/* * SPDX-License-Identifier: Apache-2.0 */ #include "onnx/defs/schema.h" namespace ONNX_NAMESPACE { static constexpr const char* QuantizeLinear_ver25_doc = R"DOC( The linear quantization operator consumes a high-precision tensor, a scale, and a zero point to compute the low-precision/quantized tensor. The scale factor and zero point must have the same shape, determining the quantization granularity. The quantization formula is `y = saturate((x / y_scale) + y_zero_point)`. Saturation is done according to: - uint16: [0, 65535] - int16: [-32768, 32767] - uint8: [0, 255] - int8: [-128, 127] - uint4: [0, 15] - int4: [-8, 7] - uint2: [0, 3] - int2: [-2, 1] For `(x / y_scale)`, it rounds to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. `y_zero_point` and `y` must have the same type. `y_zero_point` is usually not used for quantization to float8 and 4bit types, but the quantization formula remains the same for consistency, and the type of the attribute `y_zero_point` still determines the quantization type. `x` and `y_scale` are allowed to have different types. The type of `y_scale` determines the precision of the division operation between `x` and `y_scale`, unless the `precision` attribute is specified. There are three supported quantization granularities, determined by the shape of `y_scale`. In all cases, `y_zero_point` must have the same shape as `y_scale`. - Per-tensor (per-layer) quantization: `y_scale` is a scalar. - Per-axis quantization: The scale must be a 1-D tensor, with the length of the quantization axis. For an input shape `(D0, ..., Di, ..., Dn)` and `axis=i`, `y_scale` is a 1-D tensor of length `Di`. - Blocked quantization: The scale's shape is identical to the input's shape, except for one dimension, in which blocking is performed. Given `x` shape `(D0, ..., Di, ..., Dn)`, `axis=i`, and block size `B`: `y_scale` shape is `(D0, ..., ceil(Di/B), ..., Dn)`. )DOC"; ONNX_OPERATOR_SET_SCHEMA( QuantizeLinear, 25, OpSchema() .Input(0, "x", "N-D full precision Input tensor to be quantized.", "T1") .Input( 1, "y_scale", "Scale for doing quantization to get `y`. For per-tensor/layer quantization the scale is a scalar, for " "per-axis quantization it is a 1-D Tensor and for blocked quantization it has the same shape as the " "input, except for one dimension in which blocking is performed.", "T2") .Input( 2, "y_zero_point", "Zero point for doing quantization to get `y`. Shape must match `y_scale`. " "Default is uint8 with zero point of 0 if it's not specified.", "T3", OpSchema::Optional) .Output(0, "y", "N-D quantized output tensor. It has same shape as input `x`.", "T3") .Attr( "axis", "(Optional) The axis of the dequantizing dimension of the input tensor. Used only for per-axis and blocked " "quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` " "where `r = rank(input)`. When the rank of the input is 1, per-tensor quantization is applied, " "rendering the axis unnecessary in this scenario.", AttributeProto::INT, static_cast(1)) .Attr( "saturate", "The parameter defines how the conversion behaves if an input value is out of " "range of the destination type. It only applies for float 8 quantization " "(float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. " "All cases are fully described in two tables inserted in the operator description.", AttributeProto::INT, static_cast(1)) .Attr( "block_size", "(Optional) The size of the quantization block (number of times every scale is replicated). Used only for " "blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, " "`y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is " "`[ceil(Di/Si), ceil(Di/(Si-1))-1]`", AttributeProto::INT, static_cast(0)) .Attr( "output_dtype", "(Optional) The output data type. If not supplied, the output data type is inferred from `y_zero_point` data type (`T3`). " "If neither `output_dtype` nor `y_zero_point` are supplied, output data type is uint8. " "If both `output_dtype` and `y_zero_point` are specified, `output_dtype` must be `T3`.", AttributeProto::INT, static_cast(0)) .Attr( "precision", "(Optional) The