Merge pull request #41645 from Intel-tensorflow:agramesh/no_mkl_build

PiperOrigin-RevId: 324919131
Change-Id: I208164891852cc60e3fc193996122f33fda11227

.bazelrc

@@ -165,8 +165,18 @@ build:mkl -c opt
 # config to build OneDNN backend with a user specified threadpool.
 build:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
 build:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0
+build:mkl_threadpool --define=build_with_mkl_dnn_v1_only=true
+build:mkl_threadpool --define=build_with_mkl_opensource=true
 build:mkl_threadpool --define=build_with_mkldnn_threadpool=true
 build:mkl_threadpool -c opt
+
+# Config setting to build with oneDNN and without the binary blob
+build:mkl_opensource_only --define=build_with_mkl=true --define=enable_mkl=true
+build:mkl_opensource_only --define=tensorflow_mkldnn_contraction_kernel=0
+build:mkl_opensource_only --define=build_with_mkl_dnn_v1_only=true
+build:mkl_opensource_only --define=build_with_mkl_opensource=true
+build:mkl_opensource_only -c opt
+
 # This config refers to building with CUDA available. It does not necessarily
 # mean that we build CUDA op kernels.
 build:using_cuda --define=using_cuda=true

tensorflow/core/kernels/mkl_batch_matmul_op.cc

@@ -15,20 +15,26 @@ limitations under the License.
 
 // See docs in ../ops/math_ops.cc.
 
-// This file uses MKL CBLAS batched xGEMM for acceleration of TF Batch
+// This file uses both oneDNN and MKL CBLAS batched xGEMM for acceleration of
-// Matrix-Matrix Multiplication (MatMul) operations.
+// Batch Matrix-Matrix Multiplication (MatMul) operations.
-// We currently register this kernel only for MKL supported data
+// We currently register this kernel only for oneDNN supported data
-// types (float, double, complex64, complex128). The macro INTEL_MKL is defined
+// types (float, bfloat16). This file can be built with and without the use of
-// by the build system only when MKL is chosen as an option at configure stage
+// the binary MKL CBLAS calls, controlled by the macro INTEL_MKL_DNN_ONLY.
-// and when it is undefined at build time, this file becomes an empty
+// If INTEL_MKL_DNN_ONLY is defined, only oneDNN is used. For cases not
-// compilation unit
+// supported by oneDNN (ex. Batchmatmul with broadcasting) we fall back to the
+// default CPU implementation.
+// if INTEL_MKL_DNN_ONLY is not defined, both oneDNN and MKL CBLAS
+// implementations are used. This is only temporary, once we are able handle all
+// cases with oneDNN, CBLAS calls will be removed.
 
 #define EIGEN_USE_THREADS
 
 #if defined(INTEL_MKL)
 #include <vector>
 
+#if !defined(INTEL_MKL_DNN_ONLY)
 #include "mkl_cblas.h"
+#endif  // !INTEL_MKL_DNN_ONLY
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
@@ -105,14 +111,14 @@ class BatchMatMulMkl : public OpKernel {
             "In[0] and In[1] must have compatible batch dimensions: ",
             lhs.shape().DebugString(), " vs. ", rhs.shape().DebugString()));
 
-#ifdef ENABLE_MKLDNN_THREADPOOL
+#if defined(INTEL_MKL_DNN_ONLY)
     if (bcast.IsBroadcastingRequired()) {
       // Calling Eigen Kernel for broadcasting case and return. Eigen does
       // not have BF16 support, so we have to fail graciously in that case.
       eigen_batch_mm_v2_.Compute(ctx);
       return;
     }
-#endif  // ENABLE_MKLDNN_THREADPOOL
+#endif  // INTEL_MKL_DNN_ONLY
     TensorShape out_shape = bcast.output_batch_shape();
     auto batch_size = bcast.output_batch_size();
 
@@ -158,11 +164,11 @@ class BatchMatMulMkl : public OpKernel {
     std::vector<MKL_INT> ldc_array(batch_size, N);
     std::vector<MKL_INT> group_size(1, batch_size);
 
