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Grandfather in some more pytorch ops to be pt2_compliant (#113050)
We're not directly testing these, but in general the policy is to assume that PyTorch ops inside the pytorch repo are compliant. Test Plan: - existing tests Pull Request resolved: https://github.com/pytorch/pytorch/pull/113050 Approved by: https://github.com/ezyang
This commit is contained in:
rzou
PyTorch MergeBot
parent
e2236ae097
commit
c88a36ebce
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@ -19,143 +19,143 @@ TORCH_LIBRARY(quantized, m) {
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register_conv_params<3>();
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register_embedding_params();
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add.Scalar(Tensor qa, Scalar b) -> Tensor qc"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add.Scalar2(Scalar b, Tensor qa) -> Tensor qc"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu.Scalar(Tensor qa, Scalar b) -> Tensor qc"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu.Scalar2(Scalar b, Tensor qa) -> Tensor qc"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add.Scalar(Tensor qa, Scalar b) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add.Scalar2(Scalar b, Tensor qa) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu.Scalar(Tensor qa, Scalar b) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu.Scalar2(Scalar b, Tensor qa) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu.out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
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// deprecated functions, kept for backward compatibility
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar(Tensor qa, Scalar b) -> Tensor qc"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_relu(Tensor qa, Scalar b) -> Tensor qc"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_relu_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_relu_out(Tensor qa, Tensor qb, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar(Tensor qa, Scalar b) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_relu(Tensor qa, Scalar b) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_relu_out(Tensor qa, Scalar b, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
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// TODO: remove after broadcasting is supported
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar.Tensor(Tensor qa, Tensor b) -> Tensor qc"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_relu.Tensor(Tensor qa, Tensor b) -> Tensor qc"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_relu_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar.Tensor(Tensor qa, Tensor b) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_relu.Tensor(Tensor qa, Tensor b) -> Tensor qc"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::add_scalar_relu_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out) -> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
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// This is needed for graph mode quantization, when we fuse
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// dequant - aten::batch_norm - quant into quantized::batch_norm
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// and dimension is unknown given only the aten op call
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// quantized::batch_norm supports both 2d and 3d batch norm right now
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// it should also support 1d batch_norm after quantized::batch_norm1d is
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// implemented
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm1d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm1d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm2d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm2d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm3d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm3d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::clamp(Tensor qx, Scalar? min=None, Scalar? max=None) -> Tensor qy"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::threshold(Tensor qx, Scalar threshold, Scalar value) -> Tensor qy"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::cat(Tensor[] qx, int dim, float? scale, int? zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::cat_relu(Tensor[] qx, int dim, float? scale, int? zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::cat_out(Tensor[] qx, int dim, Tensor(a!) out) -> Tensor(a!)"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::cat_relu_out(Tensor[] qx, int dim, Tensor(a!) out) -> Tensor(a!)"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d.new(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_relu.new(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_add(Tensor qx, Tensor qaccum, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_add_relu(Tensor qx, Tensor qaccum, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d.new(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_relu.new(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm1d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm1d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm2d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm2d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm3d(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::batch_norm3d_relu(Tensor qx, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::clamp(Tensor qx, Scalar? min=None, Scalar? max=None) -> Tensor qy"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::threshold(Tensor qx, Scalar threshold, Scalar value) -> Tensor qy"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::cat(Tensor[] qx, int dim, float? scale, int? zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::cat_relu(Tensor[] qx, int dim, float? scale, int? zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::cat_out(Tensor[] qx, int dim, Tensor(a!) out) -> Tensor(a!)"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::cat_relu_out(Tensor[] qx, int dim, Tensor(a!) out) -> Tensor(a!)"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d.new(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_relu.new(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_add(Tensor qx, Tensor qaccum, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_add_relu(Tensor qx, Tensor qaccum, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d.new(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_relu.new(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_relu(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase weight, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
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// conv_prepack is deprecated, please use conv2d_prepack for 2D conv.
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv3dPackedParamsBase"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv3dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
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// conv_unpack is deprecated, please use conv2d_unpack for 2D conv.
