Skip to content

[0-size Tensor Job2 No.62] Add 0-size Tensor support for paddle.nn.functional.margin_cross_entropy #354

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
cangtianhuang merged 7 commits into from
Jul 21, 2025
Merged

[0-size Tensor Job2 No.62] Add 0-size Tensor support for paddle.nn.functional.margin_cross_entropy #354

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
1efe8d6
Fix
co63oc Jul 8, 2025
0d6022b
Merge branch 'main' into fix6
co63oc Jul 8, 2025
6fb169c
Fix
co63oc Jul 8, 2025
b01dd8f
Merge branch 'main' into fix6
co63oc Jul 17, 2025
58f70d0
Fix
co63oc Jul 17, 2025
be42210
Merge branch 'main' into fix6
co63oc Jul 19, 2025
1285190
Fix
co63oc Jul 19, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 0 additions & 3 deletions report/0size_tensor_cpu/error_api.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -120,7 +120,6 @@ paddle.multiplex
paddle.mv
paddle.nanmedian
paddle.nn.functional.adaptive_log_softmax_with_loss
paddle.nn.functional.affine_grid
paddle.nn.functional.avg_pool3d
paddle.nn.functional.batch_norm
paddle.nn.functional.binary_cross_entropy
Expand All @@ -131,7 +130,6 @@ paddle.nn.functional.conv2d_transpose
paddle.nn.functional.conv3d
paddle.nn.functional.cosine_similarity
paddle.nn.functional.cross_entropy
paddle.nn.functional.ctc_loss
paddle.nn.functional.dice_loss
paddle.nn.functional.embedding
paddle.nn.functional.gather_tree
Expand All @@ -144,7 +142,6 @@ paddle.nn.functional.kl_div
paddle.nn.functional.layer_norm
paddle.nn.functional.linear
paddle.nn.functional.log_loss
paddle.nn.functional.margin_cross_entropy
paddle.nn.functional.max_pool1d
paddle.nn.functional.max_pool3d
paddle.nn.functional.max_unpool2d
Expand Down
37 changes: 0 additions & 37 deletions report/0size_tensor_cpu/error_config.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5642,11 +5642,6 @@ paddle.nanmedian(Tensor([4, 0],"float64"), keepdim=True, mode="min", )
paddle.nn.functional.adaptive_log_softmax_with_loss(Tensor([128, 16],"float32"), Tensor([0],"int64"), Tensor([16, 8],"float32"), list[list[Tensor([16, 8],"float32"),Tensor([8, 5],"float32"),],list[Tensor([16, 4],"float32"),Tensor([4, 5],"float32"),],list[Tensor([16, 2],"float32"),Tensor([2, 5],"float32"),],], list[5,10,15,20,], None, )
paddle.nn.functional.adaptive_log_softmax_with_loss(Tensor([4, 8],"float32"), Tensor([0],"int64"), Tensor([8, 3],"float32"), list[list[Tensor([8, 4],"float32"),Tensor([4, 2],"float32"),],], list[2,4,], None, )
paddle.nn.functional.adaptive_log_softmax_with_loss(Tensor([8, 8],"float32"), Tensor([0],"int64"), Tensor([8, 3],"float32"), list[list[Tensor([8, 4],"float32"),Tensor([4, 2],"float32"),],], list[2,4,], None, )
paddle.nn.functional.affine_grid(Tensor([1, 2, 3],"float32"), Tensor([0],"int32"), align_corners=True, )
paddle.nn.functional.affine_grid(Tensor([1, 2, 3],"float64"), Tensor([0],"int32"), align_corners=True, )
paddle.nn.functional.affine_grid(Tensor([20, 2, 3],"float32"), Tensor([0],"int64"), align_corners=True, )
paddle.nn.functional.affine_grid(Tensor([4, 2, 3],"float64"), Tensor([0],"int32"), align_corners=False, )
paddle.nn.functional.affine_grid(Tensor([4, 2, 3],"float64"), Tensor([0],"int32"), align_corners=True, )
paddle.nn.functional.avg_pool3d(Tensor([2, 3, 8, 0, 8],"float64"), kernel_size=list[3,3,3,], stride=list[3,2,1,], padding=list[1,2,1,], ceil_mode=False, exclusive=True, divisor_override=None, data_format="NCDHW", name=None, )
