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Add microbenchmark for layer normalization and improve latency (microsoft#22223)
- Added a microbenchmark for the `LayerNormalization` MLFloat16 support added in microsoft#22063. - Updated the `LayerNormalization` MLFloat16 implementation to improve the latency. ``` ---------------------------------------------------------------------------------------------- Original MLFloat16 support Time CPU Iterations ---------------------------------------------------------------------------------------------- BM_LayerNormalization<MLFloat16, float>/1/real_time 15599 us 15625 us 47 BM_LayerNormalization<MLFloat16, float>/1/real_time 14714 us 14824 us 39 BM_LayerNormalization<MLFloat16, float>/1/real_time 14634 us 14688 us 50 ---------------------------------------------------------------------------------------------- Updated MLFloat16 support Time CPU Iterations ---------------------------------------------------------------------------------------------- BM_LayerNormalization<MLFloat16, float>/1/real_time 7276 us 7254 us 84 BM_LayerNormalization<MLFloat16, float>/1/real_time 6820 us 6720 us 93 BM_LayerNormalization<MLFloat16, float>/1/real_time 6840 us 6882 us 84 ```
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-229
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6 files changed

+604
-229
lines changed

‎cmake/onnxruntime_unittests.cmake‎

Lines changed: 2 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1164,7 +1164,8 @@ if (NOT onnxruntime_ENABLE_TRAINING_TORCH_INTEROP)
11641164
${BENCHMARK_DIR}/gelu.cc
11651165
${BENCHMARK_DIR}/activation.cc
11661166
${BENCHMARK_DIR}/quantize.cc
1167-
${BENCHMARK_DIR}/reduceminmax.cc)
1167+
${BENCHMARK_DIR}/reduceminmax.cc
1168+
${BENCHMARK_DIR}/layer_normalization.cc)
11681169
target_include_directories(onnxruntime_benchmark PRIVATE ${ONNXRUNTIME_ROOT} ${onnxruntime_graph_header} ${ONNXRUNTIME_ROOT}/core/mlas/inc)
11691170
target_compile_definitions(onnxruntime_benchmark PRIVATE BENCHMARK_STATIC_DEFINE)
11701171
if(WIN32)

‎onnxruntime/contrib_ops/cpu/skip_layer_norm.cc‎

Lines changed: 202 additions & 97 deletions
Original file line numberDiff line numberDiff line change
@@ -2,6 +2,7 @@
22
// Licensed under the MIT License.
33

44
#include "core/framework/tensor.h"
5+
#include "core/mlas/inc/mlas.h"
56
#include "core/util/math_cpuonly.h"
67
#include "core/providers/common.h"
78
#include "core/platform/threadpool.h"
@@ -36,52 +37,188 @@ REGISTER_KERNEL_TYPED(float)
3637
REGISTER_KERNEL_TYPED(double)
3738
REGISTER_KERNEL_TYPED(MLFloat16)
3839