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_unpack_sizes(Any packed_weights) -> (Any)"));
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m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_unpack(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_stride(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_output_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_dilation(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_groups(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_transpose(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_stride(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_padding(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_output_padding(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_dilation(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_groups(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_transpose(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv1d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_unpack_sizes(Any packed_weights) -> (Any)"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_unpack(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_stride(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_output_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_dilation(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_groups(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv2d_transpose(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_stride(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_padding(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_output_padding(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_dilation(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_groups(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv3d_transpose(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
|
||||
// conv_tranpsose
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose1d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose1d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose1d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose1d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_stride(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_output_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_dilation(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_groups(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_transpose(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv3dPackedParamsBase"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_unpack(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_stride(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_padding(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_output_padding(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_dilation(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_groups(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_transpose(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose1d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose1d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_dynamic(Tensor qx, __torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weight, bool reduce_range=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose1d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose1d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv2dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_unpack(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_stride(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_output_padding(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_dilation(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_groups(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose2d_transpose(__torch__.torch.classes.quantized.Conv2dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_prepack(Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] output_padding, int[] dilation, int groups) -> __torch__.torch.classes.quantized.Conv3dPackedParamsBase"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_unpack(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> (Tensor unpacked_weights, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_stride(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_padding(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_output_padding(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_dilation(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int[]"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_groups(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::conv_transpose3d_transpose(__torch__.torch.classes.quantized.Conv3dPackedParamsBase packed_weights) -> int"), {at::Tag::pt2_compliant_tag});
|
||||
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::elu(Tensor self, float output_scale, int output_zero_point, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::dropout(Tensor self, float output_scale, int output_zero_point, Scalar p=0.5, bool training=False) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_prepack(Tensor weight) -> __torch__.torch.classes.quantized.EmbeddingPackedParamsBase W_prepack"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_unpack(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase W_prepack) -> Tensor W_origin"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte_prepack(Tensor weight) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte_unpack(Tensor weight) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit_prepack(Tensor weight, bool optimized_qparams=False, int nbins=200, float ratio=0.16) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit_unpack(Tensor weight) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_2bit_prepack(Tensor weight, bool optimized_qparams=False, int nbins=200, float ratio=0.16) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_2bit_unpack(Tensor weight) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_2bit_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_byte(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, bool pruned_weights=False) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_4bit(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, bool pruned_weights=False) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::celu(Tensor self, float output_scale, int output_zero_point, Scalar alpha=1) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::group_norm(Tensor input, int num_groups, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::hardswish(Tensor input, float output_scale, int output_zero_point) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::instance_norm(Tensor input, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::layer_norm(Tensor input, int[] normalized_shape, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_relu(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_dynamic(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, bool reduce_range=False) -> Tensor Y"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_relu_dynamic(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, bool reduce_range=False) -> Tensor Y"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_dynamic_fp16(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> Tensor