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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/07/19 17:07:40 UTC
[GitHub] [tvm] AndrewZhaoLuo commented on a diff in pull request #12116: [QNN] Support different qnn params between in/out tensor in leaky_relu
AndrewZhaoLuo commented on code in PR #12116:
URL: https://github.com/apache/tvm/pull/12116#discussion_r924739254
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python/tvm/relay/qnn/op/qnn.py:
##########
@@ -1179,7 +1179,7 @@ def batch_matmul(x, y, x_zero_point, y_zero_point, x_scale, y_scale, out_dtype="
reg.register_pattern("qnn.dequantize", OpPattern.OPAQUE)
-def leaky_relu(x, alpha, scale, zero_point):
+def leaky_relu(x, alpha, scale, zero_point, output_scale, output_zero_point):
Review Comment:
change scale, zero_point --> input_scale, input_zero_point
##########
python/tvm/relay/qnn/op/qnn.py:
##########
@@ -1192,15 +1192,13 @@ def leaky_relu(x, alpha, scale, zero_point):
The scale of the quantized expr.
Review Comment:
change this and desc. for zero point to 'scale of the input quantized expr'
##########
src/relay/qnn/op/leaky_relu.cc:
##########
@@ -32,8 +32,8 @@ namespace qnn {
bool QnnLeakyReluRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
const TypeReporter& reporter) {
- // Expected Types: data, scale, zero_point
- ICHECK_EQ(types.size(), 4);
+ // Expected Types: data, scale, zero_point, output_scale, output_zero_point, out_type
Review Comment:
change scale, zero_point ref. to input_scale, input_zero_point
##########
src/relay/qnn/op/leaky_relu.cc:
##########
@@ -82,42 +87,62 @@ Expr QnnLeakyReluCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
// by a small alpha value < 1.
//
// We assume the same scale and zero point for alpha and the input tensor.
- // Let T = s(q_t - z) where q_t is the input arg[0]
- // Then, the quantized value of alpha * T is:
- // q(a * T, s, z) = [(a * T) / s] + z = a * s(q_t - z) / s + z = a * (q_t - z) + z
- // = a * q_t + (1 - a) * z
+ // LeakyReLU can be written in terms of respective quantized tensors, scales and
+ // zero points as
//
- // We return the quantized value of alpha * T for all values q_t < input_zero_point.
-
- ICHECK_EQ(new_args.size(), 3);
- Expr quantized_data = Cast(new_args[0], DataType::Int(32));
+ // scale_o * (Q_o - zp_o) = alpha * scale_i * (Q_i - zp_i) when Q_i < zp_i (1)
+ // Q_o = Q_i when Q_i >= zp_i (2)
Review Comment:
should (2) be `scale_o * (Q_o - zp_o) = scale_i * (Q_i - zp_i) when Q_i >= zp_i`
##########
src/relay/qnn/op/leaky_relu.cc:
##########
@@ -82,42 +87,62 @@ Expr QnnLeakyReluCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
// by a small alpha value < 1.
//
// We assume the same scale and zero point for alpha and the input tensor.
- // Let T = s(q_t - z) where q_t is the input arg[0]
- // Then, the quantized value of alpha * T is:
- // q(a * T, s, z) = [(a * T) / s] + z = a * s(q_t - z) / s + z = a * (q_t - z) + z
- // = a * q_t + (1 - a) * z
+ // LeakyReLU can be written in terms of respective quantized tensors, scales and
+ // zero points as
//
- // We return the quantized value of alpha * T for all values q_t < input_zero_point.
-
- ICHECK_EQ(new_args.size(), 3);
- Expr quantized_data = Cast(new_args[0], DataType::Int(32));
+ // scale_o * (Q_o - zp_o) = alpha * scale_i * (Q_i - zp_i) when Q_i < zp_i (1)
+ // Q_o = Q_i when Q_i >= zp_i (2)
+ //
+ // Since the input qnn params can be different than output qnn params, we first requantize the
+ // input tensor to the output qnn params. After requantizing Q_i, equation (1) becames equation
+ // (3) where Q_i' is the requantized data from Q_i.
+ //
+ // scale_o * (Q_o - zp_o) = alpha * scale_o * (Q_i' - zp_o) when Q_i < zp_i (3)
+ // Q_o = alpha * Q_i' + (1 - alpha) * zp_o when Q_i < zp_i (4)
+ ICHECK_EQ(new_args.size(), 5);
+ Expr data = Cast(new_args[0], DataType::Int(32));
+ Expr input_scale = new_args[1];
Expr input_zero_point = Cast(new_args[2], DataType::Int(32));
+ Expr output_scale = new_args[3];
+ Expr output_zero_point = Cast(new_args[4], DataType::Int(32));
const auto* q_attrs = attrs.as<LeakyReluAttrs>();
auto alpha = q_attrs->alpha;
+ const auto input_shape = get_shape(arg_types[0]);
+ const auto input_dtype = arg_types[0].as<TensorTypeNode>()->dtype;
+
+ // requantize the input to Q_i'
+ auto requantized_expr = RequantizeOrUpcast(data, input_scale, input_zero_point, output_scale,
+ output_zero_point, input_shape);
+
+ // alpha * Q_i'
int32_t fixed_point_multiplier, shift;
std::tie(fixed_point_multiplier, shift) = GetFixedPointMultiplierShift(alpha);
- auto prod = FixedPointMultiply(quantized_data, fixed_point_multiplier, shift);
+ auto prod = FixedPointMultiply(requantized_expr, fixed_point_multiplier, shift);
+ // (1 - alpha) * zp_o
int32_t fixed_point_multiplier_z, shift_z;
std::tie(fixed_point_multiplier_z, shift_z) = GetFixedPointMultiplierShift(1 - alpha);
- auto scaled_z = FixedPointMultiply(input_zero_point, fixed_point_multiplier_z, shift_z);
+ auto scaled_z = FixedPointMultiply(output_zero_point, fixed_point_multiplier_z, shift_z);
+ // alpha * Q_i + (1 - alpha) * zp_o
auto add = Add(prod, scaled_z);
- auto output = Where(Less(quantized_data, input_zero_point), add, quantized_data);
+ auto output = Where(Less(data, input_zero_point), add, data);
Review Comment:
So `add` will be have quantization parameters of `output_scale`, `output_zero_point` which is right while `data` will have quantization parameters of `input_scale` and `input_zero_point`. Believe you must grab from requantized_expr.