39-
// Utility to convert from MLFloat16 to float only when the input type is MLFloat16.
40-
template <typename T, typename Ret>
41-
ORT_FORCEINLINE Ret ConvertMLFloat16ToDoubleOrFloatIfNeeded(T val);
42-
43-
template <>
44-
ORT_FORCEINLINE float ConvertMLFloat16ToDoubleOrFloatIfNeeded<MLFloat16, float>(MLFloat16 val) {
45-
return val.ToFloat();
46-
}
47-
48-
template <>
49-
ORT_FORCEINLINE double ConvertMLFloat16ToDoubleOrFloatIfNeeded<MLFloat16, double>(MLFloat16 val) {
50-
return static_cast<double>(ConvertMLFloat16ToDoubleOrFloatIfNeeded<MLFloat16, float>(val));
40+
namespace {
41+
42+
template <typename T, typename = std::enable_if_t<std::is_same_v<T, float> || std::is_same_v<T, double>, void>>
43+
void ComputeJob(
44+
const T* input_data,
45+
const T* skip_data,
46+
const T* gamma_data,
47+
const T* beta_data,
48+
const T* bias_data,
49+
IAllocatorUniquePtr<float>& skip_float_uptr,
50+
IAllocatorUniquePtr<float>& gamma_float_uptr,
51+
IAllocatorUniquePtr<float>& beta_float_uptr,
52+
IAllocatorUniquePtr<float>& bias_float_uptr,
53+
ptrdiff_t task_idx,
54+
int hidden_size,
55+
int64_t skip_size,
56+
float epsilon,
57+
bool simplified,
58+
T* output_data,
59+
T* skip_input_bias_add_output_data,
60+
AllocatorPtr alloc) {
61+
ORT_UNUSED_PARAMETER(skip_float_uptr); // only used in MLFloat16 overload
62+
ORT_UNUSED_PARAMETER(gamma_float_uptr); // only used in MLFloat16 overload
63+
ORT_UNUSED_PARAMETER(beta_float_uptr); // only used in MLFloat16 overload
64+
ORT_UNUSED_PARAMETER(bias_float_uptr); // only used in MLFloat16 overload
65+
ORT_UNUSED_PARAMETER(alloc);
66+
67+
auto offset = task_idx * hidden_size;
68+
const T* p_input = input_data + offset;
69+
const T* p_skip = skip_data + (offset % skip_size);
70+
T* p_output = output_data + offset;
71+
T* p_skip_input_bias_add_output = skip_input_bias_add_output_data == nullptr ? nullptr : skip_input_bias_add_output_data + offset;
72+
73+
T mean(0.0f);
74+
T mean_square(0.0f);
75+
76+
for (decltype(hidden_size) h = 0; h < hidden_size; h++) {
77+
T val = p_input[h] + p_skip[h];
78+
79+
if (nullptr != bias_data) {
80+
val += bias_data[h];
81+
}
82+
83+
if (nullptr != p_skip_input_bias_add_output) {
84+
p_skip_input_bias_add_output[h] = val;
85+
}
86+
87+
p_output[h] = val;
88+
mean += val;
89+
mean_square += val * val;
90+
}
91+
92+
mean = mean / hidden_size;
93+
if (simplified) {
94+
mean_square = sqrt(mean_square / hidden_size + epsilon);
95+
} else {
96+
mean_square = sqrt(mean_square / hidden_size - mean * mean + epsilon);
97+
}
98+
99+
for (decltype(hidden_size) h = 0; h < hidden_size; h++) {
100+
if (simplified) {
101+
p_output[h] = p_output[h] / mean_square * gamma_data[h];
102+
} else if (nullptr == beta_data) {
103+
p_output[h] = (p_output[h] - mean) / mean_square * gamma_data[h];
104+
} else {
105+
p_output[h] = (p_output[h] - mean) / mean_square * gamma_data[h] + beta_data[h];
106+
}
107+
}
51108
}
52109