-    bool threadpool_enabled = false;
+    bool bcast_not_supported = false;
-#ifdef ENABLE_MKLDNN_THREADPOOL
+#if defined(INTEL_MKL_DNN_ONLY)
-    threadpool_enabled = true;
+    bcast_not_supported = true;
-#endif  // ENABLE_MKLDNN_THREADPOOL
+#endif  // INTEL_MKL_DNN_ONLY
-    if (std::is_same<Scalar, bfloat16>::value || threadpool_enabled) {
+    if (std::is_same<Scalar, bfloat16>::value || bcast_not_supported) {
       // DNNL bfloat16 API requires a, b, and c as pointers to tensors
       // represented as flat-byte array.
       const Scalar* a = nullptr;
@@ -227,7 +233,7 @@ class BatchMatMulMkl : public OpKernel {
       const std::vector<MKL_INT>& ldb_Array, float** C_Array,
       const std::vector<MKL_INT>& ldc_Array, const MKL_INT group_count,
       const std::vector<MKL_INT>& group_size, OpKernelContext* ctx) {
-#ifndef ENABLE_MKLDNN_THREADPOOL
+#if !defined(INTEL_MKL_DNN_ONLY)
     std::vector<CBLAS_TRANSPOSE> TransA_Array(
         group_size[0], TransA ? CblasTrans : CblasNoTrans);
     std::vector<CBLAS_TRANSPOSE> TransB_Array(
@@ -249,7 +255,7 @@ class BatchMatMulMkl : public OpKernel {
     dnnl_gemm_batch<float>(TransA_Array, TransB_Array, M_Array, N_Array,
                            K_Array, alpha_Array, *A_Array, *B_Array, beta_Array,
                            *C_Array, group_count, group_size, ctx);
-#endif  // !ENABLE_MKLDNN_THREADPOOL
+#endif  // !INTEL_MKL_DNN_ONLY
   }
 // BatchMatMul BFloat16 support only exists in DNNL 1.2 onwards.
 #if defined(ENABLE_MKLDNN_V1) && defined(ENABLE_INTEL_MKL_BFLOAT16)

tensorflow/core/kernels/mkl_matmul_ops_common.h

@@ -35,7 +35,12 @@ using mkldnn::stream;
 namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
-
+#ifdef INTEL_MKL_DNN_ONLY
+// Temporarily copying some definitions from mkl_cblas.h so the same code can
+// be used when calling oneDNN or CBLAS batchmatmul in mkl_batch_matmul_op.cc.
+typedef enum { CblasRowMajor, CblasColumnMajor } CBLAS_LAYOUT;
+#define MKL_INT int
+#endif
 // This structure aggregates multiple inputs to MklDnnMatMul* methods.
 struct MklDnnMatMulFwdParams {
   memory::dims src_dims;

tensorflow/tensorflow.bzl

@@ -47,7 +47,7 @@ load(
 load(
     "//third_party/mkl_dnn:build_defs.bzl",
     "if_mkl_open_source_only",
-    "if_mkl_v1_open_source_only",
+    "if_mkl_v1",
     "if_mkldnn_threadpool",
 )
 load(
@@ -327,12 +327,8 @@ def tf_copts(
         if_tensorrt(["-DGOOGLE_TENSORRT=1"]) +
         if_mkl(["-DINTEL_MKL=1", "-DEIGEN_USE_VML"]) +
         if_mkl_open_source_only(["-DINTEL_MKL_DNN_ONLY"]) +
-        if_mkl_v1_open_source_only(["-DENABLE_MKLDNN_V1", "-DENABLE_INTEL_MKL_BFLOAT16"]) +
+        if_mkl_v1(["-DENABLE_MKLDNN_V1", "-DENABLE_INTEL_MKL_BFLOAT16"]) +
-        if_mkldnn_threadpool([
+        if_mkldnn_threadpool(["-DENABLE_MKLDNN_THREADPOOL"]) +
-            "-DENABLE_MKLDNN_THREADPOOL",
-            "-DENABLE_MKLDNN_V1",
-            "-DINTEL_MKL_DNN_ONLY",
-        ]) +
         if_enable_mkl(["-DENABLE_MKL"]) +
         if_ngraph(["-DINTEL_NGRAPH=1"]) +
         if_android_arm(["-mfpu=neon"]) +

third_party/mkl/BUILD

@@ -10,15 +10,6 @@ config_setting(
     visibility = ["//visibility:public"],
 )
 
