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Add NHWC CONV contrib op (#10506)
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Changming Sun
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4 changed files with 168 additions and 1 deletions
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@ -12,3 +12,8 @@ samples/python/training/** @thiagocrepaldi @tlh20 @liqunfu @baijumeswani @Sherlo
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# Mobile
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/onnxruntime/test/testdata/kernel_def_hashes/ @skottmckay @gwang-msft @YUNQIUGUO @edgchen1
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# Contrib Ops
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onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.cc @zhanghuanrong @chenfucn @yufenglee @yihonglyu @snnn
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onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.cc @zhanghuanrong @chenfucn @yufenglee @yihonglyu @snnn
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onnxruntime/core/graph/contrib_ops/quantization_defs.* @zhanghuanrong @chenfucn @yufenglee @yihonglyu @snnn
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@ -37,6 +37,7 @@ Do not modify directly.*
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* <a href="#com.microsoft.MulInteger">com.microsoft.MulInteger</a>
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* <a href="#com.microsoft.MurmurHash3">com.microsoft.MurmurHash3</a>
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* <a href="#com.microsoft.NGramRepeatBlock">com.microsoft.NGramRepeatBlock</a>
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* <a href="#com.microsoft.NhwcConv">com.microsoft.NhwcConv</a>
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* <a href="#com.microsoft.NhwcMaxPool">com.microsoft.NhwcMaxPool</a>
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* <a href="#com.microsoft.Pad">com.microsoft.Pad</a>
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* <a href="#com.microsoft.QAttention">com.microsoft.QAttention</a>
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@ -1823,6 +1824,55 @@ This version of the operator has been available since version 1 of the 'com.micr
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</dl>
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### <a name="com.microsoft.NhwcConv"></a><a name="com.microsoft.nhwcconv">**com.microsoft.NhwcConv**</a>
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#### Version
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This version of the operator has been available since version 1 of the 'com.microsoft' operator set.
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#### Attributes
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<dl>
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<dt><tt>auto_pad</tt> : string</dt>
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<dd></dd>
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<dt><tt>dilations</tt> : list of ints</dt>
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<dd>dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.</dd>
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<dt><tt>group</tt> : int</dt>
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<dd>number of groups input channels and output channels are divided into.</dd>
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<dt><tt>kernel_shape</tt> : list of ints</dt>
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<dd>The shape of the convolution kernel. If not present, should be inferred from input W.</dd>
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<dt><tt>pads</tt> : list of ints</dt>
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<dd></dd>
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<dt><tt>strides</tt> : list of ints</dt>
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<dd>Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.</dd>
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</dl>
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#### Inputs (2 - 3)
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<dl>
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<dt><tt>X</tt> : T</dt>
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<dd>Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].</dd>
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<dt><tt>W</tt> : T</dt>
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<dd>The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. Assuming zero based indices for the shape array, X.shape[1] == (W.shape[1] * group) == C and W.shape[0] mod G == 0. Or in other words FILTER_IN_CHANNEL multiplied by the number of groups should be equal to DATA_CHANNEL and the number of feature maps M should be a multiple of the number of groups G.</dd>
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<dt><tt>B</tt> (optional) : T</dt>
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<dd>Optional 1D bias to be added to the convolution, has size of M.</dd>
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</dl>
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#### Outputs
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<dl>
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<dt><tt>Y</tt> : T</dt>
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<dd>Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.</dd>
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</dl>
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#### Type Constraints
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<dl>
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<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
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<dd>Constrain input and output types to float tensors.</dd>
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</dl>
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### <a name="com.microsoft.NhwcMaxPool"></a><a name="com.microsoft.nhwcmaxpool">**com.microsoft.NhwcMaxPool**</a>
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#### Version
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@ -12,6 +12,7 @@ class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Microsoft, 1, NhwcMaxPool);
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class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Microsoft, 1, QLinearGlobalAveragePool);
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class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Microsoft, 1, QLinearAveragePool);
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class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Microsoft, 1, QLinearConv);
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class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Microsoft, 1, NhwcConv);
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//Quantization ops
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class ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Microsoft, 1, DequantizeLinear);
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@ -80,6 +81,7 @@ class OpSet_Microsoft_ver1 {
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fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Microsoft, 1, QLinearGlobalAveragePool)>());
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fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Microsoft, 1, QLinearAveragePool)>());
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fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Microsoft, 1, QLinearConv)>());
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fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Microsoft, 1, NhwcConv)>());
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fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Microsoft, 1, DequantizeLinear)>());
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fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Microsoft, 1, DynamicQuantizeLSTM)>());
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@ -354,5 +354,115 @@ ONNX_MS_OPERATOR_SET_SCHEMA(QLinearConv, 1,
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onnxruntime::contrib::convPoolShapeInferenceNhwc(ctx, true, false, 0, 3);
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}
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}));
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std::function<void(OpSchema&)> ConvOpSchemaGenerator() {
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return [=](OpSchema& schema) {
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schema.Input(
