330 lines
15 KiB
C++
330 lines
15 KiB
C++
/*
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* SPDX-License-Identifier: Apache-2.0
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*/
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#include "onnx/defs/function.h"
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#include "onnx/defs/schema.h"
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namespace ONNX_NAMESPACE {
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static const char* QuantizeLinear_ver19_doc = R"DOC(
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The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor.
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The scale factor and zero point must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization.
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The quantization formula is `y = saturate ((x / y_scale) + y_zero_point)`.
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For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8.
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For (x / y_scale), it's rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details.
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'y_zero_point' and 'y' must have same type.
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'y_zero_point' is usually not used for quantization to float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz,
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but the quantization formula remains the same for consistency and
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the type of the attribute 'y_zero_point' still determines the quantization type.
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)DOC";
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ONNX_OPERATOR_SET_SCHEMA(
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QuantizeLinear,
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19,
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OpSchema()
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.Input(0, "x", "N-D full precision Input tensor to be quantized.", "T1")
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.Input(
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1,
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"y_scale",
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"Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, "
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"or a 1-D Tensor for per-axis quantization.",
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"T1")
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.Input(
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2,
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"y_zero_point",
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"Zero point for doing quantization to get 'y'. Shape must match y_scale. "
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"Default is uint8 with zero point of 0 if it's not specified.",
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"T2",
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OpSchema::Optional)
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.Output(0, "y", "N-D quantized output tensor. It has same shape as input 'x'.", "T2")
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.Attr(
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"axis",
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"(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).",
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AttributeProto::INT,
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static_cast<int64_t>(1))
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.Attr(
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"saturate",
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"The parameter defines how the conversion behaves if an input value is out of "
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"range of the destination type. It only applies for float 8 quantization "
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"(float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. "
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"All cases are fully described in two tables inserted in the operator description.",
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AttributeProto::INT,
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static_cast<int64_t>(1))
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.TypeConstraint(
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"T1",
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{"tensor(float)", "tensor(float16)", "tensor(bfloat16)", "tensor(int32)"},
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"Constrain 'x' to float, float16, bfloat16 or int32 tensor.")
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.TypeConstraint(
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"T2",
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{"tensor(int8)",
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"tensor(uint8)",
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"tensor(float8e4m3fn)",
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"tensor(float8e4m3fnuz)",
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"tensor(float8e5m2)",
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"tensor(float8e5m2fnuz)"},
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"Constrain 'y_zero_point' and 'y' to 8-bit integer/float tensor.")
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.SetDoc(QuantizeLinear_ver19_doc)
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.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
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if (ctx.hasInput(2)) {
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propagateElemTypeFromInputToOutput(ctx, 2, 0);
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} else {
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updateOutputElemType(ctx, 0, TensorProto::UINT8);
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}
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if (!hasInputShape(ctx, 0)) {
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return;
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}
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auto& input_shape = getInputShape(ctx, 0);
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updateOutputShape(ctx, 0, input_shape);
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}));
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static const char* DequantizeLinear_ver19_doc = R"DOC(
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The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor.
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The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have same shape, and can be either a scalar
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for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization.
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`x_zero_point` and `x` must have same type. `x` and `y` must have same shape. In the case of dequantizing int32,
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there's no zero point (zero point is supposed to be 0).
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`zero-point` is usually not used in the case of float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz quantization,
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but the dequantization formula remains the same for consistency and 'x_scale' still determines the output type.
