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modify input sum array

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pull/4889/head
fuzhiye 5 years ago
parent 933938494d
commit da18781fb7
5 changed files with 26 additions and 14 deletions
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12
mindspore/lite/src/runtime/kernel/arm/fp16/convolution_fp16.cc
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@ -235,10 +235,13 @@ kernel::LiteKernel *CpuConvFp16KernelCreator(const std::vector<lite::tensor::Ten
conv_param->input_w_ = inputs.front()->Width();
conv_param->output_h_ = outputs.front()->Height();
conv_param->output_w_ = outputs.front()->Width();
bool prefer_flag = false;
if (conv_param->output_h_ * conv_param->output_w_ > 64) {
prefer_flag = true;
}
kernel::LiteKernel *kernel = nullptr;
if (kernel_h == 3 && kernel_w == 3 && stride_h == 1 && stride_w == 1 && dilation_h == 1 && dilation_w == 1) {
kernel = new (std::nothrow) kernel::Convolution3x3FP16CPUKernel(opParameter, inputs, outputs, ctx, primitive);
} else if (kernel_h == 1 && kernel_w == 1) {
if (kernel_h == 1 && kernel_w == 1) {
kernel = new (std::nothrow) kernel::Convolution1x1FP16CPUKernel(opParameter, inputs, outputs, ctx, primitive);
} else {
bool use_winograd = false;
@ -249,6 +252,9 @@ kernel::LiteKernel *CpuConvFp16KernelCreator(const std::vector<lite::tensor::Ten
if (use_winograd) {
kernel = new (std::nothrow)
kernel::ConvolutionWinogradFP16CPUKernel(opParameter, inputs, outputs, ctx, primitive, out_unit);
} else if (prefer_flag && kernel_h == 3 && kernel_w == 3 && stride_h == 1 && stride_w == 1 && dilation_h == 1 &&
dilation_w == 1) {
kernel = new (std::nothrow) kernel::Convolution3x3FP16CPUKernel(opParameter, inputs, outputs, ctx, primitive);
}
if (kernel_h != 1 && kernel_w != 1 && !use_winograd) {
kernel = new (std::nothrow) kernel::ConvolutionFP16CPUKernel(opParameter, inputs, outputs, ctx, primitive);

4
mindspore/lite/src/runtime/kernel/arm/int8/convolution_int8.cc
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@ -115,7 +115,7 @@ int ConvolutionInt8CPUKernel::InitWeightBias() {
size_t input_sum_size;
if (conv_quant_arg_->per_channel_ & FILTER_PER_CHANNEL) {
input_sum_size = conv_param_->output_channel_ * tile_num_ * thread_count_ * sizeof(int32_t);
input_sum_size = oc4 * C4NUM * tile_num_ * thread_count_ * sizeof(int32_t);
} else {
input_sum_size = tile_num_ * thread_count_ * sizeof(int32_t);
}
@ -202,7 +202,7 @@ int ConvolutionInt8CPUKernel::InitWeightBiasOpt() {
size_t input_sum_size;
if (conv_quant_arg_->per_channel_ & FILTER_PER_CHANNEL) {
input_sum_size = conv_param_->output_channel_ * tile_num_ * thread_count_ * sizeof(int32_t);
input_sum_size = oc4 * C4NUM * tile_num_ * thread_count_ * sizeof(int32_t);
} else {
input_sum_size = tile_num_ * thread_count_ * sizeof(int32_t);
}

6
mindspore/lite/src/runtime/kernel/arm/nnacl/int8/conv_depthwise_int8.c
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@ -169,9 +169,9 @@ void ConvDwInt8(int8_t *output_data, const int16_t *input_data, const int16_t *w
const int32_t *bias = bias_data + oc * C4NUM;
if (per_channel) {
out_multiplier = conv_param->conv_quant_arg_.quant_multiplier_ + oc;
left_shift = conv_param->conv_quant_arg_.left_shift_ + oc;
right_shift = conv_param->conv_quant_arg_.right_shift_ + oc;
out_multiplier = conv_param->conv_quant_arg_.quant_multiplier_ + oc * C4NUM;
left_shift = conv_param->conv_quant_arg_.left_shift_ + oc * C4NUM;
right_shift = conv_param->conv_quant_arg_.right_shift_ + oc * C4NUM;
}
DepthwiseBorderInt8(dst_data, src_data, weight, bias, 0, sliding->top_, 0, conv_param->output_w_, conv_param,

