未验证 提交 ae25ab56 编写于 作者: Y ykkk2333 提交者: GitHub

xpu unittest grad compute supports more types, *test=kunlun (#44606)

上级 5be7a1ff
......@@ -32,13 +32,13 @@ import paddle.fluid.core as core
from paddle.fluid.backward import append_backward
from paddle.fluid.op import Operator
from paddle.fluid.executor import Executor
from paddle.fluid.framework import Program, OpProtoHolder, Variable
from paddle.fluid.framework import Program, OpProtoHolder, Variable, convert_np_dtype_to_dtype_
from testsuite import create_op, set_input, append_input_output, append_loss_ops
from paddle.fluid import unique_name
from white_list import op_accuracy_white_list, check_shape_white_list, compile_vs_runtime_white_list, no_check_set_white_list
from white_list import op_threshold_white_list, no_grad_set_white_list
from op_test import OpTest, _set_use_system_allocator, get_numeric_gradient
from xpu.get_test_cover_info import is_empty_grad_op_type
from xpu.get_test_cover_info import is_empty_grad_op_type, get_xpu_op_support_types, type_dict_str_to_numpy
class XPUOpTest(OpTest):
......@@ -66,6 +66,10 @@ class XPUOpTest(OpTest):
place = paddle.XPUPlace(0)
if core.is_float16_supported(place) == False:
return
if cls.dtype == np.float64:
return
super().tearDownClass()
def _get_places(self):
......@@ -144,6 +148,14 @@ class XPUOpTest(OpTest):
self._check_grad_helper()
return
cast_grad_op_types = get_xpu_op_support_types('cast')
cast_grad_op_types_np = []
for ctype in cast_grad_op_types:
cast_grad_op_types_np.append(type_dict_str_to_numpy[ctype])
if (self.dtype not in cast_grad_op_types_np):
return
if self.dtype == np.float64:
return
......@@ -212,6 +224,11 @@ class XPUOpTest(OpTest):
op_attrs["use_mkldnn"] = False
use_onednn = True
mean_grad_op_types = get_xpu_op_support_types('mean')
mean_grad_op_types_np = []
for mtype in mean_grad_op_types:
mean_grad_op_types_np.append(type_dict_str_to_numpy[mtype])
self.op = create_op(self.scope,
self.op_type,
op_inputs,
......@@ -238,6 +255,68 @@ class XPUOpTest(OpTest):
if not type(output_names) is list:
output_names = [output_names]
if (self.dtype not in mean_grad_op_types_np):
prog = Program()
block = prog.global_block()
scope = core.Scope()
self._append_ops(block)
inputs = self._get_inputs(block)
outputs = self._get_outputs(block)
feed_dict = self.feed_var(inputs, place)
cast_inputs = list(map(block.var, output_names))
cast_outputs = block.create_var(dtype="float32",
shape=cast_inputs[0].shape)
cast_op = block.append_op(type="cast",
inputs={"X": cast_inputs},
outputs={"Out": cast_outputs},
attrs={
"in_dtype":
convert_np_dtype_to_dtype_(
self.dtype),
"out_dtype":
core.VarDesc.VarType.FP32
})
cast_op.desc.infer_var_type(block.desc)
cast_op.desc.infer_shape(block.desc)
output_names = [cast_outputs.name]
loss = append_loss_ops(block, output_names)
loss_names = [loss.name]
recast_inputs = list(map(block.var, loss_names))
recast_loss = block.create_var(dtype=self.dtype,
shape=recast_inputs[0].shape)
recast_op = block.append_op(type="cast",
inputs={"X": recast_inputs},
outputs={"Out": recast_loss},
attrs={
"in_dtype":
core.VarDesc.VarType.FP32,
"out_dtype":
convert_np_dtype_to_dtype_(
self.dtype)
})
recast_op.desc.infer_var_type(block.desc)
recast_op.desc.infer_shape(block.desc)
param_grad_list = append_backward(loss=recast_loss,
parameter_list=[input_to_check],
no_grad_set=no_grad_set)
fetch_list = [g for p, g in param_grad_list]
executor = fluid.Executor(place)
return list(
map(
np.array,
executor.run(prog,
feed_dict,
fetch_list,
scope=scope,
return_numpy=False)))
analytic_grads = self._get_gradient(
inputs_to_check,
place,
......
......@@ -93,10 +93,8 @@ class XPUTestFlatten2Op(XPUOpTestWrapper):
support_types = get_xpu_op_support_types('flatten2')
support_types_for_grad = get_xpu_op_support_types('mean')
for stype in support_types:
if stype in support_types_for_grad:
create_test_class(globals(), XPUTestFlatten2Op, stype)
create_test_class(globals(), XPUTestFlatten2Op, stype)
if __name__ == "__main__":
unittest.main()
......@@ -337,10 +337,8 @@ class TestFlattenPython(unittest.TestCase):
support_types = get_xpu_op_support_types('flatten_contiguous_range')
support_types_for_grad = get_xpu_op_support_types('mean')
for stype in support_types:
if stype in support_types_for_grad:
create_test_class(globals(), XPUTestFlattenOp, stype)
create_test_class(globals(), XPUTestFlattenOp, stype)
if __name__ == "__main__":
unittest.main()
......@@ -87,10 +87,8 @@ class XPUTestFlattenOp(XPUOpTestWrapper):
support_types = get_xpu_op_support_types('flatten')
support_types_for_grad = get_xpu_op_support_types('mean')
for stype in support_types:
if stype in support_types_for_grad:
create_test_class(globals(), XPUTestFlattenOp, stype)
create_test_class(globals(), XPUTestFlattenOp, stype)
if __name__ == "__main__":
unittest.main()
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