##########
src/relay/qnn/op/leaky_relu.cc:
##########
@@ -82,42 +87,62 @@ Expr QnnLeakyReluCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
// by a small alpha value < 1.
//
// We assume the same scale and zero point for alpha and the input tensor.
- // Let T = s(q_t - z) where q_t is the input arg[0]
- // Then, the quantized value of alpha * T is:
- // q(a * T, s, z) = [(a * T) / s] + z = a * s(q_t - z) / s + z = a * (q_t - z) + z
- // = a * q_t + (1 - a) * z
+ // LeakyReLU can be written in terms of respective quantized tensors, scales and
+ // zero points as
//
- // We return the quantized value of alpha * T for all values q_t < input_zero_point.
-
- ICHECK_EQ(new_args.size(), 3);
- Expr quantized_data = Cast(new_args[0], DataType::Int(32));
+ // scale_o * (Q_o - zp_o) = alpha * scale_i * (Q_i - zp_i) when Q_i < zp_i (1)
+ // Q_o = Q_i when Q_i >= zp_i (2)
+ //
+ // Since the input qnn params can be different than output qnn params, we first requantize the
+ // input tensor to the output qnn params. After requantizing Q_i, equation (1) becames equation
+ // (3) where Q_i' is the requantized data from Q_i.
+ //
+ // scale_o * (Q_o - zp_o) = alpha * scale_o * (Q_i' - zp_o) when Q_i < zp_i (3)
+ // Q_o = alpha * Q_i' + (1 - alpha) * zp_o when Q_i < zp_i (4)
+ ICHECK_EQ(new_args.size(), 5);
+ Expr data = Cast(new_args[0], DataType::Int(32));
+ Expr input_scale = new_args[1];
Expr input_zero_point = Cast(new_args[2], DataType::Int(32));
+ Expr output_scale = new_args[3];
+ Expr output_zero_point = Cast(new_args[4], DataType::Int(32));
const auto* q_attrs = attrs.as<LeakyReluAttrs>();
auto alpha = q_attrs->alpha;
+ const auto input_shape = get_shape(arg_types[0]);
+ const auto input_dtype = arg_types[0].as<TensorTypeNode>()->dtype;
+
+ // requantize the input to Q_i'
+ auto requantized_expr = RequantizeOrUpcast(data, input_scale, input_zero_point, output_scale,
+ output_zero_point, input_shape);
+
+ // alpha * Q_i'
int32_t fixed_point_multiplier, shift;
std::tie(fixed_point_multiplier, shift) = GetFixedPointMultiplierShift(alpha);
- auto prod = FixedPointMultiply(quantized_data, fixed_point_multiplier, shift);
+ auto prod = FixedPointMultiply(requantized_expr, fixed_point_multiplier, shift);
+ // (1 - alpha) * zp_o
int32_t fixed_point_multiplier_z, shift_z;
std::tie(fixed_point_multiplier_z, shift_z) = GetFixedPointMultiplierShift(1 - alpha);
- auto scaled_z = FixedPointMultiply(input_zero_point, fixed_point_multiplier_z, shift_z);
+ auto scaled_z = FixedPointMultiply(output_zero_point, fixed_point_multiplier_z, shift_z);
+ // alpha * Q_i + (1 - alpha) * zp_o
Review Comment:
// alpha * Q_i' + (1 - alpha) * zp_o
##########
src/relay/qnn/op/leaky_relu.cc:
##########
@@ -82,42 +87,62 @@ Expr QnnLeakyReluCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
// by a small alpha value < 1.
//
// We assume the same scale and zero point for alpha and the input tensor.
- // Let T = s(q_t - z) where q_t is the input arg[0]
- // Then, the quantized value of alpha * T is:
- // q(a * T, s, z) = [(a * T) / s] + z = a * s(q_t - z) / s + z = a * (q_t - z) + z
- // = a * q_t + (1 - a) * z
+ // LeakyReLU can be written in terms of respective quantized tensors, scales and
+ // zero points as
//
- // We return the quantized value of alpha * T for all values q_t < input_zero_point.
-
- ICHECK_EQ(new_args.size(), 3);
- Expr quantized_data = Cast(new_args[0], DataType::Int(32));
+ // scale_o * (Q_o - zp_o) = alpha * scale_i * (Q_i - zp_i) when Q_i < zp_i (1)
+ // Q_o = Q_i when Q_i >= zp_i (2)
+ //
+ // Since the input qnn params can be different than output qnn params, we first requantize the
+ // input tensor to the output qnn params. After requantizing Q_i, equation (1) becames equation
+ // (3) where Q_i' is the requantized data from Q_i.
+ //
+ // scale_o * (Q_o - zp_o) = alpha * scale_o * (Q_i' - zp_o) when Q_i < zp_i (3)
+ // Q_o = alpha * Q_i' + (1 - alpha) * zp_o when Q_i < zp_i (4)
Review Comment:
For complete sake, also include case where `Q_i >= zp_i`
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