precision of the division operation between `x` and `y_scale`. If not provided, " "it will be the same as the type of `y_scale`.", AttributeProto::INT, static_cast(0)) .TypeConstraint( "T1", {"tensor(float)", "tensor(float16)", "tensor(bfloat16)", "tensor(int32)"}, "The type of the input 'x'.") .TypeConstraint( "T2", {"tensor(float)", "tensor(float16)", "tensor(bfloat16)", "tensor(int32)", "tensor(float8e8m0)"}, "The type of the input 'y_scale'.") .TypeConstraint( "T3", {"tensor(int8)", "tensor(uint8)", "tensor(int16)", "tensor(uint16)", "tensor(float8e4m3fn)", "tensor(float8e4m3fnuz)", "tensor(float8e5m2)", "tensor(float8e5m2fnuz)", "tensor(uint4)", "tensor(int4)", "tensor(float4e2m1)", "tensor(uint2)", "tensor(int2)"}, "The type of the input `y_zero_point` and the output `y`.") .SetDoc(QuantizeLinear_ver25_doc) .TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) { auto const zp_type = ctx.hasInput(2) ? ctx.getInputType(2) : nullptr; auto const output_dtype = static_cast(getAttribute(ctx, "output_dtype", TensorProto::UNDEFINED)); if (zp_type != nullptr) { auto const zp_elem_type = static_cast(getTensorElementType(*zp_type)); if (output_dtype != TensorProto::UNDEFINED && output_dtype != zp_elem_type) { fail_type_inference( "output_dtype ", TensorProto_DataType_Name(output_dtype), " does not match y_zero_point type ", TensorProto_DataType_Name(zp_elem_type), "."); } propagateElemTypeFromInputToOutput(ctx, 2, 0); } else if (output_dtype != TensorProto::UNDEFINED) { propagateElemTypeFromAttributeToOutput(ctx, "output_dtype", 0); } else { updateOutputElemType(ctx, 0, TensorProto::UINT8); } if (!hasInputShape(ctx, 0)) { return; } auto& input_shape = getInputShape(ctx, 0); updateOutputShape(ctx, 0, input_shape); })); static constexpr const char* DequantizeLinear_ver25_doc = R"DOC( The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full-precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have the same shape, determining the quantization's granularity: a scalar for per-tensor/per-layer quantization, a 1-D tensor for per-axis quantization, or have a rank identical to the input for blocked quantization. See QuantizeLinear for details on quantization granularity. `x_zero_point` and `x` must have the same type. `x` and `y` must have the same shape. In the case of dequantizing `int32`, there's no zero point (zero point is supposed to be 0). `zero-point` is usually not used in the case of float8 and 4-bit types quantization, but the dequantization formula remains the same for consistency. The output type is determined by the attribute `output_dtype`. If `output_dtype` is not supplied then the output type is the same as `x_scale`. The output type also determines the precision of the multiplication operation. )DOC"; ONNX_OPERATOR_SET_SCHEMA( DequantizeLinear, 25, OpSchema() .Input(0, "x", "N-D quantized input tensor to be de-quantized.", "T1") .Input( 1, "x_scale", "Scale for input `x`. For per-tensor/layer dequantization the scale is a scalar, for " "per per-axis dequantization it is a 1-D Tensor and for blocked dequantization it has the same shape as " "the input, except for one dimension in which blocking is performed.", "T2") .Input( 2, "x_zero_point", "Zero point for input `x`. Shape must match x_scale. " "It's optional. Zero point is 0 when it's not specified.", "T1", OpSchema::Optional) .Output( 0, "y", "N-D full precision output tensor. It has the same shape as input `x`. The data type is specified " "by the `output_dtype` attribute or, in its absence, the type of `x_scale`.", "T3") .Attr( "axis", "(Optional) The axis of the dequantizing dimension of the input tensor. Used for per-axis and blocked " "quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` " "where `r = rank(input)`.", AttributeProto::INT, static_cast(1)) .Attr( "block_size", "(Optional) The size of the quantization block (number of times every scale is replicated). Used only for " "blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, " "`y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is " "`[ceil(Di/Si), ceil(Di/(Si-1))-1]`", AttributeProto::INT, static_cast(0)) .Attr( "output_dtype", "(Optional) The output data type. If not supplied, the output data type is inferred from `x_scale` data type (`T2`)", AttributeProto::INT, static_cast(0)) .TypeConstraint( "T1", {"tensor(int8)", "tensor(uint8)", "tensor(int16)", "tensor(uint16)", "tensor(int32)", "tensor(float8e4m3fn)", "tensor(float8e4m3fnuz)", "tensor(float8e5m2)", "tensor(float8e5m2fnuz)", "tensor(uint4)", "tensor(int4)", "tensor(float4e2m1)", "tensor(uint2)", "tensor(int2)"}, "The type of the inputs 'x_zero_point' and 'x'.") .TypeConstraint( "T2", {"tensor(float)", "tensor(float16)", "tensor(bfloat16)", "tensor(float8e8m0)"}, "The type of the input 'x_scale'.") .TypeConstraint("T3", {"tensor(float)", "tensor(float16)", "tensor(bfloat16)"}, "The type of the output 'y'.") .SetDoc(DequantizeLinear_ver25_doc) .TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) { auto const output_dtype = static_cast(getAttribute(ctx, "output_dtype", TensorProto::UNDEFINED)); if (output_dtype != TensorProto::UNDEFINED) { propagateElemTypeFromAttributeToOutput(ctx, "output_dtype", 0); } else { propagateElemTypeFromInputToOutput(ctx, 1, 0); } if (!hasInputShape(ctx, 0)) { return; } auto& input_shape = getInputShape(ctx, 0); updateOutputShape(ctx, 0, input_shape); })); static constexpr const char* DynamicQuantizeLinear_ver11_doc = R"DOC( A Function to fuse calculation for Scale, Zero Point and FP32->8Bit conversion of FP32 Input data. Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input. Scale is calculated as: ``` y_scale = (maximum(0, max(x)) - minimum(0, min(x))) / (qmax - qmin) ``` * where qmax and qmin are max and min values for quantization range i.e. [0, 255] in case of uint8 * data range is adjusted to include 0. Zero point is calculated as: ``` intermediate_zero_point = qmin - min(x)/y_scale y_zero_point = cast(round(saturate(intermediate_zero_point))) ``` * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8 * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported. * rounding to nearest ties to even. Data quantization formula is: ``` y = saturate (round (x / y_scale) + y_zero_point) ``` * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported. * rounding to nearest ties to even. )DOC"; ONNX_OPERATOR_SET_SCHEMA( DynamicQuantizeLinear, 11, OpSchema() .SetDoc(DynamicQuantizeLinear_ver11_doc) .Input(0, "x", "Input tensor", "T1") .Output(0, "y", "Quantized output tensor", "T2") .Output( 1, "y_scale", "Output scale. It's a scalar, which means a per-tensor/layer quantization.", "tensor(float)") .Output( 2, "y_zero_point", "Output zero point. It's a scalar, which means a per-tensor/layer quantization.", "T2") .TypeConstraint("T1", {"tensor(float)"}, "Constrain 'x' to float tensor.") .TypeConstraint("T2", {"tensor(uint8)"}, "Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.") .FunctionBody(R"ONNX( { Q_Min = Constant() Q_Max = Constant() X_Min = ReduceMin (x) X_Min_Adjusted = Min (X_Min, Q_Min) X_Max = ReduceMax (x) X_Max_Adjusted = Max (X_Max, Q_Min) X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted) Scale = Div (X_Range, Q_Max) Min_Scaled = Div (X_Min_Adjusted, Scale) Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled) Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max) Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP) Zeropoint = Cast (Rounded_ZeroPoint_FP) y_scale = Identity (Scale) y_zero_point = Identity (Zeropoint) y = QuantizeLinear (x, Scale, Zeropoint) } )ONNX") .TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) { updateOutputElemType(ctx, 0, TensorProto::UINT8); updateOutputElemType(ctx, 1, TensorProto::FLOAT); updateOutputElemType(ctx, 2, TensorProto::UINT8); ctx.getOutputType(1)->mutable_tensor_type()->mutable_shape(); ctx.getOutputType(2)->mutable_tensor_type()->mutable_shape(); if (!hasInputShape(ctx, 0)) return; auto& input_shape = getInputShape(ctx, 0); updateOutputShape(ctx, 0, input_shape); })); } // namespace ONNX_NAMESPACE