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0,
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"X",
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"Input data tensor from previous layer; "
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"has size (N x C x H x W), where N is the batch size, "
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"C is the number of channels, and H and W are the "
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"height and width. Note that this is for the 2D image. "
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"Otherwise the size is (N x C x D1 x D2 ... x Dn). "
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"Optionally, if dimension denotation is "
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"in effect, the operation expects input data tensor "
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"to arrive with the dimension denotation of [DATA_BATCH, "
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"DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].",
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"T",
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OpSchema::Single,
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true,
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1,
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OpSchema::Differentiable);
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schema.Input(
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1,
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"W",
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"The weight tensor that will be used in the "
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"convolutions; has size (M x C/group x kH x kW), where C "
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"is the number of channels, and kH and kW are the "
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"height and width of the kernel, and M is the number "
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"of feature maps. For more than 2 dimensions, the "
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"kernel shape will be (M x C/group x k1 x k2 x ... x kn), "
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"where (k1 x k2 x ... kn) is the dimension of the kernel. "
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"Optionally, if dimension denotation is in effect, "
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"the operation expects the weight tensor to arrive "
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"with the dimension denotation of [FILTER_OUT_CHANNEL, "
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"FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. "
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"Assuming zero based indices for the shape array, "
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"X.shape[1] == (W.shape[1] * group) == C and "
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"W.shape[0] mod G == 0. Or in other words "
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"FILTER_IN_CHANNEL multiplied by the number of groups "
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"should be equal to DATA_CHANNEL and the number of "
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"feature maps M should be a multiple of the number of "
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"groups G.",
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"T",
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OpSchema::Single,
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true,
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1,
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OpSchema::Differentiable);
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schema.Input(
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2,
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"B",
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"Optional 1D bias to be added to the convolution, has size of M.",
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"T",
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OpSchema::Optional,
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true,
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1,
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OpSchema::Differentiable);
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schema.Output(
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0,
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"Y",
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"Output data tensor that contains the result of the "
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"convolution. The output dimensions are functions "
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"of the kernel size, stride size, and pad lengths.",
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"T",
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OpSchema::Single,
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true,
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1,
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OpSchema::Differentiable);
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schema.TypeConstraint(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.");
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schema.Attr(
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"kernel_shape",
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"The shape of the convolution kernel. If not present, should be inferred from input W.",
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AttributeProto::INTS,
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OPTIONAL_VALUE);
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schema.Attr(
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"dilations",
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"dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.",
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AttributeProto::INTS,
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OPTIONAL_VALUE);
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schema.Attr(
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"strides",
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"Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.",
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AttributeProto::INTS,
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OPTIONAL_VALUE);
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schema.Attr(
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"auto_pad",
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"",
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AttributeProto::STRING,
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std::string("NOTSET"));
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schema.Attr("pads", "", AttributeProto::INTS, OPTIONAL_VALUE);
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schema.Attr(
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"group",
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"number of groups input channels and output channels are divided into.",
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AttributeProto::INT,
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static_cast<int64_t>(1));
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schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
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propagateElemTypeFromInputToOutput(ctx, 0, 0);
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NhwcInferenceContext nhwc_ctx(ctx);
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nhwc_ctx.TransposeInputShape();
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convPoolShapeInference(nhwc_ctx, true, false, 0, 1);
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nhwc_ctx.TransposeOutputShape();
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});
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};
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}
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ONNX_MS_OPERATOR_SET_SCHEMA(
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NhwcConv,
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1,
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OpSchema().FillUsing(ConvOpSchemaGenerator()));
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} // namespace contrib
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} // namespace onnxruntime
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} // namespace onnxruntime
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