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未验证 提交 ec11135d 编写于 作者: Z Zhen Wang 提交者： GitHub
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Merge pull request #16341 from wzzju/add_channel_wise_in_quant_pass 

Add channel wise in quant pass.
上级 e235882c 8965819f
隐藏空白更改
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Showing with 655 addition and 146 deletion
paddle/fluid/operators/fake_dequantize_op.cc
浏览文件 @ ec11135d
......@@ -33,8 +33,51 @@ struct DequantizeFunctor<platform::CPUDeviceContext, T> {
}
};
template <typename T>
struct ChannelDequantizeFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& dev_ctx,
const framework::Tensor* in, const framework::Tensor** scales,
const int scale_num, T max_range, framework::Tensor* out) {
if (scale_num == 1) {
const int channel = in->dims()[0];
const T* scale_factor = scales[0]->data<T>();
for (int i = 0; i < channel; i++) {
T s = scale_factor[i];
framework::Tensor one_channel_in = in->Slice(i, i + 1);
framework::Tensor one_channel_out = out->Slice(i, i + 1);
auto in_e = framework::EigenVector<T>::Flatten(one_channel_in);
auto out_e = framework::EigenVector<T>::Flatten(one_channel_out);
auto& dev = *dev_ctx.eigen_device();
out_e.device(dev) = (s / max_range) * in_e;
}
} else if (scale_num == 2) {
int batch_size = in->dims()[0];
int channel = in->dims()[1];
const T* scale_one = scales[0]->data<T>();
const T* scale_two = scales[1]->data<T>();
for (int i = 0; i < batch_size; i++) {
framework::Tensor one_batch_in = in->Slice(i, i + 1).Resize(
framework::slice_ddim(in->dims(), 1, in->dims().size()));
framework::Tensor one_batch_out = out->Slice(i, i + 1).Resize(
framework::slice_ddim(out->dims(), 1, out->dims().size()));
for (int j = 0; j < channel; j++) {
T s = scale_one[j];
framework::Tensor one_channel_in = one_batch_in.Slice(j, j + 1);
framework::Tensor one_channel_out = one_batch_out.Slice(j, j + 1);
auto in_e = framework::EigenVector<T>::Flatten(one_channel_in);
auto out_e = framework::EigenVector<T>::Flatten(one_channel_out);
auto& dev = *dev_ctx.eigen_device();
out_e.device(dev) = (s * scale_two[0] / max_range) * in_e;
}
}
}
}
};
template struct DequantizeFunctor<platform::CPUDeviceContext, float>;
template struct DequantizeFunctor<platform::CPUDeviceContext, double>;
template struct ChannelDequantizeFunctor<platform::CPUDeviceContext, float>;
template struct ChannelDequantizeFunctor<platform::CPUDeviceContext, double>;
class FakeDequantizeMaxAbsOp : public framework::OperatorWithKernel {
public:
......
paddle/fluid/operators/fake_dequantize_op.cu
浏览文件 @ ec11135d
......@@ -44,8 +44,66 @@ struct DequantizeFunctor<platform::CUDADeviceContext, T> {
}
};
template <typename T>
__global__ void DequantizeOneScale(const T* in, const T* scale, T max_range,
int num, int channel, T* out) {
int tid = threadIdx.x;
int channel_size = num / channel;
const T* in_c = in + blockIdx.x * channel_size;
T* out_c = out + blockIdx.x * channel_size;
for (int i = tid; i < channel_size; i += blockDim.x) {
out_c[i] = in_c[i] * scale[blockIdx.x] / max_range;
}
}
template <typename T>
__global__ void DequantizeTwoScale(const T* in, const T* scale_one,
const T* scale_two, T max_range, int num,
int batch_size, int channel, T* out) {
int tid = threadIdx.x;
int channel_size = num / (batch_size * channel);
int scale_index = blockIdx.x % channel;
const T* in_c = in + blockIdx.x * channel_size;
T* out_c = out + blockIdx.x * channel_size;
for (int i = tid; i < channel_size; i += blockDim.x) {
out_c[i] = in_c[i] * scale_one[scale_index] * scale_two[0] / max_range;
}
}
template <typename T>
struct ChannelDequantizeFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& dev_ctx,
const framework::Tensor* in, const framework::Tensor** scales,
const int scale_num, T max_range, framework::Tensor* out) {
const T* in_data = in->data<T>();
T* out_data = out->mutable_data<T>(dev_ctx.GetPlace());
if (scale_num == 1) {
int num = in->numel();
int channel = in->dims()[0];
const T* scale_factor = scales[0]->data<T>();
int block = 1024;
int grid = channel;
DequantizeOneScale<T><<<grid, block, 0, dev_ctx.stream()>>>(
in_data, scale_factor, max_range, num, channel, out_data);
} else if (scale_num == 2) {
int num = in->numel();
int batch_size = in->dims()[0];
int channel = in->dims()[1];
const T* scale_one = scales[0]->data<T>();
const T* scale_two = scales[1]->data<T>();
int block = 1024;
int grid = batch_size * channel;
DequantizeTwoScale<T><<<grid, block, 0, dev_ctx.stream()>>>(
in_data, scale_one, scale_two, max_range, num, batch_size, channel,
out_data);
}
}
};
template struct DequantizeFunctor<platform::CUDADeviceContext, float>;
template struct DequantizeFunctor<platform::CUDADeviceContext, double>;
template struct ChannelDequantizeFunctor<platform::CUDADeviceContext, float>;
template struct ChannelDequantizeFunctor<platform::CUDADeviceContext, double>;
} // namespace operators
} // namespace paddle
......