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)DOC";
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ONNX_OPERATOR_SET_SCHEMA(
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DequantizeLinear,
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19,
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OpSchema()
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.Input(0, "x", "N-D quantized input tensor to be de-quantized.", "T1")
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.Input(
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1,
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"x_scale",
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"Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, "
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"or a 1-D tensor for per-axis dequantization.",
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"T2")
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.Input(
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2,
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"x_zero_point",
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"Zero point for input 'x'. Shape must match x_scale. "
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"It's optional. Zero point is 0 when it's not specified.",
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"T1",
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OpSchema::Optional)
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.Output(0, "y", "N-D full precision output tensor. It has same shape as input 'x'.", "T2")
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.Attr(
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"axis",
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"(Optional) The axis of the dequantizing dimension of the input tensor. Used only for per-axis quantization. "
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"Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` "
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"where `r = rank(input)`. When the rank of the input is 1, per-tensor quantization is applied, "
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"rendering the axis unnecessary in this scenario.",
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AttributeProto::INT,
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static_cast<int64_t>(1))
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.TypeConstraint(
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"T1",
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{"tensor(int8)",
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"tensor(uint8)",
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"tensor(int32)",
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"tensor(float8e4m3fn)",
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"tensor(float8e4m3fnuz)",
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"tensor(float8e5m2)",
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"tensor(float8e5m2fnuz)"},
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"Constrain 'x_zero_point' and 'x' to 8-bit integer or float, or /32-bit integer tensor.")
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.TypeConstraint(
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"T2",
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{"tensor(float)", "tensor(float16)", "tensor(bfloat16)"},
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"'x_scale' determines the output type.")
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.SetDoc(DequantizeLinear_ver19_doc)
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.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
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propagateElemTypeFromInputToOutput(ctx, 1, 0);
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if (!hasInputShape(ctx, 0)) {
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return;
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}
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auto& input_shape = getInputShape(ctx, 0);
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updateOutputShape(ctx, 0, input_shape);
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}));
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static const char* QuantizeLinear_ver13_doc = R"DOC(
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The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor.
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The scale factor and zero point must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization.
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The quantization formula is y = saturate ((x / y_scale) + y_zero_point).
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For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8.
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For (x / y_scale), it's rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type.
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)DOC";
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ONNX_OPERATOR_SET_SCHEMA(
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QuantizeLinear,
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13,
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OpSchema()
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.Input(0, "x", "N-D full precision Input tensor to be quantized.", "T1")
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.Input(
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1,
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"y_scale",
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"Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, "
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"or a 1-D Tensor for per-axis quantization.",
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"tensor(float)")
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.Input(
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2,
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"y_zero_point",
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"Zero point for doing quantization to get 'y'. Shape must match y_scale. "
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"Default is uint8 with zero point of 0 if it's not specified.",
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"T2",
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OpSchema::Optional)
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.Output(0, "y", "N-D quantized output tensor. It has same shape as input 'x'.", "T2")
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.Attr(
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"axis",
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"(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).",
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AttributeProto::INT,
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static_cast<int64_t>(1))
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.TypeConstraint("T1", {"tensor(float)", "tensor(int32)"}, "Constrain 'x' to float or int32 tensor.")
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.TypeConstraint(
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"T2",
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{"tensor(int8)", "tensor(uint8)"},
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"Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.")
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.SetDoc(QuantizeLinear_ver13_doc)
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.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
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if (ctx.hasInput(2)) {
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propagateElemTypeFromInputToOutput(ctx, 2, 0);
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} else {
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updateOutputElemType(ctx, 0, TensorProto::UINT8);
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}
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if (!hasInputShape(ctx, 0)) {
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return;
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}
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auto& input_shape = getInputShape(ctx, 0);
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updateOutputShape(ctx, 0, input_shape);
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}));
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static const char* DequantizeLinear_ver13_doc = R"DOC(
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The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor.
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The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have same shape, and can be either a scalar
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for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization.
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`x_zero_point` and `x` must have same type. `x` and `y` must have same shape. In the case of dequantizing int32,
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there's no zero point (zero point is supposed to be 0).
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)DOC";
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ONNX_OPERATOR_SET_SCHEMA(
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DequantizeLinear,
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13,
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OpSchema()
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.Input(0, "x", "N-D quantized input tensor to be de-quantized.", "T")
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.Input(
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1,
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"x_scale",
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"Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, "
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"or a 1-D tensor for per-axis dequantization.",
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"tensor(float)")
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.Input(
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2,
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"x_zero_point",
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"Zero point for input 'x'. Shape must match x_scale. "
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"It's optional. Zero point is 0 when it's not specified.",
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"T",
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OpSchema::Optional)
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.Output(0, "y", "N-D full precision output tensor. It has same shape as input 'x'.", "tensor(float)")
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.Attr(
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"axis",
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"(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).",
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AttributeProto::INT,
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static_cast<int64_t>(1))
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.TypeConstraint(
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"T",
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{"tensor(int8)", "tensor(uint8)", "tensor(int32)"},
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"Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.")
