From 56050a14d530a14efc5a0daba307f9258ac2ed9c Mon Sep 17 00:00:00 2001 From: Wojciech Uss Date: Tue, 22 Sep 2020 10:13:55 +0200 Subject: [PATCH] Add support for (de/re)quantization with shift --- paddle/fluid/operators/dequantize_op.cc | 7 +- .../operators/mkldnn/dequantize_mkldnn_op.cc | 30 +- .../operators/mkldnn/quantize_mkldnn_op.cc | 43 ++- .../operators/mkldnn/requantize_mkldnn_op.cc | 72 ++++- paddle/fluid/operators/quantize_op.cc | 10 +- paddle/fluid/operators/requantize_op.cc | 10 +- .../mkldnn/test_dequantize_mkldnn_op.py | 135 +++++++- .../mkldnn/test_quantize_mkldnn_op.py | 187 ++++++++++- .../mkldnn/test_requantize_mkldnn_op.py | 303 +++++++++++++++--- tools/codestyle/clang_format.hook | 2 +- 10 files changed, 686 insertions(+), 113 deletions(-) diff --git a/paddle/fluid/operators/dequantize_op.cc b/paddle/fluid/operators/dequantize_op.cc index 0ed3293418f..8c2aeb1f8e6 100644 --- a/paddle/fluid/operators/dequantize_op.cc +++ b/paddle/fluid/operators/dequantize_op.cc @@ -31,9 +31,10 @@ framework::OpKernelType DeQuantOp::GetExpectedKernelType( } void DeQuantOpMaker::Make() { - AddInput("Input", "input data"); - AddOutput("Output", "output data"); - AddAttr("Scale", "scale data").SetDefault({1.0f}); + AddInput("Input", "Input data"); + AddOutput("Output", "Output data"); + AddAttr("Scale", "Scale data").SetDefault({1.0f}); + AddAttr("Shift", "Shift data").SetDefault({0.0f}); AddComment(R"DOC(This op will dequantize data from INT8 to FP32)DOC"); } diff --git a/paddle/fluid/operators/mkldnn/dequantize_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/dequantize_mkldnn_op.cc index 540642c7140..25660f42273 100644 --- a/paddle/fluid/operators/mkldnn/dequantize_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/dequantize_mkldnn_op.cc @@ -16,6 +16,7 @@ limitations under the License. */ #include "paddle/fluid/framework/data_layout_transform.h" #include "paddle/fluid/framework/tensor.h" #include "paddle/fluid/operators/dequantize_op.h" +#include "paddle/fluid/platform/errors.h" #include "paddle/fluid/platform/mkldnn_helper.h" #include "paddle/fluid/platform/mkldnn_reuse.h" @@ -37,14 +38,29 @@ class DeQuantOpKernel : public framework::OpKernel { void Compute(const framework::ExecutionContext& ctx) const override { auto* input = ctx.Input("Input"); auto scale_data = ctx.Attr("Scale"); + auto scale_shift = ctx.Attr("Shift"); + bool with_shift = scale_shift != 0.0f; auto* output = ctx.Output("Output"); + + PADDLE_ENFORCE_NE(scale_data, 0.0f, + platform::errors::InvalidArgument( + "Dequantization scale cannot be 0.0")); + PADDLE_ENFORCE_GE(scale_shift, 0, + platform::errors::Unimplemented( + "Dequantization shift must be nonnegative.")); + PADDLE_ENFORCE_LE( + scale_shift, 255, + platform::errors::Unimplemented( + "Dequantization shift must be less than or equal to 255.")); + auto& dev_ctx = ctx.template device_context(); const auto& engine = dev_ctx.GetEngine(); const T* input_data = input->data(); float* output_data = output->mutable_data(ctx.GetPlace()); - std::vector reorder_scale = {1.0f / scale_data}; + + float reorder_shift = -scale_shift / scale_data; auto src_tz = paddle::framework::vectorize(input->dims()); auto dst_tz = paddle::framework::vectorize(output->dims()); @@ -65,7 +81,15 @@ class DeQuantOpKernel : public framework::OpKernel { if (reorder_p == nullptr) { mkldnn::primitive_attr attri; int mask = 0; - attri.set_output_scales(mask, reorder_scale); + float reorder_scale = 1. / scale_data; + attri.set_output_scales(mask, {reorder_scale}); + + if (with_shift) { + mkldnn::post_ops post_operations; + post_operations.append_sum(); + attri.set_post_ops(post_operations); + std::fill(output_data, output_data + output->numel(), reorder_shift); + } auto src_md = platform::MKLDNNMemDesc({src_tz}, src_dt, src_fmt); src_memory = std::make_shared( @@ -92,6 +116,8 @@ class DeQuantOpKernel : public framework::OpKernel { dst_memory = std::static_pointer_cast( dev_ctx.GetBlob(key_dst_mem)); + if (with_shift) + std::fill(output_data, output_data + output->numel(), reorder_shift); dst_memory->set_data_handle(output->mutable_data(ctx.GetPlace())); } diff --git a/paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc index a6c8f8656a4..e5dedd403f3 100644 --- a/paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc @@ -36,7 +36,21 @@ class QuantOpKernel : public framework::OpKernel { void Compute(const framework::ExecutionContext& ctx) const override { auto* input = ctx.Input("Input"); auto scale_data = ctx.Attr("Scale"); + auto scale_shift = ctx.Attr("Shift"); + bool with_shift = scale_shift != 0.0f; auto* output = ctx.Output("Output"); + + PADDLE_ENFORCE_NE( + scale_data, 0.0f, + platform::errors::InvalidArgument("Quantization scale cannot be 0.0")); + PADDLE_ENFORCE_GE(scale_shift, 0, + platform::errors::Unimplemented( + "Quantization shift must be nonnegative.")); + PADDLE_ENFORCE_LE( + scale_shift, 255, + platform::errors::Unimplemented( + "Quantization shift must be less than or equal to 255.")); + auto& dev_ctx = ctx.template device_context(); const auto& engine = dev_ctx.GetEngine(); @@ -47,11 +61,12 @@ class QuantOpKernel : public framework::OpKernel { const T* input_data = input->data(); - bool is_negative = ctx.Attr("is_negative_input"); + bool is_negative_input = ctx.Attr("is_negative_input"); bool bfloat16 = ctx.Attr("bfloat16"); - std::string key = - platform::CreateKey(platform::ThreadIDasStr(), src_tz, scale_data, - is_negative, ctx.OutputName("Output")); + + std::string key = platform::CreateKey( + platform::ThreadIDasStr(), src_tz, scale_data, scale_shift, + is_negative_input, ctx.OutputName("Output")); const std::string key_prim = key + "@r"; const std::string key_src_mem = key + "@s"; const std::string key_dst_mem = key + "@d"; @@ -69,6 +84,15 @@ class QuantOpKernel : public framework::OpKernel { int mask = 0; attri.set_output_scales(mask, {scale_data}); + if (with_shift) { + mkldnn::post_ops post_operations; + post_operations.append_sum(); + attri.set_post_ops(post_operations); + uint8_t* output_data = output->mutable_data(ctx.GetPlace()); + // memset casts scale_shift to unsigned char (uint8_t) internally + std::memset(output_data, scale_shift, output->numel()); + } + auto src_md = platform::MKLDNNMemDesc({src_tz}, memory::data_type::f32, input->format()); src_memory = std::make_shared( @@ -78,7 +102,7 @@ class QuantOpKernel : public framework::OpKernel { if (bfloat16) { platform::SetDstMemoryQuantized( ctx, output, dst_tz, engine, dst_md, dst_memory, out_format); - } else if (is_negative) { + } else if (is_negative_input && !with_shift) { platform::SetDstMemoryQuantized(ctx, output, dst_tz, engine, dst_md, dst_memory, out_format); } else { @@ -104,10 +128,13 @@ class QuantOpKernel : public framework::OpKernel { if (bfloat16) { dst_memory->set_data_handle( output->mutable_data(place)); - } else if (is_negative) { - dst_memory->set_data_handle(output->mutable_data(place)); + } else if (with_shift || !is_negative_input) { + uint8_t* output_data = output->mutable_data(ctx.GetPlace()); + if (with_shift) std::memset(output_data, scale_shift, output->numel()); + dst_memory->set_data_handle(output_data); } else { - dst_memory->set_data_handle(output->mutable_data(place)); + dst_memory->set_data_handle( + output->mutable_data(ctx.GetPlace())); } } diff --git a/paddle/fluid/operators/mkldnn/requantize_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/requantize_mkldnn_op.cc index 5ad5ad94505..4666e5b74a5 100644 --- a/paddle/fluid/operators/mkldnn/requantize_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/requantize_mkldnn_op.cc @@ -26,20 +26,45 @@ using dnnl::reorder; using platform::to_void_cast; using Tensor = framework::Tensor; +namespace { + +inline uint8_t clip_to_uint8(float x) { + return std::max(0L, std::min(255L, std::lround(x))); +} + +} // namespace + template class ReQuantOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto* input = ctx.Input("Input"); auto scale_in = ctx.Attr("Scale_in"); + auto shift_in = ctx.Attr("Shift_in"); auto scale_out = ctx.Attr("Scale_out"); + auto shift_out = ctx.Attr("Shift_out"); + bool with_shift = shift_in != 0.0f || shift_out != 0.0f; auto* output = ctx.Output("Output"); + + PADDLE_ENFORCE_NE(scale_in, 0.0f, platform::errors::InvalidArgument( + "Scale of input cannot be 0.0")); + PADDLE_ENFORCE_NE(scale_out, 0.0f, platform::errors::InvalidArgument( + "Scale of output cannot