Merge main

CITATION.cff

@@ -3,8 +3,7 @@ title: ONNX Runtime
 message: "Please use this information to cite ONNX Runtime in
   research or other publications."
 authors:
-  - affiliation: Microsoft Corporation
-    given-names: ONNX Runtime developers
+  - name: ONNX Runtime developers
 date-released: 2018-11-29
 url: "https://onnxruntime.ai"
 repository-code: "https://github.com/microsoft/onnxruntime"

cmake/CMakeLists.txt

@@ -1729,14 +1729,12 @@ if(onnxruntime_BUILD_KERNEL_EXPLORER)
 endif()
 
 # When GDK_PLATFORM is set then WINAPI_FAMILY is defined in gdk_toolchain.cmake (along with other relevant flags/definitions).
-if (WIN32 AND NOT GDK_PLATFORM)
+if (WIN32 AND NOT GDK_PLATFORM AND NOT CMAKE_CROSSCOMPILING)
   if (NOT CMAKE_CXX_STANDARD_LIBRARIES MATCHES kernel32.lib)
     # On onecore, link to the onecore build of the MSVC runtime
     get_filename_component(msvc_path "${CMAKE_C_COMPILER}/../../../.." ABSOLUTE)
     link_directories(BEFORE "${msvc_path}/lib/onecore/${onnxruntime_target_platform}")
-    # The .lib files in the MSVC runtime have a DEFAULITLIB entry for onecore.lib, which in turn links to reverse forwarders.
-    # We ignore that entry and use onecore_apiset.lib instead, since system components must not rely on reverse forwarders.
-    add_link_options("/NODEFAULTLIB:onecore.lib")
+    # The .lib files in the MSVC runtime have a DEFAULITLIB entry for onecore.lib, but it shold not cause any conflict with onecoreuap.lib
   endif()
 endif()
 

cmake/adjust_global_compile_flags.cmake

@@ -92,13 +92,8 @@ if (onnxruntime_MINIMAL_BUILD)
   endif()
 endif()
 
-# Enable stream for all the non-minimal build, except for DML. There's currently a bug
-# in the allocation planner when reusing buffers and more than one streams are used that
-# make it possible (although rarely) to reach a reference count of 0 for a buffer that is
-# still being used. Since DML doesn't benefit from multiple streams, disabling it is the
-# safest option for now.
-# https://github.com/microsoft/onnxruntime/issues/19480
-if (NOT onnxruntime_MINIMAL_BUILD AND NOT onnxruntime_USE_DML)
+# Enable stream for all the non-minimal build
+if (NOT onnxruntime_MINIMAL_BUILD)
   add_compile_definitions(ORT_ENABLE_STREAM)
 endif()
 

cmake/onnxruntime_rocm_hipify.cmake

@@ -20,10 +20,6 @@ set(contrib_ops_excluded_files
   "bert/fastertransformer_decoder_attention/*"
   "bert/multihead_attention.cc"
   "bert/multihead_attention.h"
-  "bert/fast_gelu_impl.cu"
-  "bert/fast_gelu_impl.h"
-  "bert/fast_gelu.cc"
-  "bert/fast_gelu.h"
   "bert/relative_attn_bias.cc"
   "bert/relative_attn_bias.h"
   "bert/relative_attn_bias_impl.cu"
@@ -44,12 +40,7 @@ set(contrib_ops_excluded_files
   "bert/packed_multihead_attention.cc"
   "bert/packed_multihead_attention_impl.h"
   "bert/packed_multihead_attention_impl.cu"
-  "diffusion/group_norm.cc"
   "diffusion/group_norm_impl.cu"
-  "diffusion/group_norm_impl.h"
-  "diffusion/group_norm_impl_kernel.cuh"
-  "diffusion/group_norm_common_base.h"
-  "diffusion/group_norm_common_base.cc"
   "diffusion/nhwc_conv.cc"
   "math/gemm_float8.cc"
   "math/gemm_float8.cu"

cmake/wcos_rules_override.cmake

@@ -1,2 +1,2 @@
-set(CMAKE_C_STANDARD_LIBRARIES_INIT onecoreuap_apiset.lib)
-set(CMAKE_CXX_STANDARD_LIBRARIES_INIT onecoreuap_apiset.lib)
+set(CMAKE_C_STANDARD_LIBRARIES_INIT onecoreuap.lib)
+set(CMAKE_CXX_STANDARD_LIBRARIES_INIT onecoreuap.lib)