paddle.nn.functional.avg_pool3d(x=Tensor([2, 3, 8, 0, 8],"float64"), kernel_size=list[3,3,3,], stride=list[3,2,1,], padding=list[1,2,1,], exclusive=True, )
paddle.nn.functional.batch_norm(Tensor([0, 1, 2, 3],"float32"), Tensor([1],"float32"), Tensor([1],"float32"), Tensor([1],"float32"), Tensor([1],"float32"), training=True, momentum=0.9, epsilon=1e-05, data_format="NCHW", use_global_stats=True, )
Expand Down Expand Up @@ -6426,26 +6421,6 @@ paddle.nn.functional.cross_entropy(input=Tensor([2012, 0],"float32"), label=Tens
paddle.nn.functional.cross_entropy(input=Tensor([2022, 0],"float32"), label=Tensor([2022],"int64"), reduction="mean", )
paddle.nn.functional.cross_entropy(input=Tensor([2036, 0],"float32"), label=Tensor([2036],"int64"), reduction="mean", )
paddle.nn.functional.cross_entropy(input=Tensor([32, 0],"float16"), label=Tensor([32, 1],"int64"), )
paddle.nn.functional.ctc_loss(Tensor([0, 128, 6625],"float32"), Tensor([128, 25],"int32"), Tensor([128],"int64"), Tensor([128],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([0, 2, 3],"float64"), Tensor([2, 3],"int32"), Tensor([2],"int64"), Tensor([2],"int64"), 0, "mean", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([0, 2, 3],"float64"), labels=Tensor([2, 3],"int32"), input_lengths=Tensor([2],"int64"), label_lengths=Tensor([2],"int64"), blank=0, reduction="mean", )
paddle.nn.functional.ctc_loss(Tensor([0, 2, 3],"float64"), labels=Tensor([2, 3],"int32"), input_lengths=Tensor([2],"int64"), label_lengths=Tensor([2],"int64"), blank=0, reduction="none", )
paddle.nn.functional.ctc_loss(Tensor([0, 256, 37],"float32"), Tensor([256, 25],"int32"), Tensor([256],"int64"), Tensor([256],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([0, 256, 96],"float32"), Tensor([256, 25],"int32"), Tensor([256],"int64"), Tensor([256],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([0, 3, 15],"float32"), Tensor([3, 2],"int32"), Tensor([3],"int64"), Tensor([3],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([0, 4, 34],"float32"), Tensor([4, 4],"int32"), Tensor([4],"int64"), Tensor([4],"int64"), blank=33, reduction="mean", )
paddle.nn.functional.ctc_loss(Tensor([0, 4, 34],"float32"), Tensor([4, 4],"int32"), Tensor([4],"int64"), Tensor([4],"int64"), blank=33, reduction="sum", )
paddle.nn.functional.ctc_loss(Tensor([0, 512, 37],"float32"), Tensor([512, 25],"int32"), Tensor([512],"int64"), Tensor([512],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([25, 256, 37],"float32"), Tensor([256, 0],"int32"), Tensor([256],"int64"), Tensor([256],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([25, 256, 96],"float32"), Tensor([256, 0],"int32"), Tensor([256],"int64"), Tensor([256],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([25, 512, 37],"float32"), Tensor([512, 0],"int32"), Tensor([512],"int64"), Tensor([512],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([3, 3, 15],"float32"), Tensor([3, 0],"int32"), Tensor([3],"int64"), Tensor([3],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([4, 4, 34],"float32"), Tensor([4, 0],"int32"), Tensor([4],"int64"), Tensor([4],"int64"), blank=33, reduction="mean", )
paddle.nn.functional.ctc_loss(Tensor([4, 4, 34],"float32"), Tensor([4, 0],"int32"), Tensor([4],"int64"), Tensor([4],"int64"), blank=33, reduction="sum", )