paddle/fluid/operators/fake_dequantize_op.h
浏览文件 @ ec11135d
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -28,6 +29,13 @@ struct DequantizeFunctor {
framework::Tensor* out);
};
template <typename DeviceContext, typename T>
struct ChannelDequantizeFunctor {
void operator()(const DeviceContext& dev_ctx, const framework::Tensor* in,
const framework::Tensor** scales, const int scale_num,
T max_range, framework::Tensor* out);
};
template <typename DeviceContext, typename T>
class FakeDequantizeMaxAbsKernel : public framework::OpKernel<T> {
public:
......@@ -54,32 +62,33 @@ class FakeChannelWiseDequantizeMaxAbsKernel : public framework::OpKernel<T> {
auto scales = ctx.MultiInput<framework::Tensor>("Scales");
auto* out = ctx.Output<framework::Tensor>("Out");
PADDLE_ENFORCE_EQ(scales[0]->numel(), in->dims()[0],
"The number of first scale values must be the same with "
"first dimension value of Input(X).");
auto quant_bits = ctx.Attr<std::vector<int>>("quant_bits");
int max_range = std::pow(2, quant_bits[0] - 1) - 1;
int max_range = 1;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
out->mutable_data<T>(dev_ctx.GetPlace());
auto dequant = DequantizeFunctor<DeviceContext, T>();
for (int64_t i = 0; i < in->dims()[0]; i++) {
framework::Tensor one_channel_in = in->Slice(i, i + 1);
framework::Tensor one_channel_out = out->Slice(i, i + 1);
framework::Tensor one_channel_scale = scales[0]->Slice(i, i + 1);
dequant(dev_ctx, &one_channel_in, &one_channel_scale,
static_cast<T>(max_range), &one_channel_out);
}
if (scales.size() == 2) {
int scale_num = scales.size();
if (scale_num == 1) {
PADDLE_ENFORCE_EQ(
scales[0]->numel(), in->dims()[0],
"The number of first scale values must be the same with "
"first dimension value of Input(X) when the `Scales` has only one "
"element.");
max_range *= (std::pow(2, quant_bits[0] - 1) - 1);
} else if (scale_num == 2) {
PADDLE_ENFORCE_EQ(
scales[0]->numel(), in->dims()[1],
"The number of first scale values must be the same with "
"second dimension value of Input(X) when the `Scales` has two "
"elements.");
PADDLE_ENFORCE_EQ(
scales[1]->numel(), 1,
"The second scale tensor should only have one value at now.");
max_range = std::pow(2, quant_bits[1] - 1) - 1;
dequant(dev_ctx, out, scales[1], static_cast<T>(max_range), out);
max_range *= (std::pow(2, quant_bits[0] - 1) - 1) *
(std::pow(2, quant_bits[1] - 1) - 1);
}
ChannelDequantizeFunctor<DeviceContext, T>()(
dev_ctx, in, scales.data(), scale_num, static_cast<T>(max_range), out);
}
};
......
paddle/fluid/operators/fake_quantize_op.cc
浏览文件 @ ec11135d
......@@ -37,6 +37,21 @@ struct FindAbsMaxFunctor<platform::CPUDeviceContext, T> {
template struct FindAbsMaxFunctor<platform::CPUDeviceContext, float>;
template <typename T>
struct FindChannelAbsMaxFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx, const T* in,
const int num, const int channel, T* out) {
const int channel_size = num / channel;
for (int i = 0; i < channel; i++) {
auto* start = in + i * channel_size;
auto* end = in + (i + 1) * channel_size;
out[i] = std::abs(*(std::max_element(start, end, Compare<T>())));
}
}
};
template struct FindChannelAbsMaxFunctor<platform::CPUDeviceContext, float>;
template <typename T>
struct ClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx,
......@@ -53,6 +68,36 @@ struct ClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
template struct ClipAndFakeQuantFunctor<platform::CPUDeviceContext, float>;
template <typename T>
struct ChannelClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx,
const framework::Tensor& in, const framework::Tensor& scale,
const int bin_cnt, const int channel,
framework::Tensor* out) {
auto* scale_data = scale.data<T>();
auto* in_data = in.data<T>();
auto* out_data = out->mutable_data<T>(ctx.GetPlace());
const int channel_size = in.numel() / channel;
platform::Transform<platform::CPUDeviceContext> trans;
for (int i = 0; i < channel; i++) {
T s = scale_data[i];
auto* start = in_data + i * channel_size;
auto* end = in_data + (i + 1) * channel_size;
trans(ctx, start, end, out_data + i * channel_size,
ClipFunctor<T>(-s, s));
}
for (int i = 0; i < channel; i++) {
T s = scale_data[i];
framework::Tensor one_channel_out = out->Slice(i, i + 1);
auto out_e = framework::EigenVector<T>::Flatten(one_channel_out);
out_e.device(*ctx.eigen_device()) = (bin_cnt / s * out_e).round();
}
}
};
template struct ChannelClipAndFakeQuantFunctor<platform::CPUDeviceContext,
float>;
template <typename T>
struct FindRangeAbsMaxFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx,
......@@ -169,10 +214,10 @@ class FakeChannelWiseQuantizeAbsMaxOp : public framework::OperatorWithKernel {
ctx->HasOutput("Out"),
"Output(Out) of FakeChannelWiseQuantizeOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("OutScales"),
"Output(Scales) of FakeChannelWiseQuantizeOp should not be null.");
ctx->HasOutput("OutScale"),
"Output(Scale) of FakeChannelWiseQuantizeOp should not be null.");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->SetOutputDim("OutScales", {ctx->GetInputDim("X")[0]});
ctx->SetOutputDim("OutScale", {ctx->GetInputDim("X")[0]});
ctx->ShareLoD("X", /*->*/ "Out");
}
......@@ -192,7 +237,7 @@ class FakeChannelWiseQuantizeAbsMaxOpMaker
AddOutput("Out",
"(Tensor) Output of quantized low level tensor, "
"but also saved as float data type.");
AddOutput("OutScales", "(Tensor) Current channel wise scale");
AddOutput("OutScale", "(Tensor) Current channel wise scale");
AddAttr<int>("bit_length", "(int, default 8)")
.SetDefault(8)
.AddCustomChecker([](const int& bit_length) {
......
