296 lines
13 KiB
C++
296 lines
13 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_ver21_doc = R"DOC(
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The linear quantization operator consumes a high-precision tensor, a scale, and a zero point to compute the
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low-precision/quantized tensor. The scale factor and zero point must have the same shape, determining the quantization
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granularity. The quantization formula is `y = saturate((x / y_scale) + y_zero_point)`.
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Saturation is done according to:
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- uint16: [0, 65535]
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- int16: [-32768, 32767]
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- uint8: [0, 255]
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- int8: [-128, 127]
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- uint4: [0, 15]
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- int4: [-8, 7]
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For `(x / y_scale)`, it rounds 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 the same type. `y_zero_point` is usually not used for quantization to float8 types, but the quantization
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formula remains the same for consistency, and the type of the attribute `y_zero_point` still determines the quantization type.
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There are three supported quantization granularities, determined by the shape of `y_scale`.
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In all cases, `y_zero_point` must have the same shape as `y_scale`.
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- Per-tensor (per-layer) quantization: `y_scale` is a scalar.
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- Per-axis quantization: The scale must be a 1-D tensor, with the length of the quantization axis. For an input shape
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`(D0, ..., Di, ..., Dn)` and `axis=i`, `y_scale` is a 1-D tensor of length `Di`.
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- Blocked quantization: The scale's shape is identical to the input's shape, except for one dimension, in which
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blocking is performed. Given `x` shape `(D0, ..., Di, ..., Dn)`, `axis=i`, and block size `B`: `y_scale` shape is
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`(D0, ..., ceil(Di/B), ..., Dn)`.
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)DOC";
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ONNX_OPERATOR_SET_SCHEMA(
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QuantizeLinear,
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21,
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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`. For per-tensor/layer quantization the scale is a scalar, for "
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"per-axis quantization it is a 1-D Tensor and for blocked quantization it has the same shape as the "
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"input, except for one dimension in which blocking is performed.",
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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 dequantizing dimension of the input tensor. Used only for per-axis and blocked "
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"quantization. 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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.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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.Attr(
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"block_size",
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"(Optional) The size of the quantization block (number of times every scale is replicated). Used only for "
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"blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, "
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"`y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is "
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"`[ceil(Di/Si), ceil(Di/(Si-1))-1]`",
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AttributeProto::INT,
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static_cast<int64_t>(0))
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.Attr(
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"output_dtype",
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"(Optional) The output data type. If not supplied, the output data type is inferred from `y_zero_point` data type (`T2`). "
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"If neither `output_dtype` nor `y_zero_point` are supplied, output data type is uint8. "
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"If both `output_dtype` and `y_zero_point` are specified, `output_dtype` must be `T2`.",
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AttributeProto::INT,
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static_cast<int64_t>(0))
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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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"The type of the input 'x'.")
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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(int16)",
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"tensor(uint16)",
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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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"tensor(uint4)",
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"tensor(int4)"},
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"The type of the input `y_zero_point` and the output `y`.")
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.SetDoc(QuantizeLinear_ver21_doc)
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.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
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auto const zp_type = ctx.hasInput(2) ? ctx.getInputType(2) : nullptr;
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auto const output_dtype =
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static_cast<TensorProto_DataType>(getAttribute(ctx, "output_dtype", TensorProto::UNDEFINED));
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if (zp_type != nullptr) {
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auto const zp_elem_type = static_cast<TensorProto_DataType>(getTensorElementType(*zp_type));
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if (output_dtype != TensorProto::UNDEFINED && output_dtype != zp_elem_type) {
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fail_type_inference(
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"output_dtype ",
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TensorProto_DataType_Name(output_dtype),
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" does not match y_zero_point type ",
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TensorProto_DataType_Name(zp_elem_type),
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".");
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}
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propagateElemTypeFromInputToOutput(ctx, 2, 0);
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} else if (output_dtype != TensorProto::UNDEFINED) {
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propagateElemTypeFromAttributeToOutput(ctx, "output_dtype", 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_ver21_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
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full-precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point`
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must have the same shape, determining the quantization's granularity: a scalar for per-tensor/per-layer quantization,
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a 1-D tensor for per-axis quantization, or have a rank identical to the input for blocked quantization.