paddle.nn.functional.ctc_loss(Tensor([40, 128, 6625],"float32"), Tensor([128, 0],"int32"), Tensor([128],"int64"), Tensor([128],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([5, 2, 3],"float64"), Tensor([2, 0],"int32"), Tensor([2],"int64"), Tensor([2],"int64"), 0, "mean", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([5, 2, 3],"float64"), labels=Tensor([2, 0],"int32"), input_lengths=Tensor([2],"int64"), label_lengths=Tensor([2],"int64"), blank=0, reduction="mean", )
paddle.nn.functional.ctc_loss(Tensor([5, 2, 3],"float64"), labels=Tensor([2, 0],"int32"), input_lengths=Tensor([2],"int64"), label_lengths=Tensor([2],"int64"), blank=0, reduction="none", )
paddle.nn.functional.dice_loss(Tensor([0, 2],"float64"), label=Tensor([0, 1],"int64"), epsilon=1e-05, )
paddle.nn.functional.dice_loss(Tensor([5, 0],"float64"), label=Tensor([5, 0],"int64"), epsilon=1e-05, )
paddle.nn.functional.dice_loss(Tensor([5, 0],"float64"), label=Tensor([5, 1],"int64"), epsilon=1e-05, )
Expand Down Expand Up @@ -8013,18 +7988,6 @@ paddle.nn.functional.linear(x=Tensor([30, 16],"float32"), weight=Tensor([16, 0],
paddle.nn.functional.linear(x=Tensor([32, 15, 15, 256],"float32"), weight=Tensor([256, 0],"float32"), bias=None, name=None, )
paddle.nn.functional.linear(x=Tensor([38, 16],"float32"), weight=Tensor([16, 0],"float32"), )
paddle.nn.functional.log_loss(Tensor([0, 1],"float32"), Tensor([0, 1],"float32"), epsilon=1e-07, )
paddle.nn.functional.margin_cross_entropy(Tensor([2, 0],"float64"), Tensor([2],"int64"), margin1=1.0, margin2=0.7, margin3=0.2, scale=64.0, return_softmax=True, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([2, 0],"float64"), label=Tensor([2],"int64"), margin1=0.8, margin2=0.0, margin3=0.15, scale=40.0, group=None, return_softmax=False, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([2, 0],"float64"), label=Tensor([2],"int64"), margin1=1.0, margin2=0.5, margin3=0.0, scale=64.0, group=None, return_softmax=False, reduction="mean", )
paddle.nn.functional.margin_cross_entropy(Tensor([2, 0],"float64"), label=Tensor([2],"int64"), margin1=1.0, margin2=0.7, margin3=0.2, scale=32.0, group=None, return_softmax=False, reduction="mean", )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float16"), Tensor([5],"int64"), return_softmax=False, margin1=1.0, margin2=0.5, margin3=0.0, scale=2.0, group=None, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float32"), Tensor([5],"int64"), return_softmax=False, margin1=1.0, margin2=0.5, margin3=0.0, scale=2.0, group=None, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float64"), Tensor([5],"int64"), margin1=1.0, margin2=0.5, margin3=0.0, scale=2.0, return_softmax=True, reduction="mean", )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float64"), Tensor([5],"int64"), margin1=1.0, margin2=0.5, margin3=0.0, scale=2.0, return_softmax=True, reduction="sum", )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float64"), Tensor([5],"int64"), margin1=1.0, margin2=0.5, margin3=0.0, scale=2.0, return_softmax=True, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float64"), Tensor([5],"int64"), return_softmax=False, margin1=1.0, margin2=0.0, margin3=0.35, scale=2.0, group=None, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float64"), Tensor([5],"int64"), return_softmax=False, margin1=1.0, margin2=0.5, margin3=0.0, scale=2.0, group=None, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float64"), Tensor([5],"int64"), return_softmax=False, margin1=1.35, margin2=0.0, margin3=0.0, scale=2.0, group=None, reduction=None, )