cmake/winml.cmake

@@ -827,6 +827,7 @@ if (winml_is_inbox)
     get_target_property(compile_options ${target} COMPILE_OPTIONS)
     get_target_property(include_directories ${target} INCLUDE_DIRECTORIES)
     get_target_property(link_libraries ${target} LINK_LIBRARIES)
+    get_target_property(link_flags ${target} LINK_FLAGS)
     get_target_property(link_options ${target} LINK_OPTIONS)
 
     add_library(${new_target} SHARED ${sources})
@@ -835,6 +836,7 @@ if (winml_is_inbox)
     target_compile_options(${new_target} PRIVATE ${compile_options})
     target_include_directories(${new_target} PRIVATE ${include_directories})
     target_link_libraries(${new_target} PRIVATE ${link_libraries})
+    set_property(TARGET ${new_target} PROPERTY LINK_FLAGS "${link_flags}")
     target_link_options(${new_target} PRIVATE ${link_options})
   endfunction()
 

csharp/test/Microsoft.ML.OnnxRuntime.Tests.NetCoreApp/InferenceTest.netcore.cs

@@ -145,7 +145,7 @@ private void TestCUDAProviderOptions()
         private void CanRunInferenceOnAModelWithTensorRT()
         {
             string modelPath = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet.onnx");
-            
+
             int deviceId = 0;
             string deviceIdStr = System.Environment.GetEnvironmentVariable("ONNXRUNTIME_TEST_GPU_DEVICE_ID");
             if (!string.IsNullOrEmpty(deviceIdStr) && int.TryParse(deviceIdStr, out int parsedValue) && parsedValue >= 0)

docs/OperatorKernels.md

@@ -127,6 +127,7 @@ Do not modify directly.*
 |GatherND|*in* data:**T**<br> *in* indices:**tensor(int64)**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **indices** = tensor(int64)|
 |||12|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **indices** = tensor(int64)|
 |||11|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **indices** = tensor(int64)|
+|Gelu|*in* X:**T**<br> *out* Y:**T**|20+|**T** = tensor(float)|
 |Gemm|*in* A:**T**<br> *in* B:**T**<br> *in* C:**T**<br> *out* Y:**T**|13+|**T** = tensor(double), tensor(float)|
 |||[11, 12]|**T** = tensor(double), tensor(float)|
 |||[9, 10]|**T** = tensor(double), tensor(float)|
@@ -606,6 +607,7 @@ Do not modify directly.*
 |GatherND|*in* data:**T**<br> *in* indices:**tensor(int64)**<br> *out* output:**T**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int64)<br/> **indices** = tensor(int64)|
 |||12|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int64)<br/> **indices** = tensor(int64)|
 |||11|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int64)<br/> **indices** = tensor(int64)|
+|Gelu|*in* X:**T**<br> *out* Y:**T**|20+|**T** = tensor(double), tensor(float), tensor(float16)|
 |Gemm|*in* A:**T**<br> *in* B:**T**<br> *in* C:**T**<br> *out* Y:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
 |||[11, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
 |||[9, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
@@ -617,6 +619,7 @@ Do not modify directly.*
 |||[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
 |GreaterOrEqual|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|16+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T1** = tensor(bool)|
 |||[12, 15]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T1** = tensor(bool)|
+|GridSample|*in* X:**T1**<br> *in* grid:**T2**<br> *out* Y:**T1**|16+|**T1** = tensor(float)<br/> **T2** = tensor(float)|
 |HardSigmoid|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(double), tensor(float), tensor(float16)|
 |Identity|*in* input:**T**<br> *out* output:**T**<br><br>or<br><br>*in* input:**V**<br> *out* output:**V**|19+|**V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(float8e4m3fn)), seq(tensor(float8e4m3fnuz)), seq(tensor(float8e5m2)), seq(tensor(float8e5m2fnuz)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
 |||[14, 18]|**V** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|

include/onnxruntime/core/framework/execution_provider.h

@@ -33,6 +33,8 @@ class Node;
 #include "core/framework/stream_handles.h"
 #include "core/framework/tuning_context.h"
 
+struct OrtRunOptions;
+
 namespace onnxruntime {
 
 /**
@@ -51,6 +53,8 @@ struct NodeComputeInfo {
   DestroyFunctionStateFunc release_state_func;
 };
 
+using RunOptions = OrtRunOptions;
+
 enum class DataLayout {
   NCHW,
   NHWC,
@@ -184,15 +188,17 @@ class IExecutionProvider {
      Run may not be finished on device This function should be regarded as the
      point after which a new Run would start to submit commands from CPU
   */
-  virtual common::Status OnRunStart() { return Status::OK(); }
+  virtual common::Status OnRunStart(const onnxruntime::RunOptions& /*run_options*/) { return Status::OK(); }
 