Y"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_relu_dynamic_fp16(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> Tensor Y"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_leaky_relu(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i, float negative_slope) -> Tensor Y"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_tanh(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::elu(Tensor self, float output_scale, int output_zero_point, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::dropout(Tensor self, float output_scale, int output_zero_point, Scalar p=0.5, bool training=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_prepack(Tensor weight) -> __torch__.torch.classes.quantized.EmbeddingPackedParamsBase W_prepack"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_unpack(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase W_prepack) -> Tensor W_origin"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte_prepack(Tensor weight) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte_unpack(Tensor weight) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit_prepack(Tensor weight, bool optimized_qparams=False, int nbins=200, float ratio=0.16) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit_unpack(Tensor weight) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_2bit_prepack(Tensor weight, bool optimized_qparams=False, int nbins=200, float ratio=0.16) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_2bit_unpack(Tensor weight) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_2bit_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_byte(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, bool pruned_weights=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_4bit(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase weight, Tensor indices, bool pruned_weights=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::celu(Tensor self, float output_scale, int output_zero_point, Scalar alpha=1) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::group_norm(Tensor input, int num_groups, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::hardswish(Tensor input, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::instance_norm(Tensor input, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::layer_norm(Tensor input, int[] normalized_shape, Tensor? weight, Tensor? bias, float eps, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_relu(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_dynamic(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, bool reduce_range=False) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_relu_dynamic(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, bool reduce_range=False) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_dynamic_fp16(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_relu_dynamic_fp16(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_leaky_relu(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i, float negative_slope) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_tanh(Tensor X, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack, float Y_scale_i, int Y_zero_point_i) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
|
||||
// Corresponding pattern (the ops with `*` are part of the pattern that
|
||||
// represents the computation of quantized::linear_with_input_q_dq_qweight_dq_output_fp32):
|
||||
// input -> q* -> dq* -> linear* ->
|
||||
|
|
@ -171,7 +171,7 @@ TORCH_LIBRARY(quantized, m) {
|
|||
// W_prepack: packed qint8 quantized weight and bias
|
||||
// Returns:
|
||||
// Y: float32 Tensor
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_with_input_q_dq_qweight_dq_output_fp32(Tensor X, float X_scale, int X_zero_point, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> Tensor Y"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_with_input_q_dq_qweight_dq_output_fp32(Tensor X, float X_scale, int X_zero_point, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
|
||||
// Corresponding pattern (the ops with `*` are part of the pattern that
|
||||
// represents the computation of quantized::linear_with_input_q_dq_qweight_dq_relu_output_fp32):
|
||||
// input -> q* -> dq* -> linear* -> relu* ->
|
||||
|
|
@ -187,45 +187,45 @@ TORCH_LIBRARY(quantized, m) {
|
|||
// W_prepack: packed qint8 quantized weight and bias
|
||||
// Returns:
|
||||
// Y: float32 Tensor
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_with_input_q_dq_qweight_dq_relu_output_fp32(Tensor X, float X_scale, int X_zero_point, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> Tensor Y"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_prepack(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_prepack_fp16(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_prepack_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_prepack_fp16_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_unpack(__torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> (Tensor W_origin, Tensor? B_origin)"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_unpack_fp16(__torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> (Tensor W_origin, Tensor? B_origin)"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_unpack.legacy(Tensor W_prepack) -> (Tensor W_origin, Tensor? B_origin)"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_unpack_fp16.legacy(Tensor W_prepack) -> (Tensor W_origin, Tensor? B_origin)"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::matmul(Tensor qa, Tensor qb, float scale, int zero_point)-> Tensor qc"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul(Tensor qa, Tensor qb, float scale, int zero_point)-> Tensor qc"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul.out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul.Scalar(Tensor qa, Scalar b)-> Tensor qc"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul.Scalar2(Scalar b, Tensor qa)-> Tensor qc"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu(Tensor qa, Tensor qb, float scale, int zero_point)-> Tensor qc"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu.out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu.Scalar(Tensor qa, Scalar b)-> Tensor qc"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu.Scalar2(Scalar b, Tensor qa)-> Tensor qc"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_with_input_q_dq_qweight_dq_relu_output_fp32(Tensor X, float X_scale, int X_zero_point, __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> Tensor Y"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_prepack(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_prepack_fp16(Tensor W, Tensor? B=None) -> __torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_prepack_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_prepack_fp16_legacy(Tensor W, Tensor? B=None) -> Tensor W_prepack"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_unpack(__torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> (Tensor W_origin, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_unpack_fp16(__torch__.torch.classes.quantized.LinearPackedParamsBase W_prepack) -> (Tensor W_origin, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_unpack.legacy(Tensor W_prepack) -> (Tensor W_origin, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::linear_unpack_fp16.legacy(Tensor W_prepack) -> (Tensor W_origin, Tensor? B_origin)"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::matmul(Tensor qa, Tensor qb, float scale, int zero_point)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul(Tensor qa, Tensor qb, float scale, int zero_point)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul.out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul.Scalar(Tensor qa, Scalar b)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul.Scalar2(Scalar b, Tensor qa)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu(Tensor qa, Tensor qb, float scale, int zero_point)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu.out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu.Scalar(Tensor qa, Scalar b)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu.Scalar2(Scalar b, Tensor qa)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu.Scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