paddle/fluid/operators/fake_quantize_op.cu
浏览文件 @ ec11135d
......@@ -74,6 +74,45 @@ struct FindAbsMaxFunctor<platform::CUDADeviceContext, T> {
template struct FindAbsMaxFunctor<platform::CUDADeviceContext, float>;
template <typename T>
__global__ void FindChannelAbsMaxKernel(const T* in, const int n, const int c,
T* out) {
int tid = threadIdx.x;
int channel_size = n / c;
const T* in_c = in + blockIdx.x * channel_size;
extern __shared__ T shared_max_data[];
shared_max_data[tid] = T(0);
for (int i = tid; i < channel_size; i += blockDim.x) {
T tmp = fabs(in_c[i]);
if (tmp > shared_max_data[tid]) {
shared_max_data[tid] = tmp;
}
}
__syncthreads();
for (int i = blockDim.x / 2; i > 0; i >>= 1) {
if (tid < i && (shared_max_data[tid] < shared_max_data[tid + i])) {
shared_max_data[tid] = shared_max_data[tid + i];
}
__syncthreads();
}
if (tid == 0) {
out[blockIdx.x] = shared_max_data[0];
}
}
template <typename T>
struct FindChannelAbsMaxFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx, const T* in,
const int num, const int channel, T* out) {
int block = 1024;
int grid = channel;
FindChannelAbsMaxKernel<T><<<grid, block, 1024 * sizeof(T), ctx.stream()>>>(
in, num, channel, out);
}
};
template struct FindChannelAbsMaxFunctor<platform::CUDADeviceContext, float>;
template <typename T>
__global__ void ClipAndQuantKernel(const T* in, const T* scale,
const int bin_cnt, const int n, T* out) {
......@@ -82,14 +121,76 @@ __global__ void ClipAndQuantKernel(const T* in, const T* scale,
T s = scale[0];
for (int i = bid; i < n; i += blockDim.x * gridDim.x) {
T x = in[bid];
T x = in[i];
T v = x > s ? s : x;
v = v < -s ? -s : v;
v = bin_cnt / s * v;
out[bid] = round(v);
out[i] = round(v);
}
}
template <typename T>
struct ClipAndFakeQuantFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx,
const framework::Tensor& in, const framework::Tensor& scale,
const int bin_cnt, framework::Tensor* out) {
int num = in.numel();
int block = 1024;
int grid = (block - 1 + num) / block;
const T* in_data = in.data<T>();
const T* scale_data = scale.data<T>();
T* out_data = out->mutable_data<T>(ctx.GetPlace());
ClipAndQuantKernel<T><<<grid, block, 0, ctx.stream()>>>(
in_data, scale_data, bin_cnt, num, out_data);
}
};
template struct ClipAndFakeQuantFunctor<platform::CUDADeviceContext, float>;
template <typename T>
__global__ void ChannelClipAndQuantKernel(const T* in, const T* scale,
const int bin_cnt, const int n,
const int c, T* out) {
int tid = threadIdx.x;
int channel_size = n / c;
const T* in_c = in + blockIdx.x * channel_size;
T* out_c = out + blockIdx.x * channel_size;
T s = scale[blockIdx.x];
for (int i = tid; i < channel_size; i += blockDim.x) {
T x = in_c[i];
T v = x > s ? s : x;
v = v < -s ? -s : v;
v = bin_cnt / s * v;
out_c[i] = round(v);
}
}
template <typename T>
struct ChannelClipAndFakeQuantFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx,
const framework::Tensor& in, const framework::Tensor& scale,
const int bin_cnt, const int channel,
framework::Tensor* out) {
int num = in.numel();
int block = 1024;
int grid = channel;
const T* in_data = in.data<T>();
const T* scale_data = scale.data<T>();
T* out_data = out->mutable_data<T>(ctx.GetPlace());
ChannelClipAndQuantKernel<T><<<grid, block, 0, ctx.stream()>>>(
in_data, scale_data, bin_cnt, num, channel, out_data);
}
};
template struct ChannelClipAndFakeQuantFunctor<platform::CUDADeviceContext,
float>;
template <typename T>
__global__ void FindRangeAbsMaxAndFillArray(const T* cur_scale,
const T* last_scale,
......@@ -182,26 +283,6 @@ struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext, T> {
template struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext,
float>;
template <typename T>
struct ClipAndFakeQuantFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx,
const framework::Tensor& in, const framework::Tensor& scale,
const int bin_cnt, framework::Tensor* out) {
int num = in.numel();
int block = 1024;
int grid = (block - 1 + num) / block;
const T* in_data = in.data<T>();
const T* scale_data = scale.data<T>();
T* out_data = out->mutable_data<T>(ctx.GetPlace());
ClipAndQuantKernel<T><<<grid, block, 0, ctx.stream()>>>(
in_data, scale_data, bin_cnt, num, out_data);
}
};
template struct ClipAndFakeQuantFunctor<platform::CUDADeviceContext, float>;
} // namespace operators
} // namespace paddle
......