   /**
      Called when InferenceSession::Run ended
      NOTE that due to async execution in provider, the actual work of this Run
      may not be finished on device This function should be regarded as the point
      that all commands of current Run has been submmited by CPU
   */
-  virtual common::Status OnRunEnd(bool /*sync_stream*/) { return Status::OK(); }
+  virtual common::Status OnRunEnd(bool /*sync_stream*/, const onnxruntime::RunOptions& /*run_options*/) {
+    return Status::OK();
+  }
 
   /**
      Indicate whether the graph capturing mode (e.g., cuda graph) is enabled for

include/onnxruntime/core/providers/cuda/cuda_resource.h

@@ -19,4 +19,4 @@ enum CudaResource : int {
   enable_skip_layer_norm_strict_mode_t,
   prefer_nhwc_t,
   use_tf32_t,
-};
+};

include/onnxruntime/core/session/onnxruntime_run_options_config_keys.h

@@ -30,3 +30,15 @@ static const char* const kOrtRunOptionsConfigEnableMemoryArenaShrinkage = "memor
 // Per default it will be set to '0'
 // Taking CUDA EP as an example, it omit triggering cudaStreamSynchronize on the compute stream.
 static const char* const kOrtRunOptionsConfigDisableSynchronizeExecutionProviders = "disable_synchronize_execution_providers";
+
+// Set HTP performance mode for QNN HTP backend before session run.
+// options for HTP performance mode: "burst", "balanced", "default", "high_performance",
+// "high_power_saver", "low_balanced", "extreme_power_saver", "low_power_saver", "power_saver",
+// "sustained_high_performance". Default to "default".
+static const char* const kOrtRunOptionsConfigQnnPerfMode = "qnn.htp_perf_mode";
+
+// Set HTP performance mode for QNN HTP backend post session run.
+static const char* const kOrtRunOptionsConfigQnnPerfModePostRun = "qnn.htp_perf_mode_post_run";
+
+// Set RPC control latency for QNN HTP backend
+static const char* const kOrtRunOptionsConfigQnnRpcControlLatency = "qnn.rpc_control_latency";

java/src/main/java/ai/onnxruntime/providers/CoreMLFlags.java

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021, 2023, Oracle and/or its affiliates. All rights reserved.
+ * Copyright (c) 2021, 2024, Oracle and/or its affiliates. All rights reserved.
  * Licensed under the MIT License.
  */
 package ai.onnxruntime.providers;
@@ -14,7 +14,18 @@ public enum CoreMLFlags implements OrtFlags {
   /** Enables CoreML on subgraphs. */
   ENABLE_ON_SUBGRAPH(2), // COREML_FLAG_ENABLE_ON_SUBGRAPH(0x002)
   /** Only enable usage of CoreML if the device has an Apple Neural Engine. */
-  ONLY_ENABLE_DEVICE_WITH_ANE(4); // COREML_FLAG_ONLY_ENABLE_DEVICE_WITH_ANE(0x004),
+  ONLY_ENABLE_DEVICE_WITH_ANE(4), // COREML_FLAG_ONLY_ENABLE_DEVICE_WITH_ANE(0x004)
+  /**
+   * Only allow CoreML EP to take nodes with inputs with static shapes. By default it will also
+   * allow inputs with dynamic shapes. However, the performance may be negatively impacted if inputs
+   * have dynamic shapes.
+   */
+  ONLY_ALLOW_STATIC_INPUT_SHAPES(8), // COREML_FLAG_ONLY_ALLOW_STATIC_INPUT_SHAPES(0x008)
+  /**
+   * Create an MLProgram. By default it will create a NeuralNetwork model. Requires Core ML 5 or
+   * later.
+   */
+  CREATE_MLPROGRAM(16); // COREML_FLAG_CREATE_MLPROGRAM(0x010)
 
   /** The native value of the enum. */
   public final int value;

js/common/lib/tensor-impl-type-mapping.ts

@@ -14,7 +14,6 @@ export const NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP = new Map<string, SupportedTy
   ['uint8', Uint8Array],
   ['int8', Int8Array],
   ['uint16', Uint16Array],
-  ['float16', Uint16Array],
   ['int16', Int16Array],
   ['int32', Int32Array],
   ['bool', Uint8Array],
@@ -34,16 +33,22 @@ export const NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP = new Map<SupportedTypedArray
   [Uint32Array, 'uint32'],
 ]);
 