be 0.0")); + if (shift_in != 0.0f) { + PADDLE_ENFORCE_EQ( + input->type(), framework::proto::VarType::UINT8, + platform::errors::Unimplemented("Requantize does not support nonzero " + "shift for signed input.")); + } + auto& dev_ctx = ctx.template device_context(); const auto& engine = dev_ctx.GetEngine(); auto src_tz = paddle::framework::vectorize(input->dims()); + float reorder_scale = scale_out / scale_in; + std::string key = platform::CreateKey(platform::ThreadIDasStr(), src_tz, scale_in, scale_out, ctx.OutputName("Output")); @@ -53,28 +78,37 @@ class ReQuantOpKernel : public framework::OpKernel { reorder_p = std::static_pointer_cast(dev_ctx.GetBlob(key_prim)); const T* input_data = input->data(); - T* output_data = output->mutable_data(ctx.GetPlace()); if (reorder_p == nullptr) { - dnnl::primitive_attr attri; - int mask = 0; - float scale_shift = scale_out / scale_in; - attri.set_output_scales(mask, {scale_shift}); - - auto dst_tz = paddle::framework::vectorize(output->dims()); - dnnl::memory::data_type src_dt = - paddle::framework::ToMKLDNNDataType(input->type()); - dnnl::memory::data_type dst_dt = src_dt; + auto dst_tz = framework::vectorize(output->dims()); + auto src_dt = framework::ToMKLDNNDataType(input->type()); + auto dst_dt = with_shift ? framework::MKLDNNDataType::u8 : src_dt; auto src_md = platform::MKLDNNMemDesc({src_tz}, src_dt, MKLDNNMemoryFormat::nhwc); src_memory = std::make_shared(src_md, engine, to_void_cast(input_data)); - auto dst_md = platform::MKLDNNMemDesc({dst_tz}, dst_dt, MKLDNNMemoryFormat::nhwc); - dst_memory = std::make_shared(dst_md, engine, - to_void_cast(output_data)); + + dnnl::primitive_attr attri; + int mask = 0; + attri.set_output_scales(mask, {reorder_scale}); + if (with_shift) { + mkldnn::post_ops post_operations; + post_operations.append_sum(); + attri.set_post_ops(post_operations); + uint8_t* output_data = output->mutable_data(ctx.GetPlace()); + uint8_t reorder_shift = + clip_to_uint8(shift_out - reorder_scale * shift_in); + std::memset(output_data, reorder_shift, output->numel()); + dst_memory = std::make_shared( + dst_md, engine, to_void_cast(output_data)); + } else { + T* output_data = output->mutable_data(ctx.GetPlace()); + dst_memory = std::make_shared( + dst_md, engine, to_void_cast(output_data)); + } auto reorder_pd = reorder::primitive_desc(*src_memory, *dst_memory, attri); @@ -90,7 +124,17 @@ class ReQuantOpKernel : public framework::OpKernel { dst_memory = std::static_pointer_cast(dev_ctx.GetBlob(key_dst_mem)); - dst_memory->set_data_handle(output_data); + if (with_shift) { + uint8_t* output_data = output->mutable_data(ctx.GetPlace()); + uint8_t reorder_shift = + clip_to_uint8(shift_out - reorder_scale * shift_in); + std::memset(output_data, reorder_shift, output->numel()); + dst_memory->set_data_handle(output_data); + + } else { + T* output_data = output->mutable_data(ctx.GetPlace()); + dst_memory->set_data_handle(output_data); + } } dnnl::stream astream(engine); diff --git a/paddle/fluid/operators/quantize_op.cc b/paddle/fluid/operators/quantize_op.cc index 602fdc6ff67..ee5829319d2 100644 --- a/paddle/fluid/operators/quantize_op.cc +++ b/paddle/fluid/operators/quantize_op.cc @@ -31,12 +31,16 @@ framework::OpKernelType QuantOp::GetExpectedKernelType( } void QuantOpMaker::Make() { - AddInput("Input", "input data"); - AddOutput("Output", "output data"); + AddInput("Input", "Input data"); + AddOutput("Output", "Output data"); AddAttr("is_negative_input", "(bool, default false) Only used in mkldnn INT8 kernel") .SetDefault(false); - AddAttr("Scale", "scale data").SetDefault({1.0f}); + AddAttr("Scale", "Scale data").SetDefault({1.0f}); + AddAttr( + "Shift", + "Shift data. When Shift is non-zero, data is quantized to unsigned int8.") + .SetDefault({0.0f}); AddAttr("output_format", "Convert format to NHWC or NCHW during quantization.") .SetDefault("NHWC"); diff --git a/paddle/fluid/operators/requantize_op.cc b/paddle/fluid/operators/requantize_op.cc index c17b6ef8842..ea3058c5ae4 100644 --- a/paddle/fluid/operators/requantize_op.cc +++ b/paddle/fluid/operators/requantize_op.cc @@ -31,10 +31,12 @@ framework::OpKernelType ReQuantOp::GetExpectedKernelType( } void