paddle/fluid/operators/fake_quantize_op.h
浏览文件 @ ec11135d
......@@ -42,6 +42,19 @@ struct FindRangeAbsMaxFunctor {
framework::Tensor* scales_arr, framework::Tensor* out_scale);
};
template <typename DeviceContext, typename T>
struct FindChannelAbsMaxFunctor {
void operator()(const DeviceContext& ctx, const T* in, const int num,
const int channel, T* out);
};
template <typename DeviceContext, typename T>
struct ChannelClipAndFakeQuantFunctor {
void operator()(const DeviceContext& ctx, const framework::Tensor& in,
const framework::Tensor& scale, const int bin_cnt,
const int channel, framework::Tensor* out);
};
template <typename DeviceContext, typename T>
struct FindMovingAverageAbsMaxFunctor {
void operator()(const DeviceContext& ctx, const framework::Tensor& in_accum,
......@@ -78,29 +91,18 @@ class FakeChannelWiseQuantizeAbsMaxKernel : public framework::OpKernel<T> {
auto* in = context.Input<framework::Tensor>("X");
auto* out = context.Output<framework::Tensor>("Out");
auto* out_scales = context.Output<framework::Tensor>("OutScales");
T* out_scales_data = out_scales->mutable_data<T>(context.GetPlace());
auto* out_scale = context.Output<framework::Tensor>("OutScale");
T* out_scale_data = out_scale->mutable_data<T>(context.GetPlace());
out->mutable_data<T>(context.GetPlace());
int bit_length = context.Attr<int>("bit_length");
int bin_cnt = std::pow(2, bit_length - 1) - 1;
auto& dev_ctx = context.template device_context<DeviceContext>();
auto find_abs_max = FindAbsMaxFunctor<DeviceContext, T>();
for (int64_t i = 0; i < in->dims()[0]; i++) {
framework::Tensor one_channel = in->Slice(i, i + 1);
const T* one_channel_data = one_channel.data<T>();
find_abs_max(dev_ctx, one_channel_data, one_channel.numel(),
&out_scales_data[i]);
}
auto clip_quant = ClipAndFakeQuantFunctor<DeviceContext, T>();
for (int64_t i = 0; i < in->dims()[0]; i++) {
framework::Tensor one_channel_in = in->Slice(i, i + 1);
framework::Tensor one_channel_out = out->Slice(i, i + 1);
framework::Tensor one_channel_scale = out_scales->Slice(i, i + 1);
clip_quant(dev_ctx, one_channel_in, one_channel_scale, bin_cnt,
&one_channel_out);
}
FindChannelAbsMaxFunctor<DeviceContext, T>()(
dev_ctx, in->data<T>(), in->numel(), in->dims()[0], out_scale_data);
ChannelClipAndFakeQuantFunctor<DeviceContext, T>()(
dev_ctx, *in, *out_scale, bin_cnt, in->dims()[0], out);
}
};
......
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
浏览文件 @ ec11135d
......@@ -22,6 +22,7 @@ from ....framework import IrGraph
from ....framework import IrNode
from ....framework import Program
from ....initializer import Constant
from ....initializer import NumpyArrayInitializer
from .... import unique_name
__all__ = [
......@@ -54,14 +55,15 @@ class QuantizationTransformPass(object):
the bias is not quantized.
activation_bits (int): quantization bit number for activation.
activation_quantize_type (str): quantization type for activation,
now support 'abs_max', 'range_abs_max'. If use 'abs_max' mode,
the quantization scale will be calculated dynamically each step
in both training and testing period. If use 'range_abs_max',
a static quantization scale will be calculated during training
and used in inference.
now support 'abs_max', 'range_abs_max' and 'moving_average_abs_max'.
If use 'abs_max' mode, the quantization scale will be calculated
dynamically each step in both training and testing period. If use
'range_abs_max', a static quantization scale will be calculated
during training and used in inference.
weight_quantize_type (str): quantization type for weights,
support 'abs_max'. The 'range_abs_max' usually is not used for
weight, since weights are fixed once the model is well trained.
support 'abs_max' and 'channel_wise_abs_max'. The 'range_abs_max'
usually is not used for weight, since weights are fixed once the
model is well trained.
window_size (int): the window size for 'range_abs_max' quantization.
Examples:
......@@ -84,7 +86,11 @@ class QuantizationTransformPass(object):
self._weight_bits = weight_bits
self._activation_bits = activation_bits
quant_type = ['abs_max', 'range_abs_max', 'moving_average_abs_max']
quant_type = [
'abs_max', 'channel_wise_abs_max', 'range_abs_max',
'moving_average_abs_max'
]
assert activation_quantize_type != 'channel_wise_abs_max', "The activation quantization type does not support 'channel_wise_abs_max'."
if activation_quantize_type not in quant_type:
raise ValueError(
"Unknown activation_quantize_type : '%s'. It can only be ",
......@@ -93,7 +99,7 @@ class QuantizationTransformPass(object):
if weight_quantize_type not in quant_type:
raise ValueError(
"Unknown weight_quantize_type: '%s'. It can only be ",
"'abs_max' or 'range_abs_max' or 'moving_average_abs_max'.",
"'abs_max' or 'channel_wise_abs_max' or 'range_abs_max' or 'moving_average_abs_max'.",
str(weight_quantize_type))
self._activation_quantize_type = activation_quantize_type
......@@ -103,6 +109,7 @@ class QuantizationTransformPass(object):
self._need_initialized = collections.OrderedDict()
self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
self._conv_ops = ['conv2d', 'depthwise_conv2d']
self._quantizable_grad_ops = [
'%s_grad' % (op) for op in self._quantizable_ops
]
......@@ -135,10 +142,26 @@ class QuantizationTransformPass(object):
else self._activation_bits
quant_type = self._weight_quantize_type if var_node.name() \
in persistable_vars else self._activation_quantize_type
quant_var_node, scale_var_node = self._insert_quant_op(
graph, var_node, quant_bits, quant_type)
dequant_var_node = self._insert_dequant_op(
graph, quant_var_node, scale_var_node, quant_bits)
if quant_type == 'channel_wise_abs_max':
assert var_node.name(
) in persistable_vars, "'channel_wise_abs_max' can only be applied on weights."
if op.name() in self._conv_ops:
quant_var_node, scale_var_node = self._insert_channel_quant_op(
graph, var_node, quant_bits)
dequant_var_node = self._insert_channel_dequant_op(
graph, quant_var_node, [scale_var_node],
[quant_bits])
else:
quant_var_node, scale_var_node = self._insert_quant_op(
graph, var_node, quant_bits, 'abs_max')
dequant_var_node = self._insert_dequant_op(
graph, quant_var_node, scale_var_node,
quant_bits)
else:
quant_var_node, scale_var_node = self._insert_quant_op(
graph, var_node, quant_bits, quant_type)
dequant_var_node = self._insert_dequant_op(
graph, quant_var_node, scale_var_node, quant_bits)
dequantized_vars[var_node.name()] = dequant_var_node
graph.update_input_link(var_node, dequant_var_node, op)
......@@ -244,7 +267,7 @@ class QuantizationTransformPass(object):
scale_var_node = graph.create_var_node(
name=self._quantized_scale_name(var_node.name()),
var_type=var_node.type(),
shape=var_node.shape(),
shape=[1],
var_dtype=var_node.dtype())
quant_op_node = graph.create_op_node(
op_type='fake_quantize_abs_max',
......@@ -384,6 +407,36 @@ class QuantizationTransformPass(object):
return quant_var_node, scale_out_node
def _insert_channel_quant_op(self, graph, var_node, quant_bits):
"""
Insert fake_channel_wise_quantize_abs_max op in the graph.