paddle.nn.functional.max_pool1d(Tensor([2, 3, 0],"float64"), 2, 1, list[1,1,], False, False, None, )
paddle.nn.functional.max_pool1d(x=Tensor([2, 3, 0],"float64"), kernel_size=2, stride=1, padding=list[1,1,], )
paddle.nn.functional.max_pool3d(Tensor([2, 3, 8, 0, 8],"float64"), kernel_size=list[3,3,3,], stride=list[3,2,1,], padding=list[1,2,1,], return_mask=False, ceil_mode=False, data_format="NCDHW", name=None, )
Expand Down
3 changes: 0 additions & 3 deletions report/0size_tensor_gpu/error_api.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -180,7 +180,6 @@ paddle.nn.functional.adaptive_log_softmax_with_loss
paddle.nn.functional.adaptive_max_pool1d
paddle.nn.functional.adaptive_max_pool2d
paddle.nn.functional.adaptive_max_pool3d
paddle.nn.functional.affine_grid
paddle.nn.functional.avg_pool1d
paddle.nn.functional.avg_pool2d
paddle.nn.functional.avg_pool3d
Expand All @@ -195,7 +194,6 @@ paddle.nn.functional.conv3d
paddle.nn.functional.conv3d_transpose
paddle.nn.functional.cosine_similarity
paddle.nn.functional.cross_entropy
paddle.nn.functional.ctc_loss
paddle.nn.functional.dice_loss
paddle.nn.functional.embedding
paddle.nn.functional.gather_tree
Expand All @@ -213,7 +211,6 @@ paddle.nn.functional.log_loss
paddle.nn.functional.log_softmax
paddle.nn.functional.lp_pool1d
paddle.nn.functional.lp_pool2d
paddle.nn.functional.margin_cross_entropy
paddle.nn.functional.margin_ranking_loss
paddle.nn.functional.max_pool1d
paddle.nn.functional.max_pool2d
Expand Down
37 changes: 0 additions & 37 deletions report/0size_tensor_gpu/error_config.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8310,11 +8310,6 @@ paddle.nn.functional.adaptive_max_pool3d(x=Tensor([0, 3, 5, 7, 7],"float32"), ou
paddle.nn.functional.adaptive_max_pool3d(x=Tensor([0, 3, 5, 7, 7],"float32"), output_size=list[2,3,5,], )
paddle.nn.functional.adaptive_max_pool3d(x=Tensor([0, 3, 5, 7, 7],"float32"), output_size=list[3,3,3,], )
paddle.nn.functional.adaptive_max_pool3d(x=Tensor([0, 3, 5, 7, 7],"float32"), output_size=list[None,3,None,], )
paddle.nn.functional.affine_grid(Tensor([1, 2, 3],"float32"), Tensor([0],"int32"), align_corners=True, )
paddle.nn.functional.affine_grid(Tensor([1, 2, 3],"float64"), Tensor([0],"int32"), align_corners=True, )
paddle.nn.functional.affine_grid(Tensor([20, 2, 3],"float32"), Tensor([0],"int64"), align_corners=True, )
paddle.nn.functional.affine_grid(Tensor([4, 2, 3],"float64"), Tensor([0],"int32"), align_corners=False, )
paddle.nn.functional.affine_grid(Tensor([4, 2, 3],"float64"), Tensor([0],"int32"), align_corners=True, )
paddle.nn.functional.avg_pool1d(Tensor([0, 1, 120],"float32"), 25, 1, 0, True, False, None, )
paddle.nn.functional.avg_pool1d(Tensor([0, 3, 32],"float32"), 2, None, 0, True, False, None, )
paddle.nn.functional.avg_pool1d(Tensor([0, 3, 32],"float32"), kernel_size=2, stride=2, padding="SAME", )
Expand Down Expand Up @@ -9288,26 +9283,6 @@ paddle.nn.functional.cross_entropy(input=Tensor([2012, 0],"float32"), label=Tens
paddle.nn.functional.cross_entropy(input=Tensor([2022, 0],"float32"), label=Tensor([2022],"int64"), reduction="mean", )