12
mindspore/lite/src/runtime/kernel/arm/nnacl/int8/conv_int8.c
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@ -28,6 +28,7 @@ void IndirectGemmInt8(int8_t *dst, int32_t *tmp_dst, const int8_t *src, const in
int32_t out_zp = conv_param->conv_quant_arg_.output_quant_args_[0].zp_;
int32_t act_min = conv_param->conv_quant_arg_.out_act_min_[0];
int32_t act_max = conv_param->conv_quant_arg_.out_act_max_[0];
int oc4 = UP_DIV(output_channel, C4NUM);
#ifdef __aarch64__
IndirectGemmInt8_4x4(dst, src, weight, bias, UP_DIV(kernel_plane, C4NUM), ic4, output_channel,
output_channel * sizeof(int8_t), input_sum, act_min, act_max, out_zp, out_multiplier,
@ -96,7 +97,7 @@ void IndirectGemmInt8(int8_t *dst, int32_t *tmp_dst, const int8_t *src, const in
dst[dst_tile_offset] = (int8_t)result;
} else if ((conv_param->conv_quant_arg_.asymmetric_ & FILTER_ASYMMETRIC) &&
(conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL)) {
tmp_dst[dst_tile_offset] -= input_sum[n * output_channel + oc];
tmp_dst[dst_tile_offset] -= input_sum[n * oc4 * C4NUM + oc];
int result = tmp_dst[dst_tile_offset] + bias[oc];
result = RoundingDivideByPOT(
SaturatingRoundingDoublingHighMul(result * (1 << (unsigned int)shift_before[oc]), out_multiplier[oc]),
@ -120,6 +121,7 @@ void IndirectGemmInt8Opt(int8_t *dst, int32_t *tmp_dst, const int8_t *src, const
int32_t out_zp = conv_param->conv_quant_arg_.output_quant_args_[0].zp_;
int32_t act_min = conv_param->conv_quant_arg_.out_act_min_[0];
int32_t act_max = conv_param->conv_quant_arg_.out_act_max_[0];
int oc4 = UP_DIV(output_channel, C4NUM);
if (gemm_func != NULL) {
#ifdef __aarch64__
gemm_func(dst, src, weight, bias, kernel_plane, ic4, output_channel, output_channel * sizeof(int8_t), input_sum,
@ -181,7 +183,7 @@ void IndirectGemmInt8Opt(int8_t *dst, int32_t *tmp_dst, const int8_t *src, const
dst[dst_tile_offset] = (int8_t)result;
} else if ((conv_param->conv_quant_arg_.asymmetric_ & FILTER_ASYMMETRIC) &&
(conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL)) {
tmp_dst[dst_tile_offset] -= input_sum[n * output_channel + oc];
tmp_dst[dst_tile_offset] -= input_sum[n * oc4 * C4NUM + oc];
int result = tmp_dst[dst_tile_offset] + bias[oc];
result = RoundingDivideByPOT(
SaturatingRoundingDoublingHighMul(result * (1 << (unsigned int)shift_before[oc]), out_multiplier[oc]),
@ -252,6 +254,7 @@ void ConvInt8(int8_t *input_data, int8_t *packed_input, int8_t *packed_weight, c
int out_h = conv_param->output_h_;
int out_w = conv_param->output_w_;
int out_channel = conv_param->output_channel_;
int oc4 = UP_DIV(out_channel, C4NUM);
int32_t input_zp = conv_param->conv_quant_arg_.input_quant_args_[0].zp_;
int tile_n = conv_param->tile_num_;
@ -264,7 +267,7 @@ void ConvInt8(int8_t *input_data, int8_t *packed_input, int8_t *packed_weight, c
int packed_input_size = output_tile_count * tile_n * unit_size;
int input_sum_offset;
if (conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL) {
input_sum_offset = tile_n * out_channel;
input_sum_offset = tile_n * oc4 * C4NUM;
} else {
input_sum_offset = tile_n;
}
@ -314,6 +317,7 @@ void ConvInt8Opt(int8_t *input_data, int8_t *packed_input, int8_t *packed_weight
int out_h = conv_param->output_h_;
int out_w = conv_param->output_w_;
int out_channel = conv_param->output_channel_;
int oc4 = UP_DIV(out_channel, C4NUM);
int32_t input_zp = conv_param->conv_quant_arg_.input_quant_args_[0].zp_;
int tile_n = conv_param->tile_num_;
int thread_count = conv_param->thread_num_;
@ -325,7 +329,7 @@ void ConvInt8Opt(int8_t *input_data, int8_t *packed_input, int8_t *packed_weight
int packed_input_size = output_tile_count * tile_n * unit_size;
int input_sum_offset;
if (conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL) {
input_sum_offset = tile_n * out_channel;
input_sum_offset = tile_n * oc4 * C4NUM;
} else {
input_sum_offset = tile_n;
}

6
mindspore/lite/src/runtime/kernel/arm/nnacl/pack.c
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@ -255,6 +255,7 @@ void Im2ColPackUnitInt8(const int8_t *input_data, int8_t *packed_input, int real
int in_h = conv_param->input_h_;
int in_w = conv_param->input_w_;
int ic4 = UP_DIV(in_channel, C4NUM);
int oc4 = UP_DIV(conv_param->output_channel_, C4NUM);
int out_w = conv_param->output_w_;
for (int i = 0; i < real_cal_num; i++) {
@ -297,7 +298,7 @@ void Im2ColPackUnitInt8(const int8_t *input_data, int8_t *packed_input, int real
continue;
} else if ((conv_param->conv_quant_arg_.asymmetric_ & FILTER_ASYMMETRIC) &&
(conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL)) {
int cal_num_offset = i * conv_param->output_channel_;
int cal_num_offset = i * oc4 * C4NUM;
for (int l = 0; l < conv_param->output_channel_; ++l) {
input_sum[cal_num_offset + l] = input_accumulator * filter_arg[l].zp_;
}
@ -325,6 +326,7 @@ void Im2ColPackUnitInt8Opt(const int8_t *input_data, int8_t *packed_input, int r
int in_h = conv_param->input_h_;
int in_w = conv_param->input_w_;
int ic4 = UP_DIV(in_channel, C4NUM);
int oc4 = UP_DIV(conv_param->output_channel_, C4NUM);
int out_w = conv_param->output_w_;
int block_size = kernel_h * kernel_w;
@ -368,7 +370,7 @@ void Im2ColPackUnitInt8Opt(const int8_t *input_data, int8_t *packed_input, int r
continue;
} else if ((conv_param->conv_quant_arg_.asymmetric_ & FILTER_ASYMMETRIC) &&
(conv_param->conv_quant_arg_.per_channel_ & FILTER_PER_CHANNEL)) {
int cal_num_offset = i * conv_param->output_channel_;
int cal_num_offset = i * oc4 * C4NUM;
for (int l = 0; l < conv_param->output_channel_; ++l) {
input_sum[cal_num_offset + l] = input_accumulator * filter_arg[l].zp_;
}

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