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See QuantizeLinear for details on quantization granularity.
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`x_zero_point` and `x` must have the same type. `x` and `y` must have the same shape. In the case of dequantizing
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`int32`, 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 float8 types quantization, but the dequantization formula remains the same
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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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21,
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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`. For per-tensor/layer dequantization the scale is a scalar, for "
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"per per-axis dequantization it is a 1-D Tensor and for blocked dequantization it has the same shape as "
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"the input, except for one dimension in which blocking is performed.",
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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 for per-axis and blocked "
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"quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` "
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"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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"block_size",
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"(Optional) The size of the quantization block (number of times every scale is replicated). Used only for "
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"blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, "
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"`y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is "
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"`[ceil(Di/Si), ceil(Di/(Si-1))-1]`",
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AttributeProto::INT,
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static_cast<int64_t>(0))
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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(int16)",
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"tensor(uint16)",
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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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"tensor(uint4)",
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"tensor(int4)"},
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"The type of the inputs 'x_zero_point' and 'x'.")
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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_ver21_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* DynamicQuantizeLinear_ver11_doc = R"DOC(
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A Function to fuse calculation for Scale, Zero Point and FP32->8Bit conversion of FP32 Input data.
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Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input.
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Scale is calculated as:
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```
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y_scale = (maximum(0, max(x)) - minimum(0, min(x))) / (qmax - qmin)
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```
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* where qmax and qmin are max and min values for quantization range i.e. [0, 255] in case of uint8
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* data range is adjusted to include 0.
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Zero point is calculated as:
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```
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intermediate_zero_point = qmin - min(x)/y_scale
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y_zero_point = cast(round(saturate(itermediate_zero_point)))
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```
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* where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8
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* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
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* rounding to nearest ties to even.
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Data quantization formula is:
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```
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y = saturate (round (x / y_scale) + y_zero_point)
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```
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* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported.
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* rounding to nearest ties to even.
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)DOC";
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ONNX_OPERATOR_SET_SCHEMA(
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DynamicQuantizeLinear,
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11,
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OpSchema()
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.SetDoc(DynamicQuantizeLinear_ver11_doc)
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.Input(0, "x", "Input tensor", "T1")
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.Output(0, "y", "Quantized output tensor", "T2")
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.Output(
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1,
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"y_scale",
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"Output scale. It's a scalar, which means a per-tensor/layer quantization.",
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"tensor(float)")
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.Output(
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2,
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"y_zero_point",
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"Output zero point. It's a scalar, which means a per-tensor/layer quantization.",
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"T2")
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.TypeConstraint("T1", {"tensor(float)"}, "Constrain 'x' to float tensor.")
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.TypeConstraint("T2", {"tensor(uint8)"}, "Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.")
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.FunctionBody(R"ONNX(
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{
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Q_Min = Constant<value = float {0.0}>()
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Q_Max = Constant<value = float {255.0}>()
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X_Min = ReduceMin <keepdims = 0> (x)
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X_Min_Adjusted = Min (X_Min, Q_Min)
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X_Max = ReduceMax <keepdims = 0> (x)
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X_Max_Adjusted = Max (X_Max, Q_Min)
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X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted)
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Scale = Div (X_Range, Q_Max)
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Min_Scaled = Div (X_Min_Adjusted, Scale)
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Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled)
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Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max)
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Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP)
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Zeropoint = Cast <to = 2> (Rounded_ZeroPoint_FP)
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y_scale = Identity (Scale)
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y_zero_point = Identity (Zeropoint)
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y = QuantizeLinear (x, Scale, Zeropoint)
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}
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)ONNX")
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.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
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updateOutputElemType(ctx, 0, TensorProto::UINT8);
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updateOutputElemType(ctx, 1, TensorProto::FLOAT);
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updateOutputElemType(ctx, 2, TensorProto::UINT8);
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ctx.getOutputType(1)->mutable_tensor_type()->mutable_shape();
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ctx.getOutputType(2)->mutable_tensor_type()->mutable_shape();
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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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