53-
template <>
54-
ORT_FORCEINLINE constexpr float ConvertMLFloat16ToDoubleOrFloatIfNeeded<float, float>(float val) {
55-
return val;
110+
void ComputeJob(
111+
const MLFloat16* input_data,
112+
const MLFloat16* skip_data,
113+
const MLFloat16* gamma_data,
114+
const MLFloat16* beta_data,
115+
const MLFloat16* bias_data,
116+
IAllocatorUniquePtr<float>& skip_float_uptr,
117+
IAllocatorUniquePtr<float>& gamma_float_uptr,
118+
IAllocatorUniquePtr<float>& beta_float_uptr,
119+
IAllocatorUniquePtr<float>& bias_float_uptr,
120+
ptrdiff_t task_idx,
121+
int hidden_size,
122+
int64_t skip_size,
123+
float epsilon,
124+
bool simplified,
125+
MLFloat16* output_data,
126+
MLFloat16* skip_input_bias_add_output_data,
127+
AllocatorPtr alloc) {
128+
auto offset = task_idx * hidden_size;
129+
const MLFloat16* p_input = input_data + offset;
130+
const MLFloat16* p_skip = skip_data + (offset % skip_size);
131+
MLFloat16* p_output = output_data + offset;
132+
MLFloat16* p_skip_input_bias_add_output = skip_input_bias_add_output_data == nullptr ? nullptr : skip_input_bias_add_output_data + offset;
133+
134+
float mean(0.0f);
135+
float mean_square(0.0f);
136+
const size_t num_elems = static_cast<size_t>(hidden_size);
137+
138+
IAllocatorUniquePtr<float> input_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
139+
MlasConvertHalfToFloatBuffer(p_input, input_float_uptr.get(), num_elems);
140+
141+
if (!skip_float_uptr) {
142+
skip_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
143+
MlasConvertHalfToFloatBuffer(p_skip, skip_float_uptr.get(), num_elems);
144+
}
145+
146+
if (bias_data && !bias_float_uptr) {
147+
bias_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
148+
MlasConvertHalfToFloatBuffer(bias_data, bias_float_uptr.get(), num_elems);
149+
}
150+
151+
IAllocatorUniquePtr<float> output_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
152+
float* output_float_ptr = output_float_uptr.get();
153+
154+
const float* input_float_ptr = input_float_uptr.get();
155+
const float* skip_float_ptr = skip_float_uptr.get();
156+
const float* bias_float_ptr = bias_float_uptr.get();
157+
for (size_t h = 0; h < num_elems; h++) {
158+
float val = input_float_ptr[h] + skip_float_ptr[h];
159+
160+
if (bias_float_uptr) {
161+
val += bias_float_ptr[h];
162+
}
163+
164+
output_float_ptr[h] = val;
165+
mean += val;
166+
mean_square += val * val;
167+
}
168+
169+
if (nullptr != p_skip_input_bias_add_output) {
170+
MlasConvertFloatToHalfBuffer(output_float_ptr, p_skip_input_bias_add_output, num_elems);
171+
}
172+
173+
mean = mean / hidden_size;
174+
if (simplified) {
175+
mean_square = sqrt(mean_square / hidden_size + epsilon);
176+
} else {
177+
mean_square = sqrt(mean_square / hidden_size - mean * mean + epsilon);
178+
}
179+
180+
if (!gamma_float_uptr) {
181+
gamma_float_uptr = std::move(input_float_uptr); // overwrite input with gamma values, since they have the same size
182+
MlasConvertHalfToFloatBuffer(gamma_data, gamma_float_uptr.get(), num_elems);
183+
}
184+
185+
if (beta_data && !beta_float_uptr) {
186+
beta_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
187+
MlasConvertHalfToFloatBuffer(beta_data, beta_float_uptr.get(), num_elems);
188+
}
189+
190+
const float* gamma_float_ptr = gamma_float_uptr.get();
191+
const float* beta_float_ptr = beta_float_uptr.get();
192+
for (size_t h = 0; h < num_elems; h++) {
193+
if (simplified) {
194+
output_float_ptr[h] = output_float_ptr[h] / mean_square * gamma_float_ptr[h];
195+
} else if (nullptr == beta_float_uptr) {
196+
output_float_ptr[h] = (output_float_ptr[h] - mean) / mean_square * gamma_float_ptr[h];
197+
} else {
198+
output_float_ptr[h] = (output_float_ptr[h] - mean) / mean_square * gamma_float_ptr[h] + beta_float_ptr[h];
199+
}
200+
}
201+
202+
MlasConvertFloatToHalfBuffer(output_float_ptr, p_output, num_elems);
56203
}
57204

58-
template <>
59-
ORT_FORCEINLINE constexpr double ConvertMLFloat16ToDoubleOrFloatIfNeeded<double, double>(double val) {
60-
return val;
61-
}
205+
void ConvertMLFloat16ToFloatIfNeeded(const Tensor& tensor, AllocatorPtr alloc, IAllocatorUniquePtr<float>& dest, bool& is_packed) {
206+
if (tensor.GetElementType() == utils::ToTensorProtoElementType<MLFloat16>()) {
207+
auto tensor_data_ptr = tensor.Data<MLFloat16>();
208+
auto tensor_size = static_cast<size_t>(tensor.Shape().Size());
209+
auto float_ptr = IAllocator::MakeUniquePtr<float>(alloc, tensor_size, true);
62210

63-
// Function template that only converts the input value to MLFloat16 if T is MLFloat16.
64-
template <typename T>
65-
ORT_FORCEINLINE constexpr typename std::enable_if_t<std::is_same_v<T, float> || std::is_same_v<T, double>, T>
66-
ConvertDoubleOrFloatToMLFloat16IfNeeded(T val) {
67-
return val;
211+
MlasConvertHalfToFloatBuffer(tensor_data_ptr, float_ptr.get(), tensor_size);
212+
dest = std::move(float_ptr);
213+
is_packed = true;
214+
}
68215
}
69216