ReQuantOpMaker::Make() { - AddInput("Input", "input data"); - AddOutput("Output", "output data"); - AddAttr("Scale_in", "scale in data").SetDefault({1.0f}); - AddAttr("Scale_out", "scale out data").SetDefault({1.0f}); + AddInput("Input", "Input data"); + AddOutput("Output", "Output data"); + AddAttr("Scale_in", "Scale in data").SetDefault({1.0f}); + AddAttr("Scale_out", "Scale out data").SetDefault({1.0f}); + AddAttr("Shift_in", "Shift in data").SetDefault({1.0f}); + AddAttr("Shift_out", "Shift out data").SetDefault({1.0f}); AddComment( R"DOC(This op will re-quantize data from INT8 with scale_in to INT8 with scale_out)DOC"); } diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_dequantize_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_dequantize_mkldnn_op.py index 35419462909..838db7451d0 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_dequantize_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_dequantize_mkldnn_op.py @@ -22,37 +22,60 @@ from paddle.fluid.tests.unittests.op_test import OpTest class TestDeQuantizeOp(OpTest): def setUp(self): self.op_type = 'dequantize' - self.scale = 2.0 - self.input_size = [1, 1, 5, 5] #Naive nChw16c + self.scale = 127.0 + self.shift = 0.0 + self.input_size = [1, 1, 5, 5] # Naive nChw16c self.data_type = 'int8' self.set_scale() + self.set_shift() self.set_data_type() + self.set_input_size() + self.prepare_input() + self.prepare_output() + def prepare_input(self): if self.data_type == 'int8': - input = (np.random.randint(0, 100, self.input_size) - 50 - ).astype(self.data_type) - output = (input * (1 / self.scale)).astype('float') + # input data values are integers from interval [-128, 128) + self.input = (np.random.randint(0, 256, self.input_size) - 128 + ).astype(self.data_type) else: - input = (np.random.randint(0, 100, - self.input_size)).astype(self.data_type) - output = (input * (1 / self.scale)).astype('float') + # input data values are integers from interval [0, 256) + self.input = (np.random.randint( + 0, 256, self.input_size)).astype(self.data_type) - self.inputs = {'Input': OpTest.np_dtype_to_fluid_dtype(input)} + self.inputs = {'Input': OpTest.np_dtype_to_fluid_dtype(self.input)} + self.attrs = {'Scale': self.scale, 'Shift': self.shift} + def prepare_output(self): + output = (self.input / self.scale - + (self.shift / self.scale)).astype('float') self.outputs = {'Output': output} - self.attrs = {'Scale': self.scale, } - def test_check_output(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode self.check_output(check_dygraph=False) + def check_raise_error(self, msg): + try: + self.check_output() + except Exception as e: + if msg in str(e): + raise AttributeError + else: + print(e) + def set_scale(self): pass + def set_shift(self): + pass + def set_data_type(OpTest): pass + def set_input_size(self): + pass + class TestDeQuantizeOp1(TestDeQuantizeOp): def set_scale(self): @@ -70,5 +93,95 @@ class TestDeQuantizeOp2(TestDeQuantizeOp): self.data_type = 'uint8' +class TestDeQuantizeOp_ZeroScale(TestDeQuantizeOp): + def set_scale(self): + self.scale = 0.0 + + def prepare_output(self): + self.output = np.zeros(self.input_size) + self.outputs = {'Output': self.output} + + def test_check_output(self): + self.assertRaises(AttributeError, self.check_raise_error, + 'Dequantization scale cannot be 0.0') + + +# 2-dim input +# P - positive input, with shift +class TestDeQuantizeOpShift_2_P(TestDeQuantizeOp): + def set_data_type(self): + self.data_type = 'uint8' + + def set_scale(self): + self.scale = 255.0 + + def set_shift(self): + self.shift = 128.0 + + def set_input_size(self): + self.input_size = [2, 3] + + +# 2-dim input +# N - negative input, with shift +class TestDeQuantizeOpShift_2_N(TestDeQuantizeOpShift_2_P): + def set_data_type(self): + self.data_type = 'int8' + + def set_scale(self): + self.scale = 127.0 + + def set_shift(self): + self.shift = 10.0 + + def set_input_size(self): + self.input_size = [2, 3] + + +# 3-dim input +class TestDeQuantizeOpShift_3_P(TestDeQuantizeOpShift_2_P): + def set_input_size(self): + self.input_size = [2, 3, 4] + + +class TestDeQuantizeOpShift_3_N(TestDeQuantizeOpShift_2_N): + def set_input_size(self): + self.input_size = [2, 3, 4] + + +# 4-dim input +class TestDeQuantizeOpShift_4_P(TestDeQuantizeOpShift_2_P): + def set_input_size(self): + self.input_size = [2, 3, 4, 5] + + +class TestDeQuantizeOpShift_4_N(TestDeQuantizeOpShift_2_N): + def set_input_size(self): + self.input_size = [2, 3, 4, 5] + + +class TestDeQuantizeOp_NegativeShift(TestDeQuantizeOp): + def set_shift(self): + self.shift = -10.0 + + def prepare_output(self): + self.output = np.zeros(self.input_size) + self.outputs = {'Output': self.output} + + def test_check_output(self): + self.assertRaises(AttributeError, self.check_raise_error, + 'Dequantization shift must be nonnegative.') + + +class TestDeQuantizeOp_TooBigShift(TestDeQuantizeOp_NegativeShift): + def set_shift(self): + self.shift = 300.0 + + def test_check_output(self): + self.assertRaises( + AttributeError, self.check_raise_error, + 'Dequantization shift must be less than or equal to 255.') + + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_quantize_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_quantize_mkldnn_op.py index 9f08fea778a..a7acc5f3f9b 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_quantize_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_quantize_mkldnn_op.py @@ -22,44 +22,75 @@ from paddle.fluid.tests.unittests.op_test import OpTest class TestQuantizeOp(OpTest): def setUp(self): self.op_type = 'quantize' - self.scale = 2.0 - self.input_size = [1, 1, 5, 5] #Naive nChw16c + self.scale = 255.0 + self.shift = 0.0 + self.input_size = [1, 1, 5, 5] # Naive nChw16c self.is_negative = False + self.output_format = 'NCHW' self.set_scale() + self.set_shift() self.set_is_negative() + self.set_input_size() + self.set_output_format() + self.prepare_input() + self.prepare_output() + def prepare_input(self): if self.is_negative: - input = (100 * np.random.random_sample(self.input_size) - 50 - ).astype('float32') - output = np.round(input * self.scale).astype('int8') + # input data values are from interval [-1.0, 1.0) + self.input = (2 * np.random.random_sample(self.input_size) - 1 + ).astype('float32') else: - input = (100 * - np.random.random_sample(self.input_size)).astype('float32') - output = np.round(input * self.scale).astype('uint8') - - self.inputs = {'Input': OpTest.np_dtype_to_fluid_dtype(input)} - - self.outputs = {'Output': output} + # input data values are from interval [0.0, 1.0) + self.input = ( + np.random.random_sample(self.input_size)).astype('float32') + self.inputs = {'Input': OpTest.np_dtype_to_fluid_dtype(self.input)} self.attrs = { 'Scale': self.scale, - 'is_negative_input': self.is_negative + 'Shift': self.shift, + 'is_negative_input': self.is_negative, + 'output_format': self.output_format } + def prepare_output(self): + input_data_type = 'int8' if self.is_negative else 'uint8' + output = np.rint(self.input * self.scale + self.shift).astype( + input_data_type) + self.outputs = {'Output': output} + def test_check_output(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode self.check_output(check_dygraph=False) + def check_raise_error(self, msg): + try: + self.check_output() + except Exception as e: + if msg in str(e): + raise AttributeError + else: + print(e) + def set_scale(self): pass + def set_shift(self): + pass + def set_is_negative(self): pass + def set_input_size(self): + pass + + def set_output_format(self): + pass + class TestQuantizeOp1(TestQuantizeOp): def set_scale(self): - self.scale = 1.5 + self.scale = 127.0 def set_is_negative(self): self.is_nagative = True @@ -67,11 +98,137 @@ class TestQuantizeOp1(TestQuantizeOp): class TestQuantizeOp2(TestQuantizeOp): def set_scale(self): - self.scale = 0.1 + self.scale = 255.0 + + def set_is_negative(self): + self.is_nagative = False + + +class TestQuantizeOp_ZeroScale(TestQuantizeOp): + def set_scale(self): + self.scale = 0.0 + + def prepare_output(self): + self.output = np.zeros(self.input_size) + self.outputs = {'Output': self.output} + + def test_check_output(self): + self.assertRaises(AttributeError, self.check_raise_error, + 'Quantization scale cannot be 0.0') + + +# 2-dim input +# P - positive input +class TestQuantizeOpShift_NCHW_2_P(TestQuantizeOp): + def set_output_format(self): + self.output_format = 'NCHW' + + def set_is_negative(self): + self.is_nagative = False + + def set_scale(self): + self.scale = 255.0 + + def set_shift(self): + self.shift = 0.0 + + def set_input_size(self): + self.input_size = [2, 3] + + +# 2-dim input +# N - negative input +class TestQuantizeOpShift_NCHW_2_N(TestQuantizeOpShift_NCHW_2_P): + def set_is_negative(self): + self.is_nagative = True + + def set_scale(self): + self.scale = 127.0 + + def set_shift(self): + self.shift = 128.0 + + +class TestQuantizeOpShift_NHWC_2_P(TestQuantizeOpShift_NCHW_2_P): + def set_output_format(self): + self.output_format = 'NHWC' + + +class TestQuantizeOpShift_NHWC_2_N(TestQuantizeOpShift_NCHW_2_N): + def set_output_format(self): + self.output_format = 'NHWC' + + +# 3-dim input +class TestQuantizeOpShift_NCHW_3_P(TestQuantizeOpShift_NCHW_2_P): + def set_input_size(self): + self.input_size = [2, 3, 4] + + +class TestQuantizeOpShift_NCHW_3_N(TestQuantizeOpShift_NCHW_2_N): + def set_input_size(self): + self.input_size = [2, 3, 4] + + +class TestQuantizeOpShift_NHWC_3_P(TestQuantizeOpShift_NCHW_3_P): + def set_output_format(self): + self.output_format = 'NHWC' + + +class TestQuantizeOpShift_NHWC_3_N(TestQuantizeOpShift_NCHW_3_N): + def set_output_format(self): + self.output_format = 'NHWC' + + +# 4-dim input +class TestQuantizeOpShift_NCHW_4_P(TestQuantizeOpShift_NCHW_2_P): + def set_input_size(self): + self.input_size = [2, 3, 4, 5] + +class TestQuantizeOpShift_NCHW_4_N(TestQuantizeOpShift_NCHW_2_N): + def set_input_size(self): + self.input_size = [2, 3, 4, 5] + + +class TestQuantizeOpShift_NHWC_4_P(TestQuantizeOpShift_NCHW_4_P): + def set_output_format(self): + self.output_format = 'NHWC' + + +class TestQuantizeOpShift_NHWC_4_N(TestQuantizeOpShift_NCHW_4_N): + def set_output_format(self): + self.output_format = 'NHWC' + + +class TestQuantizeOp_NegativeShift(TestQuantizeOp): def set_is_negative(self): self.is_nagative = False + def set_scale(self): + self.scale = 100.0 + + def set_shift(self): + self.shift = -10.0 + + def prepare_output(self): + self.output = np.zeros(self.input_size) + self.outputs = {'Output': self.output} + + def test_check_output(self): + self.assertRaises(AttributeError, self.check_raise_error, + 'Quantization shift must be nonnegative.') + + +class TestQuantizeOp_TooBigShift(TestQuantizeOp_NegativeShift): + def set_shift(self): + self.shift = 300.0 + + def test_check_output(self): + self.assertRaises( + AttributeError, self.check_raise_error, + 'Quantization shift must be less than or equal to 255.') + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_requantize_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_requantize_mkldnn_op.py index 750e7f37df4..7babec667b8 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_requantize_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_requantize_mkldnn_op.py @@ -25,88 +25,271 @@ from mkldnn_op_test import format_reorder class TestReQuantizeOp(OpTest): def setUp(self): self.op_type = 'requantize' - self.scale_in = 2.0 - self.scale_out = 1.5 + self.scale_in = 127.0 + self.shift_in = 0.0 + self.scale_out = 100.0 + self.shift_out = 0.0 self.input_size = [1, 1, 10, 10] - self.data_type = 'int8' - self.set_scale() - self.set_data_type() - self.prepare_inputs() - - def prepare_inputs(self): - scale_shift = self.scale_out / self.scale_in - - if self.data_type == 'int8': - self.input = (np.random.randint(0, 100, self.input_size) - 50 - ).astype(self.data_type) - output_tmp = np.round(self.input.astype('float32') * - scale_shift).astype('int8') + self.input_data_type = 'int8' + self.set_scales() + self.set_shifts() + self.set_input_data_type() + self.prepare_input() + self.prepare_output() + + def prepare_input(self): + if self.input_data_type == 'int8': + # input data values are integers from interval [-128, 128) + self.input = (np.random.randint(0, 256, self.input_size) - 128 + ).astype(self.input_data_type) else: + # input data values are integers from interval [0, 256) self.input = (np.random.randint( - 0, 100, self.input_size)).astype(self.data_type) - output_tmp = np.round(self.input.astype('float32') * - scale_shift).astype('uint8') - - self.output = format_reorder(output_tmp, self.input_size) + 0, 