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.SetDoc(DequantizeLinear_ver13_doc)
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.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
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auto y_type = ctx.getOutputType(0);
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// only float is supported
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y_type->mutable_tensor_type()->set_elem_type(ONNX_NAMESPACE::TensorProto::FLOAT);
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if (!hasInputShape(ctx, 0))
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return;
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auto& input_shape = getInputShape(ctx, 0);
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updateOutputShape(ctx, 0, input_shape);
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}));
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static const char* QuantizeLinear_ver10_doc = R"DOC(
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The linear per-tensor/layer quantization operator. It consumes a high precision tensor, a scale, a zero point to compute the low precision / quantized tensor.
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The quantization formula is y = saturate ((x / y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8.
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For (x / y_scale), it's rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type.
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)DOC";
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ONNX_OPERATOR_SET_SCHEMA(
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QuantizeLinear,
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10,
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OpSchema()
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.Input(0, "x", "N-D full precision Input tensor to be quantized.", "T1")
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.Input(
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1,
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"y_scale",
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"Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.",
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"tensor(float)")
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.Input(
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2,
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"y_zero_point",
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"Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. "
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"Default value is uint8 typed 0 if it's not specified.",
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"T2",
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OpSchema::Optional)
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.Output(0, "y", "N-D quantized output tensor. It has same shape as input 'x'.", "T2")
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.TypeConstraint("T1", {"tensor(float)", "tensor(int32)"}, "Constrain 'x' to float or int32 tensor.")
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.TypeConstraint(
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"T2",
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{"tensor(int8)", "tensor(uint8)"},
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"Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.")
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.SetDoc(QuantizeLinear_ver10_doc)
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.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
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if (ctx.hasInput(2)) {
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propagateElemTypeFromInputToOutput(ctx, 2, 0);
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} else {
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updateOutputElemType(ctx, 0, TensorProto::UINT8);
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}
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if (!hasInputShape(ctx, 0)) {
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return;
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}
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auto& input_shape = getInputShape(ctx, 0);
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updateOutputShape(ctx, 0, input_shape);
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}));
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static const char* DequantizeLinear_ver10_doc = R"DOC(
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The linear dequantization operator. It consumes a quantized tensor, a scale, a zero point to compute the full precision tensor.
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The dequantization formula is y = (x - x_zero_point) * x_scale. 'x_scale' and 'x_zero_point' are both scalars.
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'x_zero_point' and 'x' must have same type. 'x' and 'y' must have same shape. In the case of dequantizing int32,
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there's no zero point (zero point is supposed to be 0).
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)DOC";
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ONNX_OPERATOR_SET_SCHEMA(
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DequantizeLinear,
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10,
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OpSchema()
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.Input(0, "x", "N-D quantized input tensor to be de-quantized.", "T")
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.Input(
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1,
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"x_scale",
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"Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.",
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"tensor(float)")
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.Input(
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2,
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"x_zero_point",
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"Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. "
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"It's optional. 0 is the default value when it's not specified.",
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"T",
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OpSchema::Optional)
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.Output(0, "y", "N-D full precision output tensor. It has same shape as input 'x'.", "tensor(float)")
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.TypeConstraint(
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"T",
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{"tensor(int8)", "tensor(uint8)", "tensor(int32)"},
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"Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.")
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.SetDoc(DequantizeLinear_ver10_doc)
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.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
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auto y_type = ctx.getOutputType(0);
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// only float is supported
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y_type->mutable_tensor_type()->set_elem_type(ONNX_NAMESPACE::TensorProto::FLOAT);
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if (!hasInputShape(ctx, 0))
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return;
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auto& input_shape = getInputShape(ctx, 0);
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updateOutputShape(ctx, 0, input_shape);
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}));
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} // namespace ONNX_NAMESPACE
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