-config_setting(
-    name = "build_with_mkl_ml_only",
-    define_values = {
-        "build_with_mkl": "true",
-        "build_with_mkl_ml_only": "true",
-    },
-    visibility = ["//visibility:public"],
-)
-
 config_setting(
     name = "build_with_mkl_lnx_x64",
     define_values = {
@@ -39,11 +30,6 @@ config_setting(
     visibility = ["//visibility:public"],
 )
 
-load(
-    "//third_party/mkl:build_defs.bzl",
-    "if_mkl",
-)
-
 filegroup(
     name = "LICENSE",
     srcs = ["MKL_LICENSE"] + select({

third_party/mkl/build_defs.bzl

@@ -41,26 +41,11 @@ def if_mkl_ml(if_true, if_false = []):
       a select evaluating to either if_true or if_false as appropriate.
     """
     return select({
-        "@org_tensorflow//third_party/mkl_dnn:build_with_mkl_dnn_only": if_false,
+        "@org_tensorflow//third_party/mkl_dnn:build_with_mkl_opensource": if_false,
         "@org_tensorflow//third_party/mkl:build_with_mkl": if_true,
         "//conditions:default": if_false,
     })
 
-def if_mkl_ml_only(if_true, if_false = []):
-    """Shorthand for select()'ing on whether we're building with MKL-ML only.
-
-    Args:
-      if_true: expression to evaluate if building with MKL-ML only.
-      if_false: expression to evaluate if building without MKL, or with MKL-DNN.
-
-    Returns:
-      a select evaluating to either if_true or if_false as appropriate.
-    """
-    return select({
-        "@org_tensorflow//third_party/mkl:build_with_mkl_ml_only": if_true,
-        "//conditions:default": if_false,
-    })
-
 def if_mkl_lnx_x64(if_true, if_false = []):
     """Shorthand to select() if building with MKL and the target is Linux x86-64.
 
@@ -107,8 +92,6 @@ def mkl_deps():
     return select({
         "@org_tensorflow//third_party/mkl_dnn:build_with_mkl_dnn_only": ["@mkl_dnn"],
         "@org_tensorflow//third_party/mkl_dnn:build_with_mkl_dnn_v1_only": ["@mkl_dnn_v1//:mkl_dnn"],
-        "@org_tensorflow//third_party/mkl_dnn:build_with_mkldnn_threadpool": ["@mkl_dnn_v1//:mkl_dnn"],
-        "@org_tensorflow//third_party/mkl:build_with_mkl_ml_only": ["@org_tensorflow//third_party/mkl:intel_binary_blob"],
         "@org_tensorflow//third_party/mkl:build_with_mkl": [
             "@org_tensorflow//third_party/mkl:intel_binary_blob",
             "@mkl_dnn",

third_party/mkl_dnn/BUILD

@@ -18,6 +18,16 @@ config_setting(
     visibility = ["//visibility:public"],
 )
 
+config_setting(
+    name = "build_with_mkl_opensource",
+    define_values = {
+        "build_with_mkl": "true",
+        "build_with_mkl_dnn_v1_only": "true",
+        "build_with_mkl_opensource": "true",
+    },
+    visibility = ["//visibility:public"],
+)
+
 config_setting(
     name = "build_with_mkl_dnn_v1_only",
     define_values = {
@@ -31,6 +41,8 @@ config_setting(
     name = "build_with_mkldnn_threadpool",
     define_values = {
         "build_with_mkl": "true",
+        "build_with_mkl_dnn_v1_only": "true",
+        "build_with_mkl_opensource": "true",
         "build_with_mkldnn_threadpool": "true",
     },
     visibility = ["//visibility:public"],

third_party/mkl_dnn/build_defs.bzl

@@ -10,11 +10,11 @@ def if_mkl_open_source_only(if_true, if_false = []):
 