"""
assert var_node.is_var(), '{} is not a var'.format(var_node.name())
quant_var_node = graph.create_var_node(
name=self._quantized_var_name(var_node.name()),
var_type=var_node.type(),
shape=var_node.shape(),
var_dtype=var_node.dtype())
scale_var_node = graph.create_var_node(
name=self._quantized_scale_name(var_node.name()),
var_type=var_node.type(),
shape=[var_node.shape()[0]],
var_dtype=var_node.dtype())
quant_op_node = graph.create_op_node(
op_type='fake_channel_wise_quantize_abs_max',
attrs={
'bit_length': quant_bits,
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': var_node},
outputs={'Out': quant_var_node,
'OutScale': scale_var_node})
graph.link_to(var_node, quant_op_node)
graph.link_to(quant_op_node, quant_var_node)
graph.link_to(quant_op_node, scale_var_node)
return quant_var_node, scale_var_node
def _insert_dequant_op(self, graph, var_node, scale_var_node, quant_bits):
"""
Insert fake_dequantize_op in the graph.
......@@ -410,6 +463,33 @@ class QuantizationTransformPass(object):
graph.link_to(dequant_op_node, dequant_var_node)
return dequant_var_node
def _insert_channel_dequant_op(self, graph, var_node, scale_var_nodes,
quant_bits):
"""
Insert fake_channel_wise_dequantize_max_abs in the graph.
"""
assert var_node.is_var(), '{} is not a var'.format(var_node.name())
dequant_var_node = graph.create_var_node(
name=self._dequantized_var_name(var_node.name()),
var_type=var_node.type(),
shape=var_node.shape(),
var_dtype=var_node.dtype())
dequant_op_node = graph.create_op_node(
op_type='fake_channel_wise_dequantize_max_abs',
attrs={
'quant_bits': quant_bits,
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': var_node,
'Scales': scale_var_nodes},
outputs={'Out': dequant_var_node})
graph.link_to(var_node, dequant_op_node)
for scale_n in scale_var_nodes:
graph.link_to(scale_n, dequant_op_node)
graph.link_to(dequant_op_node, dequant_var_node)
return dequant_var_node
def _quantized_var_name(self, var_name):
"""
Return quantized variable name for the input `var_name`.
......@@ -442,7 +522,7 @@ class QuantizationFreezePass(object):
place(fluid.CPUPlace|fluid.CUDAPlace): place is used to restore the weight tensors.
weight_bits (int): quantization bit number for weights.
activation_bits (int): quantization bit number for activation.
weight_quantize_type (str): quantization type for weights, support 'abs_max'.
weight_quantize_type (str): quantization type for weights, support 'abs_max' and 'channel_wise_abs_max'.
The 'range_abs_max' usually is not used for weight, since weights are fixed once the
model is well trained.
"""
......@@ -463,11 +543,15 @@ class QuantizationFreezePass(object):
self._activation_bits = activation_bits
self._weight_quantize_type = weight_quantize_type
self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
self._conv_ops = ['conv2d', 'depthwise_conv2d']
self._fake_quant_op_names = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max',
'fake_quantize_moving_average_abs_max'
'fake_quantize_moving_average_abs_max',
'fake_channel_wise_quantize_abs_max'
]
self._fake_dequant_op_names = [
'fake_dequantize_max_abs', 'fake_channel_wise_dequantize_max_abs'
]
self._fake_dequant_op_names = ['fake_dequantize_max_abs']
self._op_input_rename_map = collections.OrderedDict()
self._op_output_rename_map = collections.OrderedDict()
self._var_scale_map = collections.OrderedDict()
......@@ -489,20 +573,27 @@ class QuantizationFreezePass(object):
if self._weight_quantize_type == 'abs_max':
param = self._load_var(input_arg_name)
scale_v = np.max(np.abs(param))
elif self._weight_quantize_type == 'channel_wise_abs_max':
param = self._load_var(input_arg_name)
if len(param.shape) == 4: # conv2d or depthwise_conv2d
scale_v = []
for i in range(param.shape[0]):
scale_v.append(np.max(np.abs(param[i])))
else:
scale_v = np.max(np.abs(param))
else:
scale_v = self._load_var(
op_node.output('OutScale')[0])[0]
self._var_scale_map[input_arg_name] = scale_v
else:
scale_v = graph.var_node(op_node.output('OutScale')[0])
self._var_scale_map[input_arg_name] = scale_v
if input_arg_name in persistable_vars:
self._remove_fake_quant_and_dequant_op(graph, op_node)
# quantize weight and restore
param_v = self._load_var(input_arg_name)
quantized_param_v = self._quant(param_v, scale_v,
self._weight_bits)
self._restore_var(input_arg_name, quantized_param_v)
else:
scale_v = graph.var_node(op_node.output('OutScale')[0])
self._var_scale_map[input_arg_name] = scale_v
ops = graph.all_op_nodes()
for op_node in ops:
......@@ -514,7 +605,10 @@ class QuantizationFreezePass(object):
for op_node in ops:
op_name = op_node.name()
if op_name in self._quantizable_ops:
self._insert_post_dequant_op(graph, op_node)
if self._weight_quantize_type == 'channel_wise_abs_max' and op_name in self._conv_ops:
self._insert_post_channel_dequant_op(graph, op_node)
else:
self._insert_post_dequant_op(graph, op_node)
for op_node in ops:
# insert dequant_op after fc/conv, need to rename inputs of the followed ops
......@@ -538,9 +632,73 @@ class QuantizationFreezePass(object):
self._op_input_rename_map[k] = self._op_input_rename_map[v]
graph.safe_remove_nodes(op_node)
def _insert_post_channel_dequant_op(self, graph, op_node):
persistable_vars = [p.name() for p in graph.all_persistable_nodes()]
for var_node in op_node.inputs:
name = var_node.name()