256, self.input_size)).astype(self.input_data_type) self.inputs = {'Input': OpTest.np_dtype_to_fluid_dtype(self.input)} + self.attrs = { + 'Scale_in': self.scale_in, + 'Scale_out': self.scale_out, + 'Shift_in': self.shift_in, + 'Shift_out': self.shift_out + } - self.outputs = {'Output': self.output} + def prepare_output(self): + scale_ratio = self.scale_out / self.scale_in + with_shift = (self.shift_in != 0.0 or self.shift_out != 0.0) + + if with_shift or self.input_data_type == 'uint8': + dst_type = 'uint8' + type_min = 0 + type_max = 255 + new_shift = np.clip( + np.rint(self.shift_out - scale_ratio * self.shift_in), type_min, + type_max) + else: + dst_type = 'int8' + type_min = -128 + type_max = 127 + new_shift = 0 - self.attrs = {'Scale_in': self.scale_in, 'Scale_out': self.scale_out} + output_tmp = np.clip( + np.rint(self.input.astype('float32') * scale_ratio + new_shift), + type_min, type_max).astype(dst_type) + + self.output = format_reorder(output_tmp, self.input_size) + self.outputs = {'Output': self.output} def test_check_output(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode + self.assertTrue(self.input_data_type == 'uint8' or self.shift_in == 0.0, + 'Input data must be unsigned if it has nonzero shift.') self.check_output(check_dygraph=False) - def set_scale(self): + def check_raise_error(self, msg): + try: + self.check_output() + except Exception as e: + if msg in str(e): + raise AttributeError + else: + print(e) + + def set_scales(self): pass - def set_data_type(OpTest): + def set_shifts(self): pass + def set_input_data_type(OpTest): + pass + + +# ---------------test requantize with s8 input, no shift-------------------- + -#--------------------test requantize with s8 input-------------------- +class TestReQuantizeOp_S8_SameScales(TestReQuantizeOp): + def set_scales(self): + self.scale_in = 127.0 + self.scale_out = 127.0 -class TestReQuantizeOp1(TestReQuantizeOp): - def set_scale(self): - self.scale_in = 1.5 - self.scale_out = 1.5 +class TestReQuantizeOp_S8_DifferentScales_1(TestReQuantizeOp): + def set_scales(self): + self.scale_in = 127.0 + self.scale_out = 100.0 -class TestReQuantizeOp2(TestReQuantizeOp): - def set_scale(self): - self.scale_in = 0.1 - self.scale_out = 0.2 +class TestReQuantizeOp_S8_DifferentScales_2(TestReQuantizeOp): + def set_scales(self): + self.scale_in = 100.0 + self.scale_out = 127.0 -#--------------------test requantize with u8 input-------------------- +class TestReQuantizeOp_S8_ZeroInputScale(TestReQuantizeOp): + def set_scales(self): + self.scale_in = 0.0 + self.scale_out = 127.0 + def prepare_output(self): + self.output = np.zeros(self.input_size) + self.outputs = {'Output': self.output} + + def test_check_output(self): + self.assertRaises(AttributeError, self.check_raise_error, + 'Scale of input cannot be 0.0') -class TestReQuantizeOp3(TestReQuantizeOp1): - def set_data_type(self): - self.data_type = 'uint8' +class TestReQuantizeOp_S8_ZeroOutputScale(TestReQuantizeOp): + def set_scales(self): + self.scale_in = 127.0 + self.scale_out = 0.0 -class TestReQuantizeOp4(TestReQuantizeOp2): - def set_data_type(self): - self.data_type = 'uint8' + def prepare_output(self): + self.output = np.zeros(self.input_size) + self.outputs = {'Output': self.output} + def test_check_output(self): + self.assertRaises(AttributeError, self.check_raise_error, + 'Scale of output cannot be 0.0') + + +# ---------------test requantize with u8 input, no shift-------------------- + + +class TestReQuantizeOp_U8_SameScales(TestReQuantizeOp_S8_SameScales): + def set_input_data_type(self): + self.input_data_type = 'uint8' + + +class TestReQuantizeOp_U8_DifferentScales_1( + TestReQuantizeOp_S8_DifferentScales_1): + def set_input_data_type(self): + self.input_data_type = 'uint8' + + +class TestReQuantizeOp_U8_DifferentScales_2( + TestReQuantizeOp_S8_DifferentScales_2): + def set_input_data_type(self): + self.input_data_type = 'uint8' + + +# ---------------test requantize with s8 input, with shift------------------ + + +class TestReQuantizeOp_S8_WithShift(TestReQuantizeOp): + def set_scales(self): + self.scale_in = 60.0 + self.scale_out = 127.0 + + def set_shifts(self): + self.shift_in = 128.0 + self.shift_out = 128.0 + + def