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[PIR]Fix cross_entropy_with_softmax vjp bug && check_grad passed #59451

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merged 1 commit into from
Nov 29, 2023
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[PIR]Fix cross_entropy_with_softmax vjp bug && check_grad passed #59451

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2 changes: 1 addition & 1 deletion paddle/fluid/pir/dialect/op_generator/op_interface_gen.py
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Original file line number Diff line number Diff line change
Expand Up @@ -149,7 +149,7 @@ def gen_op_vjp_str(
index_0 = fwd_outputs_list.index(bw_input_name)
else:
vjp_param_name = 'out_grads'
grad_idx += 1
grad_idx = fwd_outputs_list.index(bw_input_name[:-5])
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@Aurelius84 Aurelius84 Nov 29, 2023 •
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Suggested change
grad_idx = fwd_outputs_list.index(bw_input_name[:-5])
offset = len("_grad")
grad_idx = fwd_outputs_list.index(bw_input_name[:-offset])

这里建议不要直接写 -5，不熟悉背景的其他开发者会比较奇怪这个magic_number 是怎么来的。之前我升级gen.py时遇到过影响其他magic_number导致CI失败问题，排查起来不是很友好。

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好的，后面fix一下

index_0 = grad_idx
if op_grad_info.input_optional_list[idx] == 'true':
if input_type == 'Tensor':
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24 changes: 12 additions & 12 deletions test/legacy_test/test_softmax_with_cross_entropy_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,22 +160,22 @@ def test_check_grad(self):
if core.is_compiled_with_rocm():
if self.python_api is not None:
self.check_grad(
["Logits"], "Loss", max_relative_error=5e-1, check_pir=False
["Logits"], "Loss", max_relative_error=5e-1, check_pir=True
)
# HIP will have accuracy fail when using float32 in CPU place
self.check_grad(
["Logits"], "Loss", max_relative_error=5e-1, check_pir=False
["Logits"], "Loss", max_relative_error=5e-1, check_pir=True
)
else:
if self.python_api is not None:
self.check_grad(
["Logits"],
"Loss",
numeric_grad_delta=0.001,
check_pir=False,
check_pir=True,
)
self.check_grad(
["Logits"], "Loss", numeric_grad_delta=0.001, check_pir=False
["Logits"], "Loss", numeric_grad_delta=0.001, check_pir=True
)


Expand Down Expand Up @@ -517,9 +517,9 @@ def test_check_output(self):

def test_check_grad(self):
if self.python_api is not None:
self.check_grad(["Logits"], "Loss", check_pir=False)
self.check_grad(["Logits"], "Loss", check_pir=True)
self.check_grad(
["Logits"], "Loss", max_relative_error=0.1, check_pir=False
["Logits"], "Loss", max_relative_error=0.1, check_pir=True
)


Expand All @@ -540,10 +540,10 @@ def initParams(self):
def test_check_grad(self):
if self.python_api is not None:
self.check_grad(
["Logits"], "Loss", max_relative_error=0.1, check_pir=False
["Logits"], "Loss", max_relative_error=0.1, check_pir=True
)
self.check_grad(
["Logits"], "Loss", max_relative_error=0.1, check_pir=False
["Logits"], "Loss", max_relative_error=0.1, check_pir=True
)


Expand Down Expand Up @@ -574,15 +574,15 @@ def test_check_grad(self):
# HIP will have accuracy fail when using float32 in CPU place
if self.python_api is not None:
self.check_grad(
["Logits"], "Loss", max_relative_error=0.1, check_pir=False
["Logits"], "Loss", max_relative_error=0.1, check_pir=True
)
self.check_grad(
["Logits"], "Loss", max_relative_error=0.1, check_pir=False
["Logits"], "Loss", max_relative_error=0.1, check_pir=True
)
else:
if self.python_api is not None:
self.check_grad(["Logits"], "Loss", check_pir=False)
self.check_grad(["Logits"], "Loss", check_pir=False)
self.check_grad(["Logits"], "Loss", check_pir=True)
self.check_grad(["Logits"], "Loss", check_pir=True)


class TestSoftmaxWithCrossEntropyOp3(TestSoftmaxWithCrossEntropyOp):
Expand Down