-// the following code allows delaying execution of BigInt checking. This allows lazy initialization for
-// NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP and NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP, which allows BigInt polyfill
-// if available.
-let isBigIntChecked = false;
-export const checkBigInt = () => {
-  if (!isBigIntChecked) {
-    isBigIntChecked = true;
-    const isBigInt64ArrayAvailable = typeof BigInt64Array !== 'undefined' && typeof BigInt64Array.from === 'function';
-    const isBigUint64ArrayAvailable =
-        typeof BigUint64Array !== 'undefined' && typeof BigUint64Array.from === 'function';
+// a dummy type declaration for Float16Array in case any polyfill is available.
+declare global {
+  // eslint-disable-next-line @typescript-eslint/naming-convention, @typescript-eslint/no-explicit-any
+  const Float16Array: any;
+}
+
+// the following code allows delaying execution of BigInt/Float16Array checking. This allows lazy initialization for
+// NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP and NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP, which allows BigInt/Float16Array
+// polyfill if available.
+let isTypedArrayChecked = false;
+export const checkTypedArray = () => {
+  if (!isTypedArrayChecked) {
+    isTypedArrayChecked = true;
+    const isBigInt64ArrayAvailable = typeof BigInt64Array !== 'undefined' && BigInt64Array.from;
+    const isBigUint64ArrayAvailable = typeof BigUint64Array !== 'undefined' && BigUint64Array.from;
+    const isFloat16ArrayAvailable = typeof Float16Array !== 'undefined' && Float16Array.from;
 
     if (isBigInt64ArrayAvailable) {
       NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('int64', BigInt64Array);
@@ -53,5 +58,12 @@ export const checkBigInt = () => {
       NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('uint64', BigUint64Array);
       NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigUint64Array, 'uint64');
     }
+    if (isFloat16ArrayAvailable) {
+      NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('float16', Float16Array);
+      NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(Float16Array, 'float16');
+    } else {
+      // if Float16Array is not available, use 'Uint16Array' to store the data.
+      NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('float16', Uint16Array);
+    }
   }
 };

js/common/lib/tensor-impl.ts

@@ -5,7 +5,7 @@ import {tensorToDataURL, tensorToImageData} from './tensor-conversion-impl.js';
 import {TensorToDataUrlOptions, TensorToImageDataOptions} from './tensor-conversion.js';
 import {tensorFromGpuBuffer, tensorFromImage, tensorFromPinnedBuffer, tensorFromTexture} from './tensor-factory-impl.js';
 import {CpuPinnedConstructorParameters, GpuBufferConstructorParameters, TensorFromGpuBufferOptions, TensorFromImageBitmapOptions, TensorFromImageDataOptions, TensorFromImageElementOptions, TensorFromTextureOptions, TensorFromUrlOptions, TextureConstructorParameters} from './tensor-factory.js';
-import {checkBigInt, NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP, NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP, SupportedTypedArray, SupportedTypedArrayConstructors} from './tensor-impl-type-mapping.js';
+import {checkTypedArray, NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP, NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP, SupportedTypedArray, SupportedTypedArrayConstructors} from './tensor-impl-type-mapping.js';
 import {calculateSize, tensorReshape} from './tensor-utils-impl.js';
 import {Tensor as TensorInterface} from './tensor.js';
 
@@ -67,8 +67,8 @@ export class Tensor implements TensorInterface {
       arg0: TensorType|TensorDataType|readonly string[]|readonly boolean[]|CpuPinnedConstructorParameters|
       TextureConstructorParameters|GpuBufferConstructorParameters,
       arg1?: TensorDataType|readonly number[]|readonly string[]|readonly boolean[], arg2?: readonly number[]) {
-    // perform one-time check for BigInt support
-    checkBigInt();
+    // perform one-time check for BigInt/Float16Array support
+    checkTypedArray();
 
     let type: TensorType;
     let dims: readonly number[];
@@ -142,7 +142,9 @@ export class Tensor implements TensorInterface {
             throw new TypeError(`Unsupported tensor type: ${arg0}.`);
           }
           if (Array.isArray(arg1)) {
-            if (arg0 === 'float16') {
+            if (arg0 === 'float16' && typedArrayConstructor === Uint16Array) {
+              // When no Float16Array polyfill is used, we cannot create 'float16' tensor from number array.
+              //
               // Throw error here because when user try to use number array as data,
               // e.g. new Tensor('float16', [1, 2, 3, 4], dims)), it will actually call
               // Uint16Array.from(arg1) which generates wrong data.