test_check_output(self): + self.assertRaises( + AttributeError, self.check_raise_error, + 'Requantize does not support nonzero shift for signed input.') -#-------------------test reused requantize op--------------------------- + +class TestReQuantizeOp_S8_WithOutputShift(TestReQuantizeOp): + def set_scales(self): + self.scale_in = 127.0 + self.scale_out = 60.0 + + def set_shifts(self): + self.shift_in = 0.0 + self.shift_out = 120.0 + + +# ---------------test requantize with u8 input, with shift------------------ + + +class TestReQuantizeOp_U8_SameScales_SameShift(TestReQuantizeOp_U8_SameScales): + def set_shifts(self): + self.shift_in = 128.0 + self.shift_out = 128.0 + + +class TestReQuantizeOp_U8_SameScales_DifferentShift_1( + TestReQuantizeOp_U8_SameScales): + def set_shifts(self): + self.shift_in = 60.0 + self.shift_out = 128.0 + + +class TestReQuantizeOp_U8_SameScales_DifferentShift_2( + TestReQuantizeOp_U8_SameScales): + def set_shifts(self): + self.shift_in = 128.0 + self.shift_out = 60.0 + + +class TestReQuantizeOp_U8_DifferentScales_1_SameShift( + TestReQuantizeOp_U8_DifferentScales_1): + def set_shifts(self): + self.shift_in = 128.0 + self.shift_out = 128.0 + + +class TestReQuantizeOp_U8_DifferentScales_2_SameShift( + TestReQuantizeOp_U8_DifferentScales_2): + def set_shifts(self): + self.shift_in = 128.0 + self.shift_out = 128.0 + + +class TestReQuantizeOp_U8_DifferentScales_1_DifferentShift_1( + TestReQuantizeOp_U8_DifferentScales_1): + def set_shifts(self): + self.shift_in = 128.0 + self.shift_out = 60.0 + + +class TestReQuantizeOp_U8_DifferentScales_2_DifferentShift_1( + TestReQuantizeOp_U8_DifferentScales_2): + def set_shifts(self): + self.shift_in = 128.0 + self.shift_out = 60.0 + + +class TestReQuantizeOp_U8_DifferentScales_1_DifferentShift_2( + TestReQuantizeOp_U8_DifferentScales_1): + def set_shifts(self): + self.shift_in = 60.0 + self.shift_out = 128.0 + + +class TestReQuantizeOp_U8_DifferentScales_2_DifferentShift_2( + TestReQuantizeOp_U8_DifferentScales_2): + def set_shifts(self): + self.shift_in = 60.0 + self.shift_out = 128.0 + + +# ---------------test reused requantize op, no shift------------------------ class TestReQuantizeOpReused(TestReQuantizeOp): def setUp(self): - self.input_size = [1, 1, 10, 10] - self.data_type = 'int8' - self.set_scale() - self.prepare_inputs() - - def set_scale(self): - self.scale_in = 0.1 - self.scale_out = 0.2 + # self.input_size = [1, 1, 10, 10] + self.input_size = [1, 1, 2, 2] + self.input_data_type = 'int8' + self.set_scales() + self.set_shifts() + self.set_input_data_type() + self.prepare_input() + self.prepare_output() + + def set_scales(self): + self.scale_in = 100.0 + self.scale_out = 120.0 + + def set_shifts(self): + self.shift_in = 0.0 + self.shift_out = 0.0 + + def set_input_data_type(self): + pass def test_check_output(self): variables = { @@ -119,12 +302,16 @@ class TestReQuantizeOpReused(TestReQuantizeOp): for name in variables: block.create_var( name=name, dtype="int8", shape=variables[name].shape) - requant_op = block.append_op( + block.append_op( type="requantize", inputs={'Input': block.var('input'), }, outputs={"Output": block.var('output')}, - attrs={'Scale_in': self.scale_in, - 'Scale_out': self.scale_out}) + attrs={ + 'Scale_in': self.scale_in, + 'Scale_out': self.scale_out, + 'Shift_in': self.shift_in, + 'Shift_out': self.shift_out + }) place = core.CPUPlace() exe = fluid.Executor(place) for i in range(2): @@ -137,5 +324,17 @@ class TestReQuantizeOpReused(TestReQuantizeOp): variables['output'], out[0], atol=1e-4), 'output') +# ---------------test reused requantize op, no shift------------------------ + + +class TestReQuantizeOpReused_WithShift(TestReQuantizeOpReused): + def set_input_data_type(self): + self.input_data_type = 'uint8' + + def set_shifts(self): + self.shift_in = 128 + self.shift_out = 60 + + if __name__ == '__main__': unittest.main() diff --git a/tools/codestyle/clang_format.hook b/tools/codestyle/clang_format.hook index 1d928216867..d646e52c436 100755 --- a/tools/codestyle/clang_format.hook +++ b/tools/codestyle/clang_format.hook @@ -1,7 +1,7 @@ #!/bin/bash set -e -readonly VERSION="3.8" +readonly VERSION="3.9" version=$(clang-format -version) -- GitLab