70-
template <typename T>
71-
ORT_FORCEINLINE constexpr typename std::enable_if_t<std::is_same_v<T, MLFloat16>, T>
72-
ConvertDoubleOrFloatToMLFloat16IfNeeded(float val) {
73-
return MLFloat16(val);
74-
}
75-
76-
template <typename T>
77-
ORT_FORCEINLINE constexpr typename std::enable_if_t<std::is_same_v<T, MLFloat16>, T>
78-
ConvertDoubleOrFloatToMLFloat16IfNeeded(double val) {
79-
return MLFloat16(static_cast<float>(val));
80-
}
217+
} // namespace
81218

82219
template <typename T, bool simplified>
83220
SkipLayerNorm<T, simplified>::SkipLayerNorm(const OpKernelInfo& op_kernel_info)
84-
: OpKernel(op_kernel_info) {
221+
: OpKernel(op_kernel_info), skip_fp32_(nullptr), gamma_fp32_(nullptr), beta_fp32_(nullptr), bias_fp32_(nullptr) {
85222
ORT_ENFORCE(op_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK());
86223
ORT_ENFORCE(epsilon_ >= 0);
87224
}
@@ -94,8 +231,7 @@ Status SkipLayerNorm<T, simplified>::Compute(OpKernelContext* p_ctx) const {
94231
const Tensor* beta = p_ctx->Input<Tensor>(3);
95232
const Tensor* bias = p_ctx->Input<Tensor>(4);
96233
Tensor* output = p_ctx->Output(0, input->Shape());
97-
// For inferencing, we support one more optional output which is the sum
98-
// of the input and skip tensors
234+
// For inferencing, we support one more optional output which is the sum of the input and skip tensors
99235
Tensor* skip_input_bias_add_output = p_ctx->Output(3, input->Shape());
100236

101237
const auto& input_dims = input->Shape().GetDims();
@@ -120,75 +256,44 @@ Status SkipLayerNorm<T, simplified>::Compute(OpKernelContext* p_ctx) const {
120256

121257
T* output_data = output->MutableData<T>();
122258

123-
// For inferencing, we support one more optional output which is the sum
124-
// of the input and skip tensors
125-
T* skip_input_bias_add_output_data = skip_input_bias_add_output != nullptr ? skip_input_bias_add_output->MutableData<T>() : nullptr;
259+
// For inferencing, we support one more optional output which is the sum of the input and skip tensors
260+
T* skip_input_bias_add_output_data = skip_input_bias_add_output == nullptr ? nullptr : skip_input_bias_add_output->MutableData<T>();
126261

127-
const auto& skip_size = skip->Shape().Size();
262+
const int64_t& skip_size = skip->Shape().Size();
263+
264+
AllocatorPtr alloc;
265+
ORT_RETURN_IF_ERROR(p_ctx->GetTempSpaceAllocator(&alloc));
128266