if name in self._op_input_rename_map:
old_in = graph.var_node(name)
new_in = graph.var_node(self._op_input_rename_map[name])
new_in.clear_outputs()
graph.update_input_link(old_in, new_in, op_node)
original_var_name = self._original_var_name(name)
scale_v = self._var_scale_map[original_var_name]
if original_var_name in persistable_vars:
assert isinstance(
scale_v,
list), 'The scale of parameter %s is not a list.' % (
original_var_name)
channel_scale = np.array(scale_v)
else:
assert isinstance(scale_v, IrNode)
scale_var_node = self._var_scale_map[original_var_name]
if len(op_node.outputs) != 1:
raise ValueError("Only support one output, but op %s has"
" more than one output." % (op_node.name()))
output_var_node = op_node.outputs[0]
weight_scale_node = graph.create_persistable_node(
name=unique_name.generate('channel_scale'),
var_type=core.VarDesc.VarType.LOD_TENSOR,
shape=[channel_scale.shape[0]],
var_dtype=output_var_node.dtype())
init_program = Program()
weight_scale_var = init_program.global_block().create_var(
name=weight_scale_node.name(),
shape=weight_scale_node.shape(),
dtype=weight_scale_node.dtype(),
type=weight_scale_node.type(),
lod_level=weight_scale_node.var().lod_level(),
persistable=weight_scale_node.persistable())
initializer = NumpyArrayInitializer(value=channel_scale)
initializer(weight_scale_var, init_program.global_block())
exe = Executor(self._place)
exe.run(program=init_program, scope=self._scope)
dequant_var_node = graph.create_var_node(
name=self._dequantized_var_name(output_var_node.name()),
var_type=output_var_node.type(),
shape=output_var_node.shape(),
var_dtype=output_var_node.dtype())
dequant_op_node = graph.create_op_node(
op_type='fake_channel_wise_dequantize_max_abs',
attrs={
'quant_bits': [self._weight_bits, self._activation_bits],
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={
'X': output_var_node,
'Scales': [weight_scale_node, scale_var_node]
},
outputs={'Out': dequant_var_node})
graph.link_to(output_var_node, dequant_op_node)
graph.link_to(scale_var_node, dequant_op_node)
graph.link_to(weight_scale_node, dequant_op_node)
graph.link_to(dequant_op_node, dequant_var_node)
self._op_output_rename_map[output_var_node.name()] = dequant_var_node
return dequant_var_node
def _insert_post_dequant_op(self, graph, op_node):
max_range = None
scale_var_node = None
persistable_vars = [p.name() for p in graph.all_persistable_nodes()]
for var_node in op_node.inputs:
name = var_node.name()
......@@ -637,7 +795,12 @@ class QuantizationFreezePass(object):
or isinstance(v, np.float64)
def _quant(self, x, scale, num_bits):
return np.round(x / scale * ((1 << (num_bits - 1)) - 1))
if isinstance(scale, list):
for i, s in enumerate(scale):
x[i] = np.round(x[i] / s * ((1 << (num_bits - 1)) - 1))
return x
else:
return np.round(x / scale * ((1 << (num_bits - 1)) - 1))
class ConvertToInt8Pass(object):
......@@ -731,9 +894,13 @@ class TransformForMobilePass(object):
def __init__(self):
self._fake_quant_op_names = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
'fake_quantize_abs_max', 'fake_quantize_range_abs_max',
'fake_quantize_moving_average_abs_max',
'fake_channel_wise_quantize_abs_max'
]
self._fake_dequant_op_names = [
'fake_dequantize_max_abs', 'fake_channel_wise_dequantize_max_abs'
]
self._fake_dequant_op_names = ['fake_dequantize_max_abs']
def apply(self, graph):
"""
......
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
浏览文件 @ ec11135d
......@@ -127,7 +127,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
arg_name.endswith('.quantized.dequantized'))
self.assertTrue(arg_name in quantized_ops)
def linear_fc_quant(self, quant_type, for_ci=False):
def linear_fc_quant(self, activation_quant_type, for_ci=False):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
......@@ -140,14 +140,15 @@ class TestQuantizationTransformPass(unittest.TestCase):
transform_pass = QuantizationTransformPass(
scope=fluid.global_scope(),
place=place,
activation_quantize_type=quant_type)
activation_quantize_type=activation_quant_type)
transform_pass.apply(graph)
if not for_ci:
marked_nodes = set()
for op in graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
graph.draw('.', 'quantize_fc_' + quant_type, marked_nodes)
graph.draw('.', 'quantize_fc_' + activation_quant_type,
marked_nodes)
program = graph.to_program()
self.check_program(transform_pass, program)
val_graph = IrGraph(core.Graph(program.desc), for_test=False)
......@@ -156,7 +157,8 @@ class TestQuantizationTransformPass(unittest.TestCase):
for op in val_graph.all_op_nodes():
if op.name().find('quantize') > -1:
val_marked_nodes.add(op)
val_graph.draw('.', 'val_fc_' + quant_type, val_marked_nodes)
val_graph.draw('.', 'val_fc_' + activation_quant_type,
val_marked_nodes)
def test_linear_fc_quant_abs_max(self):
self.linear_fc_quant('abs_max', for_ci=True)
......@@ -167,7 +169,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
def test_linear_fc_quant_moving_average_abs_max(self):
self.linear_fc_quant('moving_average_abs_max', for_ci=True)
def residual_block_quant(self, quant_type, for_ci=False):
def residual_block_quant(self, activation_quant_type, for_ci=False):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
......@@ -180,14 +182,15 @@ class TestQuantizationTransformPass(unittest.TestCase):
transform_pass = QuantizationTransformPass(