paddle.nn.functional.cross_entropy(input=Tensor([2036, 0],"float32"), label=Tensor([2036],"int64"), reduction="mean", )
paddle.nn.functional.cross_entropy(input=Tensor([32, 0],"float16"), label=Tensor([32, 1],"int64"), )
paddle.nn.functional.ctc_loss(Tensor([0, 128, 6625],"float32"), Tensor([128, 25],"int32"), Tensor([128],"int64"), Tensor([128],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([0, 2, 3],"float64"), Tensor([2, 3],"int32"), Tensor([2],"int64"), Tensor([2],"int64"), 0, "mean", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([0, 2, 3],"float64"), labels=Tensor([2, 3],"int32"), input_lengths=Tensor([2],"int64"), label_lengths=Tensor([2],"int64"), blank=0, reduction="mean", )
paddle.nn.functional.ctc_loss(Tensor([0, 2, 3],"float64"), labels=Tensor([2, 3],"int32"), input_lengths=Tensor([2],"int64"), label_lengths=Tensor([2],"int64"), blank=0, reduction="none", )
paddle.nn.functional.ctc_loss(Tensor([0, 256, 37],"float32"), Tensor([256, 25],"int32"), Tensor([256],"int64"), Tensor([256],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([0, 256, 96],"float32"), Tensor([256, 25],"int32"), Tensor([256],"int64"), Tensor([256],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([0, 3, 15],"float32"), Tensor([3, 2],"int32"), Tensor([3],"int64"), Tensor([3],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([0, 4, 34],"float32"), Tensor([4, 4],"int32"), Tensor([4],"int64"), Tensor([4],"int64"), blank=33, reduction="mean", )
paddle.nn.functional.ctc_loss(Tensor([0, 4, 34],"float32"), Tensor([4, 4],"int32"), Tensor([4],"int64"), Tensor([4],"int64"), blank=33, reduction="sum", )
paddle.nn.functional.ctc_loss(Tensor([0, 512, 37],"float32"), Tensor([512, 25],"int32"), Tensor([512],"int64"), Tensor([512],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([25, 256, 37],"float32"), Tensor([256, 0],"int32"), Tensor([256],"int64"), Tensor([256],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([25, 256, 96],"float32"), Tensor([256, 0],"int32"), Tensor([256],"int64"), Tensor([256],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([25, 512, 37],"float32"), Tensor([512, 0],"int32"), Tensor([512],"int64"), Tensor([512],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([3, 3, 15],"float32"), Tensor([3, 0],"int32"), Tensor([3],"int64"), Tensor([3],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([4, 4, 34],"float32"), Tensor([4, 0],"int32"), Tensor([4],"int64"), Tensor([4],"int64"), blank=33, reduction="mean", )
paddle.nn.functional.ctc_loss(Tensor([4, 4, 34],"float32"), Tensor([4, 0],"int32"), Tensor([4],"int64"), Tensor([4],"int64"), blank=33, reduction="sum", )
paddle.nn.functional.ctc_loss(Tensor([40, 128, 6625],"float32"), Tensor([128, 0],"int32"), Tensor([128],"int64"), Tensor([128],"int64"), 0, "none", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([5, 2, 3],"float64"), Tensor([2, 0],"int32"), Tensor([2],"int64"), Tensor([2],"int64"), 0, "mean", norm_by_times=False, )
paddle.nn.functional.ctc_loss(Tensor([5, 2, 3],"float64"), labels=Tensor([2, 0],"int32"), input_lengths=Tensor([2],"int64"), label_lengths=Tensor([2],"int64"), blank=0, reduction="mean", )
paddle.nn.functional.ctc_loss(Tensor([5, 2, 3],"float64"), labels=Tensor([2, 0],"int32"), input_lengths=Tensor([2],"int64"), label_lengths=Tensor([2],"int64"), blank=0, reduction="none", )
