_utils import test_with_pir_api


class TestConcatOp(OpTest):
Expand Down Expand Up @@ -591,76 +592,88 @@ def test_input_same_dtype():


class TestConcatAPI(unittest.TestCase):
@test_with_pir_api
def test_base_api(self):
paddle.enable_static()
x_1 = paddle.static.data(
shape=[None, 1, 4, 5], dtype='int32', name='x_1'
)
paddle.concat([x_1, x_1], 0)

input_2 = np.random.random([2, 1, 4, 5]).astype("int32")
input_3 = np.random.random([2, 2, 4, 5]).astype("int32")
x_2 = paddle.static.data(shape=[2, 1, 4, 5], dtype='int32', name='x_2')
x_3 = paddle.static.data(shape=[2, 2, 4, 5], dtype='int32', name='x_3')
positive_1_int32 = paddle.tensor.fill_constant([1], "int32", 1)
positive_1_int64 = paddle.tensor.fill_constant([1], "int64", 1)
out_1 = paddle.concat([x_2, x_3], axis=1)
out_2 = paddle.concat([x_2, x_3], axis=positive_1_int32)
out_3 = paddle.concat([x_2, x_3], axis=positive_1_int64)

exe = base.Executor(place=base.CPUPlace())
[res_1, res_2, res_3] = exe.run(
base.default_main_program(),
feed={"x_1": input_2, "x_2": input_2, "x_3": input_3},
fetch_list=[out_1, out_2, out_3],
)
np.testing.assert_array_equal(
res_1, np.concatenate((input_2, input_3), axis=1)
)
np.testing.assert_array_equal(
res_2, np.concatenate((input_2, input_3), axis=1)
)
np.testing.assert_array_equal(
res_3, np.concatenate((input_2, input_3), axis=1)
)
with paddle.static.program_guard(paddle.static.Program()):
x_1 = paddle.static.data(
shape=[None, 1, 4, 5], dtype='int32', name='x_1'
)
paddle.concat([x_1, x_1], 0)

input_2 = np.random.random([2, 1, 4, 5]).astype("int32")
input_3 = np.random.random([2, 2, 4, 5]).astype("int32")
x_2 = paddle.static.data(
shape=[2, 1, 4, 5], dtype='int32', name='x_2'
)
x_3 = paddle.static.data(
shape=[2, 2, 4, 5], dtype='int32', name='x_3'
)
positive_1_int32 = paddle.tensor.fill_constant([1], "int32", 1)
positive_1_int64 = paddle.tensor.fill_constant([1], "int64", 1)
out_1 = paddle.concat([x_2, x_3], axis=1)
out_2 = paddle.concat([x_2, x_3], axis=positive_1_int32)
out_3 = paddle.concat([x_2, x_3], axis=positive_1_int64)

exe = base.Executor(place=base.CPUPlace())
[res_1, res_2, res_3] = exe.run(
paddle.static.default_main_program(),
feed={"x_1": input_2, "x_2": input_2, "x_3": input_3},
fetch_list=[out_1, out_2, out_3],
)
np.testing.assert_array_equal(
res_1, np.concatenate((input_2, input_3), axis=1)
)
np.testing.assert_array_equal(
res_2, np.concatenate((input_2, input_3), axis=1)
)
np.testing.assert_array_equal(
res_3, np.concatenate((input_2, input_3), axis=1)
)

@test_with_pir_api
def test_api(self):
paddle.enable_static()
x_1 = paddle.static.data(
shape=[None, 1, 4, 5], dtype='int32', name='x_1'
)
paddle.concat([x_1, x_1], 0)

input_2 = np.random.random([2, 1, 4, 5]).astype("int32")
input_3 = np.random.random([2, 2, 4, 5]).astype("int32")
x_2 = paddle.static.data(shape=[2, 1, 4, 5], dtype='int32', name='x_2')
x_3 = paddle.static.data(shape=[2, 2, 4, 5], dtype='int32', name='x_3')
positive_1_int32 = paddle.tensor.fill_constant([1], "int32", 1)
positive_1_int64 = paddle.tensor.fill_constant([1], "int64", 1)
negative_int64 = paddle.tensor.fill_constant([1], "int64", -3)
out_1 = paddle.concat(x=[x_2, x_3], axis=1)
out_2 = paddle.concat(x=[x_2, x_3], axis=positive_1_int32)
out_3 = paddle.concat(x=[x_2, x_3], axis=positive_1_int64)
out_4 = paddle.concat(x=[x_2, x_3], axis=negative_int64)

exe = paddle.static.Executor(place=paddle.CPUPlace())
[res_1, res_2, res_3, res_4] = exe.run(
paddle.static.default_main_program(),
feed={"x_1": input_2, "x_2": input_2, "x_3": input_3},
fetch_list=[out_1, out_2, out_3, out_4],
)
np.testing.assert_array_equal(
res_1, np.concatenate((input_2, input_3), axis=1)
)
np.testing.assert_array_equal(
res_2, np.concatenate((input_2, input_3), axis=1)
)
np.testing.assert_array_equal(
res_3, np.concatenate((input_2, input_3), axis=1)
)
np.testing.assert_array_equal(
res_4, np.concatenate((input_2, input_3), axis=1)
)
with paddle.static.program_guard(paddle.static.Program()):
x_1 = paddle.static.data(
shape=[None, 1, 4, 5], dtype='int32', name='x_1'
)
paddle.concat([x_1, x_1], 0)

input_2 = np.random.random([2, 1, 4, 5]).astype("int32")
input_3 = np.random.random([2, 2, 4, 5]).astype("int32")
x_2 = paddle.static.data(
shape=[2, 1, 4, 5], dtype='int32', name='x_2'
)
x_3 = paddle.static.data(
shape=[2, 2, 4, 5], dtype='int32', name='x_3'
)
positive_1_int32 = paddle.tensor.fill_constant([1], "int32", 1)
positive_1_int64 = paddle.tensor.fill_constant([1], "int64", 1)
negative_int64 = paddle.tensor.fill_constant([1], "int64", -3)
out_1 = paddle.concat(x=[x_2, x_3], axis=1)
out_2 = paddle.concat(x=[x_2, x_3], axis=positive_1_int32)
out_3 = paddle.concat(x=[x_2, x_3], axis=positive_1_int64)
out_4 = paddle.concat(x=[x_2, x_3], axis=negative_int64)

exe = paddle.static.Executor(place=paddle.CPUPlace())
[res_1, res_2, res_3, res_4] = exe.run(
paddle.static.default_main_program(),
feed={"x_1": input_2, "x_2": input_2, "x_3": input_3},
fetch_list=[out_1, out_2, out_3, out_4],
)
np.testing.assert_array_equal(
res_1, np.concatenate((input_2, input_3), axis=1)
)
np.testing.assert_array_equal(
res_2, np.concatenate((input_2, input_3), axis=1)
)
np.testing.assert_array_equal(
res_3, np.concatenate((input_2, input_3), axis=1)
)
np.testing.assert_array_equal(
res_4, np.concatenate((input_2, input_3), axis=1)
)

def test_imperative(self):
in1 = np.array([[1, 2, 3], [4, 5, 6]])
Expand Down Expand Up @@ -729,8 +742,8 @@ def setUp(self):
def set_program(self, use_base_api):
paddle.enable_static()
if use_base_api:
self.program = base.Program()
with base.program_guard(self.program):
self.program = paddle.static.Program()
with paddle.static.program_guard(self.program):
input = paddle.assign(self.x)
tensor_array = paddle.tensor.create_array(dtype='float32')
zero = paddle.tensor.fill_constant(
Expand Down
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