scope=fluid.global_scope(),
place=place,
activation_quantize_type=quant_type)
activation_quantize_type=activation_quant_type)
transform_pass.apply(graph)
if not for_ci:
marked_nodes = set()
for op in graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
graph.draw('.', 'quantize_residual_' + quant_type, marked_nodes)
graph.draw('.', 'quantize_residual_' + activation_quant_type,
marked_nodes)
program = graph.to_program()
self.check_program(transform_pass, program)
val_graph = IrGraph(core.Graph(program.desc), for_test=False)
......@@ -196,7 +199,8 @@ class TestQuantizationTransformPass(unittest.TestCase):
for op in val_graph.all_op_nodes():
if op.name().find('quantize') > -1:
val_marked_nodes.add(op)
val_graph.draw('.', 'val_residual_' + quant_type, val_marked_nodes)
val_graph.draw('.', 'val_residual_' + activation_quant_type,
val_marked_nodes)
def test_residual_block_abs_max(self):
self.residual_block_quant('abs_max', for_ci=True)
......@@ -209,7 +213,12 @@ class TestQuantizationTransformPass(unittest.TestCase):
class TestQuantizationFreezePass(unittest.TestCase):
def freeze_graph(self, use_cuda, seed, quant_type, for_ci=False):
def freeze_graph(self,
use_cuda,
seed,
activation_quant_type,
weight_quant_type='abs_max',
for_ci=False):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
......@@ -243,7 +252,12 @@ class TestQuantizationFreezePass(unittest.TestCase):
with fluid.scope_guard(scope):
exe.run(startup)
transform_pass = QuantizationTransformPass(
scope=scope, place=place, activation_quantize_type=quant_type)
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type)
#transform_pass = QuantizationTransformPass(
# scope=scope, place=place, activation_quantize_type=activation_quant_type)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
dev_name = '_gpu_' if use_cuda else '_cpu_'
......@@ -252,12 +266,14 @@ class TestQuantizationFreezePass(unittest.TestCase):
for op in main_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
main_graph.draw('.', 'main' + dev_name + quant_type, marked_nodes)
main_graph.draw('.', 'main' + dev_name + activation_quant_type + '_'
+ weight_quant_type, marked_nodes)
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test' + dev_name + quant_type, marked_nodes)
test_graph.draw('.', 'test' + dev_name + activation_quant_type + '_'
+ weight_quant_type, marked_nodes)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
......@@ -282,8 +298,9 @@ class TestQuantizationFreezePass(unittest.TestCase):
feed=feeder.feed(data),
fetch_list=[loss])
if not for_ci:
print('{}: {}'.format('loss' + dev_name + quant_type,
loss_v))
print('{}: {}'.format('loss' + dev_name +
activation_quant_type + '_' +
weight_quant_type, loss_v))
test_data = next(test_reader())
with fluid.program_guard(quantized_test_program):
......@@ -296,14 +313,17 @@ class TestQuantizationFreezePass(unittest.TestCase):
fetch_list=[loss, w_var])
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(scope=scope, place=place)
freeze_pass = QuantizationFreezePass(
scope=scope, place=place, weight_quantize_type=weight_quant_type)
#freeze_pass = QuantizationFreezePass(scope=scope, place=place)
freeze_pass.apply(test_graph)
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_freeze' + dev_name + quant_type,
test_graph.draw('.', 'test_freeze' + dev_name +
activation_quant_type + '_' + weight_quant_type,
marked_nodes)
server_program = test_graph.to_program()
......@@ -313,18 +333,20 @@ class TestQuantizationFreezePass(unittest.TestCase):
fetch_list=[loss])
self.assertAlmostEqual(test_loss1, test_loss2, delta=5e-3)
if not for_ci:
print('{}: {}'.format('test_loss1' + dev_name + quant_type,
test_loss1))
print('{}: {}'.format('test_loss2' + dev_name + quant_type,
test_loss2))
print(
'{}: {}'.format('test_loss1' + dev_name + activation_quant_type
+ '_' + weight_quant_type, test_loss1))
print(
'{}: {}'.format('test_loss2' + dev_name + activation_quant_type
+ '_' + weight_quant_type, test_loss2))
w_freeze = np.array(scope.find_var('conv2d_1.w_0').get_tensor())
# Maybe failed, this is due to the calculation precision
# self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant))
if not for_ci:
print('{}: {}'.format('w_freeze' + dev_name + quant_type,
np.sum(w_freeze)))
print('{}: {}'.format('w_quant' + dev_name + quant_type,
np.sum(w_quant)))
print('{}: {}'.format('w_freeze' + dev_name + activation_quant_type
+ '_' + weight_quant_type, np.sum(w_freeze)))
print('{}: {}'.format('w_quant' + dev_name + activation_quant_type +
'_' + weight_quant_type, np.sum(w_quant)))
# Convert parameter to 8-bit.
convert_int8_pass = ConvertToInt8Pass(scope=scope, place=place)
......@@ -334,26 +356,28 @@ class TestQuantizationFreezePass(unittest.TestCase):
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_int8' + dev_name + quant_type,
marked_nodes)
test_graph.draw('.', 'test_int8' + dev_name + activation_quant_type
+ '_' + weight_quant_type, marked_nodes)
server_program_int8 = test_graph.to_program()
# Save the 8-bit parameter and model file.
with fluid.scope_guard(scope):
fluid.io.save_inference_model('server_int8' + dev_name + quant_type,
['image', 'label'], [loss], exe,
server_program_int8)
fluid.io.save_inference_model(
'server_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, ['image', 'label'], [loss], exe,
server_program_int8)