     """
     return select({
-        "@org_tensorflow//third_party/mkl_dnn:build_with_mkl_dnn_only": if_true,
+        "@org_tensorflow//third_party/mkl_dnn:build_with_mkl_opensource": if_true,
         "//conditions:default": if_false,
     })
 
-def if_mkl_v1_open_source_only(if_true, if_false = []):
+def if_mkl_v1(if_true, if_false = []):
     """Returns `if_true` if MKL-DNN v1.x is used.
 
     Shorthand for select()'ing on whether we're building with

third_party/mkl_dnn/mkldnn.BUILD

@@ -3,7 +3,7 @@ exports_files(["LICENSE"])
 load(
     "@org_tensorflow//third_party/mkl_dnn:build_defs.bzl",
     "if_mkl_open_source_only",
-    "if_mkl_v1_open_source_only",
+    "if_mkl_v1",
 )
 load(
     "@org_tensorflow//third_party:common.bzl",
@@ -60,7 +60,7 @@ cc_library(
         "src/cpu/**/*.cpp",
         "src/cpu/**/*.hpp",
         "src/cpu/xbyak/*.h",
-    ]) + if_mkl_v1_open_source_only([
+    ]) + if_mkl_v1([
         ":mkldnn_config_h",
     ]) + [":mkldnn_version_h"],
     hdrs = glob(["include/*"]),
@@ -71,7 +71,7 @@ cc_library(
     ] + if_mkl_open_source_only([
         "-UUSE_MKL",
         "-UUSE_CBLAS",
-    ]) + if_mkl_v1_open_source_only([
+    ]) + if_mkl_v1([
         "-UUSE_MKL",
         "-UUSE_CBLAS",
     ]) + select({

third_party/mkl_dnn/mkldnn_v1.BUILD

@@ -3,9 +3,13 @@ exports_files(["LICENSE"])
 load(
     "@org_tensorflow//third_party/mkl_dnn:build_defs.bzl",
     "if_mkl_open_source_only",
-    "if_mkl_v1_open_source_only",
+    "if_mkl_v1",
     "if_mkldnn_threadpool",
 )
+load(
+    "@org_tensorflow//third_party/mkl:build_defs.bzl",
+    "if_mkl_ml",
+)
 load(
     "@org_tensorflow//third_party:common.bzl",
     "template_rule",
@@ -85,7 +89,7 @@ cc_library(
     ] + if_mkl_open_source_only([
         "-UUSE_MKL",
         "-UUSE_CBLAS",
-    ]) + if_mkl_v1_open_source_only([
+    ]) + if_mkl_v1([
         "-UUSE_MKL",
         "-UUSE_CBLAS",
     ]) + if_mkldnn_threadpool([
@@ -109,21 +113,10 @@ cc_library(
         "src/cpu/xbyak",
     ],
     visibility = ["//visibility:public"],
-    deps = select({
+    deps = if_mkl_ml(
-        "@org_tensorflow//tensorflow:linux_x86_64": [
+        ["@org_tensorflow//third_party/mkl:intel_binary_blob"],
-            "@mkl_linux//:mkl_headers",
+        [],
-            "@mkl_linux//:mkl_libs_linux",
+    ),
-        ],
-        "@org_tensorflow//tensorflow:macos": [
-            "@mkl_darwin//:mkl_headers",
-            "@mkl_darwin//:mkl_libs_darwin",
-        ],
-        "@org_tensorflow//tensorflow:windows": [
-            "@mkl_windows//:mkl_headers",
-            "@mkl_windows//:mkl_libs_windows",
-        ],
-        "//conditions:default": [],
-    }),
 )
 
 cc_library(