paddle.nn.functional.dice_loss(Tensor([0, 2],"float64"), label=Tensor([0, 1],"int64"), epsilon=1e-05, )
paddle.nn.functional.dice_loss(Tensor([5, 0],"float64"), label=Tensor([5, 0],"int64"), epsilon=1e-05, )
paddle.nn.functional.dice_loss(Tensor([5, 0],"float64"), label=Tensor([5, 1],"int64"), epsilon=1e-05, )
Expand Down Expand Up @@ -11201,18 +11176,6 @@ paddle.nn.functional.lp_pool1d(Tensor([2, 3, 0],"float32"), 7.0, 2, None, 1, Tru
paddle.nn.functional.lp_pool1d(Tensor([2, 3, 0],"float32"), norm_type=7, kernel_size=2, stride=2, padding=list[1,], ceil_mode=True, )
paddle.nn.functional.lp_pool2d(Tensor([0, 3, 32, 32],"float16"), 2, kernel_size=3, stride=2, ceil_mode=False, )
paddle.nn.functional.lp_pool2d(Tensor([0, 3, 32, 32],"float16"), norm_type=2.0, kernel_size=3, stride=2, padding=0, ceil_mode=False, data_format="NCHW", name=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([2, 0],"float64"), Tensor([2],"int64"), margin1=1.0, margin2=0.7, margin3=0.2, scale=64.0, return_softmax=True, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([2, 0],"float64"), label=Tensor([2],"int64"), margin1=0.8, margin2=0.0, margin3=0.15, scale=40.0, group=None, return_softmax=False, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([2, 0],"float64"), label=Tensor([2],"int64"), margin1=1.0, margin2=0.5, margin3=0.0, scale=64.0, group=None, return_softmax=False, reduction="mean", )
paddle.nn.functional.margin_cross_entropy(Tensor([2, 0],"float64"), label=Tensor([2],"int64"), margin1=1.0, margin2=0.7, margin3=0.2, scale=32.0, group=None, return_softmax=False, reduction="mean", )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float16"), Tensor([5],"int64"), return_softmax=False, margin1=1.0, margin2=0.5, margin3=0.0, scale=2.0, group=None, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float32"), Tensor([5],"int64"), return_softmax=False, margin1=1.0, margin2=0.5, margin3=0.0, scale=2.0, group=None, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float64"), Tensor([5],"int64"), margin1=1.0, margin2=0.5, margin3=0.0, scale=2.0, return_softmax=True, reduction="mean", )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float64"), Tensor([5],"int64"), margin1=1.0, margin2=0.5, margin3=0.0, scale=2.0, return_softmax=True, reduction="sum", )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float64"), Tensor([5],"int64"), margin1=1.0, margin2=0.5, margin3=0.0, scale=2.0, return_softmax=True, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float64"), Tensor([5],"int64"), return_softmax=False, margin1=1.0, margin2=0.0, margin3=0.35, scale=2.0, group=None, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float64"), Tensor([5],"int64"), return_softmax=False, margin1=1.0, margin2=0.5, margin3=0.0, scale=2.0, group=None, reduction=None, )
paddle.nn.functional.margin_cross_entropy(Tensor([5, 0],"float64"), Tensor([5],"int64"), return_softmax=False, margin1=1.35, margin2=0.0, margin3=0.0, scale=2.0, group=None, reduction=None, )
paddle.nn.functional.margin_ranking_loss(Tensor([0],"float16"), Tensor([0],"float16"), Tensor([0],"float16"), 0.5, "mean", None, )
paddle.nn.functional.max_pool1d(Tensor([0, 1, 2],"float64"), 2, 2, 0, True, False, None, )
paddle.nn.functional.max_pool1d(Tensor([0, 3, 16],"float32"), kernel_size=2, stride=2, return_mask=True, )
Expand Down
Loading