# Test whether the 8-bit parameter and model file can be loaded successfully.
[infer, feed, fetch] = fluid.io.load_inference_model(
'server_int8' + dev_name + quant_type, exe)
'server_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, exe)
# Check the loaded 8-bit weight.
w_8bit = np.array(scope.find_var('conv2d_1.w_0.int8').get_tensor())
self.assertEqual(w_8bit.dtype, np.int8)
self.assertEqual(np.sum(w_8bit), np.sum(w_freeze))
if not for_ci:
print('{}: {}'.format('w_8bit' + dev_name + quant_type,
np.sum(w_8bit)))
print('{}: {}'.format('w_freeze' + dev_name + quant_type,
np.sum(w_freeze)))
print('{}: {}'.format('w_8bit' + dev_name + activation_quant_type +
'_' + weight_quant_type, np.sum(w_8bit)))
print('{}: {}'.format('w_freeze' + dev_name + activation_quant_type
+ '_' + weight_quant_type, np.sum(w_freeze)))
mobile_pass = TransformForMobilePass()
mobile_pass.apply(test_graph)
......@@ -362,42 +386,103 @@ class TestQuantizationFreezePass(unittest.TestCase):
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_mobile' + dev_name + quant_type,
test_graph.draw('.', 'test_mobile' + dev_name +
activation_quant_type + '_' + weight_quant_type,
marked_nodes)
mobile_program = test_graph.to_program()
with fluid.scope_guard(scope):
fluid.io.save_inference_model('mobile_int8' + dev_name + quant_type,
['image', 'label'], [loss], exe,
mobile_program)
fluid.io.save_inference_model(
'mobile_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, ['image', 'label'], [loss], exe,
mobile_program)
def test_freeze_graph_cuda_dynamic(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_graph(
True, seed=1, quant_type='abs_max', for_ci=True)
True,
seed=1,
activation_quant_type='abs_max',
weight_quant_type='abs_max',
for_ci=True)
with fluid.unique_name.guard():
self.freeze_graph(
True,
seed=1,
activation_quant_type='abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
def test_freeze_graph_cpu_dynamic(self):
with fluid.unique_name.guard():
self.freeze_graph(False, seed=2, quant_type='abs_max', for_ci=True)
self.freeze_graph(
False,
seed=2,
activation_quant_type='abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
False,
seed=2,
activation_quant_type='abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
def test_freeze_graph_cuda_static(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_graph(
True, seed=1, quant_type='range_abs_max', for_ci=True)
True,
seed=1,
activation_quant_type='range_abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
True,
seed=1,
activation_quant_type='moving_average_abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
True,
seed=1,
quant_type='moving_average_abs_max',
activation_quant_type='range_abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
self.freeze_graph(
True,
seed=1,
activation_quant_type='moving_average_abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
def test_freeze_graph_cpu_static(self):
with fluid.unique_name.guard():
self.freeze_graph(
False, seed=2, quant_type='range_abs_max', for_ci=True)
False,
seed=2,
activation_quant_type='range_abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
False,
seed=2,
activation_quant_type='moving_average_abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
False,
seed=2,
activation_quant_type='range_abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
self.freeze_graph(
False, seed=2, quant_type='moving_average_abs_max', for_ci=True)
False,
seed=2,
activation_quant_type='moving_average_abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_fake_dequantize_op.py
浏览文件 @ ec11135d
......@@ -31,15 +31,27 @@ def dequantize_max_abs(x, scale, max_range):
return y
def channel_wise_quantize_max_abs(x, quant_bit=8):
def channel_wise_quantize_max_abs(x, quant_bit=8, use_second_dim=False):
scales = []
for i in range(x.shape[0]):
scales.append(np.max(np.abs(x[i])).astype("float32"))
y = x.copy()
max_range = math.pow(2, quant_bit - 1) - 1
for i, scale in enumerate(scales):
y[i] = np.round(y[i] / scale * max_range)
if not use_second_dim:
for i in range(x.shape[0]):
scales.append(np.max(np.abs(x[i])).astype("float32"))
y = x.copy()
max_range = math.pow(2, quant_bit - 1) - 1
for i, scale in enumerate(scales):
y[i] = np.round(x[i] / scale * max_range)
else:
for i in range(x.shape[0]):
s = []
for j in range(x.shape[1]):
s.append(np.max(np.abs(x[i][j])).astype("float32"))
scales.append(s)
scales = np.amax(np.array(scales), axis=0)
y = x.copy()
max_range = math.pow(2, quant_bit - 1) - 1
for i in range(x.shape[0]):
for j, scale in enumerate(scales):
y[i][j] = np.round(x[i][j] / scale * max_range)
return y, scales
......@@ -47,10 +59,16 @@ def channel_wise_dequantize_max_abs(x,
scales,
quant_bits,
activation_scale=None):
y = x.copy()
for i in range(x.shape[0]):
y[i] = (scales[i] / (math.pow(2, quant_bits[0] - 1) - 1)) * y[i]
if activation_scale is not None:
if activation_scale is None:
y = x.copy()
for i in range(x.shape[0]):
y[i] = (scales[i] / (math.pow(2, quant_bits[0] - 1) - 1)) * x[i]
else:
y = x.copy()
for i in range(x.shape[0]):
for j in range(x.shape[1]):
y[i][j] = (scales[j] /
(math.pow(2, quant_bits[0] - 1) - 1)) * x[i][j]
y *= activation_scale / (math.pow(2, quant_bits[1] - 1) - 1)
return y
......@@ -65,7 +83,8 @@ class TestFakeChannelWiseDequantizeMaxAbsOpTwoScales(OpTest):
self.set_args()
self.op_type = "fake_channel_wise_dequantize_max_abs"
x = np.random.randn(4, 3, 64, 64).astype(self.data_type)
yq, scales = channel_wise_quantize_max_abs(x, self.quant_bits[0])
yq, scales = channel_wise_quantize_max_abs(
x, self.quant_bits[0], use_second_dim=True)
ydq = channel_wise_dequantize_max_abs(yq, scales, self.quant_bits,
self.activation_scale)
......
python/paddle/fluid/tests/unittests/test_fake_quantize_op.py
浏览文件 @ ec11135d
......@@ -53,7 +53,7 @@ class TestFakeChannelWiseQuantizeOp(OpTest):
self.outputs = {
'Out': outputs,
'OutScales': np.array(scales).astype("float32"),
'OutScale': np.array(scales).astype("float32"),
}
def test_check_output(self):
......
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