129267
concurrency::ThreadPool::TryBatchParallelFor(
130268
p_ctx->GetOperatorThreadPool(), static_cast<int32_t>(task_count),
131269
[&](ptrdiff_t task_idx) {
132-
auto offset = task_idx * hidden_size;
133-
134-
const T* p_input = input_data + offset;
135-
const T* p_skip = skip_data + (offset % skip_size);
136-
T* p_output = output_data + offset;
137-
T* p_skip_input_bias_add_output_data = skip_input_bias_add_output_data != nullptr ? skip_input_bias_add_output_data + offset : nullptr;
138-
139-
using DoubleOrFloat = typename std::conditional<
140-
std::is_same<T, double>::value, // If T is double
141-
double, // Use double
142-
float // Otherwise, use float (covers float and MLFloat16)
143-
>::type;
144-
145-
DoubleOrFloat mean(0.0f);
146-
DoubleOrFloat mean_square(0.0f);
147-
148-
std::unique_ptr<DoubleOrFloat[]> output_buffer = std::make_unique<DoubleOrFloat[]>(hidden_size);
149-
for (size_t h = 0; h < static_cast<size_t>(hidden_size); h++) {
150-
DoubleOrFloat input_value = ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(p_input[h]);
151-
DoubleOrFloat skip_value = ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(p_skip[h]);
152-
153-
DoubleOrFloat value = input_value + skip_value;
154-
155-
if (nullptr != bias_data) {
156-
value += ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(bias_data[h]);
157-
}
158-
159-
output_buffer[h] = value;
160-
T converted_value = ConvertDoubleOrFloatToMLFloat16IfNeeded<T>(value);
161-
if (nullptr != p_skip_input_bias_add_output_data) {
162-
p_skip_input_bias_add_output_data[h] = converted_value;
163-
}
164-
165-
mean += value;
166-
mean_square += value * value;
167-
}
168-
169-
mean = mean / hidden_size;
170-
if (simplified) {
171-
mean_square = sqrt(mean_square / hidden_size + epsilon_);
172-
} else {
173-
mean_square = sqrt(mean_square / hidden_size - mean * mean + epsilon_);
174-
}
175-
176-
for (size_t h = 0; h < static_cast<size_t>(hidden_size); h++) {
177-
DoubleOrFloat gamma_value = ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(gamma_data[h]);
178-
if (simplified) {
179-
p_output[h] = ConvertDoubleOrFloatToMLFloat16IfNeeded<T>(output_buffer[h] / mean_square * gamma_value);
180-
} else if (nullptr == beta_data) {
181-
p_output[h] = ConvertDoubleOrFloatToMLFloat16IfNeeded<T>((output_buffer[h] - mean) / mean_square * gamma_value);
182-
} else {
183-
DoubleOrFloat beta_value = ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(beta_data[h]);
184-
p_output[h] = ConvertDoubleOrFloatToMLFloat16IfNeeded<T>((output_buffer[h] - mean) / mean_square * gamma_value + beta_value);
185-
}
186-
}
270+
ComputeJob(input_data, skip_data, gamma_data, beta_data, bias_data, skip_fp32_, gamma_fp32_, beta_fp32_,
271+
bias_fp32_, task_idx, hidden_size, skip_size, epsilon_, simplified, output_data,
272+
skip_input_bias_add_output_data, alloc);
187273
},
188274
0);
189275

190276
return Status::OK();
191277
}
192278

279+
template <typename T, bool simplified>
280+
Status SkipLayerNorm<T, simplified>::PrePack(const Tensor& tensor, int input_idx, AllocatorPtr alloc,
281+
bool& is_packed, PrePackedWeights* prepacked_weights) {
282+
ORT_UNUSED_PARAMETER(prepacked_weights);
283+
284+
is_packed = false;
285+
if (input_idx == 1) { // skip
286+
ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, skip_fp32_, is_packed);
287+
} else if (input_idx == 2) { // gamma
288+
ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, gamma_fp32_, is_packed);
289+
} else if (input_idx == 3) { // beta
290+
ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, beta_fp32_, is_packed);
291+
} else if (input_idx == 4) { // bias
292+
ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, bias_fp32_, is_packed);
293+
}
294+
295+
return Status::OK();
296+
}
297+
193298
} // namespace contrib
194299
} // namespace onnxruntime

‎onnxruntime/contrib_ops/cpu/skip_layer_norm.h‎

Lines changed: 7 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -16,8 +16,15 @@ class SkipLayerNorm final : public OpKernel {
1616
SkipLayerNorm(const OpKernelInfo& op_kernel_info);
1717
Status Compute(OpKernelContext* p_op_kernel_context) const override;
1818

19+
Status PrePack(const Tensor& tensor, int input_idx, AllocatorPtr alloc,
20+
bool& is_packed, PrePackedWeights* prepacked_weights) override;
21+
1922
private:
2023
float epsilon_;
24+
mutable IAllocatorUniquePtr<float> skip_fp32_;
25+
mutable IAllocatorUniquePtr<float> gamma_fp32_;
26+
mutable IAllocatorUniquePtr<float> beta_fp32_;
27+
mutable IAllocatorUniquePtr<float> bias_fp32_;
2128
};
2229

2330
} // namespace contrib

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