|
||||
// deprecated functions, kept for backward compatibility
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu_out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar(Tensor qa, Scalar b)-> Tensor qc"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_relu(Tensor qa, Scalar b)-> Tensor qc"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_relu_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_relu_out(Tensor qa, Tensor qb, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar(Tensor qa, Scalar b)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_relu(Tensor qa, Scalar b)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_relu_out(Tensor qa, Scalar b, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
|
||||
// TODO: remove after broadcasting is supported
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar.Tensor(Tensor qa, Tensor b)-> Tensor qc"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_relu.Tensor(Tensor qa, Tensor b)-> Tensor qc"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out)-> Tensor(a!) out"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_relu_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out)-> Tensor(a!) out"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::max_pool1d(Tensor qx, int[] kernel_size, int[] stride, int[] padding, int[] dilation, bool ceil_mode) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::max_pool2d(Tensor qx, int[] kernel_size, int[] stride, int[] padding, int[] dilation, bool ceil_mode) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::relu6(Tensor qx, bool inplace=False) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::leaky_relu(Tensor qx, Scalar negative_slope, bool inplace, float output_scale, int output_zero_point) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::prelu(Tensor qx, Tensor weight, float output_scale, int output_zero_point) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::sigmoid(Tensor qx, float output_scale, int output_zero_point) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::softmax(Tensor qx, int dim, float output_scale, int output_zero_point) -> Tensor"));
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar.Tensor(Tensor qa, Tensor b)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_relu.Tensor(Tensor qa, Tensor b)-> Tensor qc"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::mul_scalar_relu_out.Tensor(Tensor qa, Tensor b, Tensor(a!) out)-> Tensor(a!) out"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::max_pool1d(Tensor qx, int[] kernel_size, int[] stride, int[] padding, int[] dilation, bool ceil_mode) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::max_pool2d(Tensor qx, int[] kernel_size, int[] stride, int[] padding, int[] dilation, bool ceil_mode) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::relu6(Tensor qx, bool inplace=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::leaky_relu(Tensor qx, Scalar negative_slope, bool inplace, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::prelu(Tensor qx, Tensor weight, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::sigmoid(Tensor qx, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
m.def(TORCH_SELECTIVE_SCHEMA("quantized::softmax(Tensor qx, int dim, float output_scale, int output_zero_point) -> Tensor"), {at::Tag::pt2_compliant_tag});
|
||||
}
|
||||
|
||||
// According to #33294: The "_" prefix registration will be
|
||||
|
|
|
|||
|
|
@ -423,7 +423,9 @@ class TestDeserialize(TestCase):
|
|||
def test_tensor_tensor_list(self):
|
||||
from torch.library import Library
|
||||
lib = Library("_export", "FRAGMENT")
|
||||
lib.define("_test_tensor_tensor_list_output(Tensor x, Tensor y) -> (Tensor, Tensor[])")
|
||||
lib.define(
|
||||
"_test_tensor_tensor_list_output(Tensor x, Tensor y) -> (Tensor, Tensor[])",
|
||||
tags=torch.Tag.pt2_compliant_tag)
|
||||
|
||||
def _test_tensor_tensor_list_output(x, y):
|
||||
return y, [x]
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from torch import Tensor
|
|||
from torch.autograd import Function
|
||||
|
||||
_test_lib_def = torch.library.Library("_inductor_test", "DEF")
|
||||
_test_lib_def.define("realize(Tensor self) -> Tensor")
|
||||
_test_lib_def.define("realize(Tensor self) -> Tensor", tags=torch.Tag.pt2_compliant_tag)
|
||||
|
||||
_test_lib_impl = torch.library.Library("_inductor_test", "IMPL")
|
||||
for dispatch_key in ("CPU", "CUDA", "Meta"):
|
||||
|
|
|
|||
|
|
@ -271,7 +271,7 @@ def _make_prim(
|
|||
|
||||
"""
|
||||
|
||||
prim.define(schema)
|
||||
prim.define(schema, tags=torch.Tag.pt2_compliant_tag)
|
||||
|
||||
def _prim_impl(*args, **kwargs):
|
||||
# always run the meta function because aten implementation will
|
||||
|
|
|
|||
|
|
@ -730,20 +730,20 @@ class TORCH_API Library final {
|
|||
// These overloads cover cases when a SelectiveStr (see Note [Selective
|
||||
// build]) has been disabled at compile time. In that case, don't generate
|
||||
// any code referencing the passed in functions at all.
|
||||
Library& def(detail::SelectiveStr<false>) & {
|
||||
Library& def(detail::SelectiveStr<false>, const std::vector<at::Tag>& tags = {}) & {
|
||||
return *this;
|
||||
}
|
||||
Library& def(detail::SelectiveStr<true> raw_schema) & {
|
||||
return def(raw_schema.operator const char*());
|
||||
Library& def(detail::SelectiveStr<true> raw_schema, const std::vector<at::Tag>& tags = {}) & {
|
||||
return def(raw_schema.operator const char*(), tags);
|
||||
}
|
||||
template <typename Func>
|
||||
Library& def(detail::SelectiveStr<false>, Func&& /*raw_f*/) & {
|
||||
Library& def(detail::SelectiveStr<false>, Func&& /*raw_f*/, const std::vector<at::Tag>& tags = {}) & {
|
||||
return *this;
|
||||
}
|
||||
template <typename Func>
|
||||
Library& def(detail::SelectiveStr<true> raw_name_or_schema, Func&& raw_f) & {
|
||||
Library& def(detail::SelectiveStr<true> raw_name_or_schema, Func&& raw_f, const std::vector<at::Tag>& tags = {}) & {
|
||||
return def(
|
||||
raw_name_or_schema.operator const char*(), std::forward<Func>(raw_f));
|
||||
raw_name_or_schema.operator const char*(), std::forward<Func>(raw_f), tags);
|
||||
}
|
||||
|
||||
template <typename Func>
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user