Prepare for TensorRT 8.6

The upcoming CUDA-12 upgrades requires TensorRT 8.6 and this version has a new set of headers which requires an update to the bazel configure script. I also had to change `find_cuda_config.py` because previously it has only been reporting the major version of TensorRT back to the configure script. But we will also need the minor version to distinguish between TensorRT 8.5 and below, and TensorRT 8.6+. PiperOrigin-RevId: 564927510

Prepare for TensorRT 8.6
The upcoming CUDA-12 upgrades requires TensorRT 8.6 and this version has a new set of headers which requires an update to the bazel configure script. I also had to change `find_cuda_config.py` because previously it has only been reporting the major version of TensorRT back to the configure script. But we will also need the minor version to distinguish between TensorRT 8.5 and below, and TensorRT 8.6+. PiperOrigin-RevId: 564927510
9377294a · A. Unique TensorFlower · TensorFlower Gardener · 93dee739 · 9377294a · 9377294a
9 changed file
--- a/third_party/gpus/find_cuda_config.py
+++ b/third_party/gpus/find_cuda_config.py
@@ -53,6 +53,12 @@ tf_<library>_header_dir: ...
 tf_<library>_library_dir: ...
 """
+# You can use the following command to regenerate the base64 version of this
+# script:
+# cat third_party/tensorflow/third_party/gpus/find_cuda_config.oss.py |
+#   pigz -z | base64 -w0 >
+#   third_party/tensorflow/third_party/gpus/find_cuda_config.py.gz.base64.oss
 import io
 import os
 import glob
@@ -528,7 +534,7 @@ def _find_tensorrt_config(base_paths, required_version):
  library_path = _find_library(base_paths, "nvinfer", tensorrt_version)
  return {
-      "tensorrt_version": tensorrt_version,
+      "tensorrt_version": header_version,
      "tensorrt_include_dir": os.path.dirname(header_path),
      "tensorrt_library_dir": os.path.dirname(library_path),
  }

--- a/third_party/gpus/find_cuda_config.py.gz.base64
+++ b/third_party/gpus/find_cuda_config.py.gz.base64
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
\ No newline at end of file
--- a/third_party/tensorrt/tensorrt_configure.bzl
+++ b/third_party/tensorrt/tensorrt_configure.bzl
@@ -47,6 +47,22 @@ _TF_TENSORRT_HEADERS_V8 = [
    "NvInferRuntimeCommon.h",
    "NvInferPluginUtils.h",
 ]
+_TF_TENSORRT_HEADERS_V8_6 = [
+    "NvInfer.h",
+    "NvInferConsistency.h",
+    "NvInferConsistencyImpl.h",
+    "NvInferImpl.h",
+    "NvInferLegacyDims.h",
+    "NvInferPlugin.h",
+    "NvInferPluginUtils.h",
+    "NvInferRuntime.h",
+    "NvInferRuntimeBase.h",
+    "NvInferRuntimeCommon.h",
+    "NvInferRuntimePlugin.h",
+    "NvInferSafeRuntime.h",
+    "NvInferVersion.h",
+    "NvUtils.h",
+]
 _DEFINE_TENSORRT_SONAME_MAJOR = "#define NV_TENSORRT_SONAME_MAJOR"
 _DEFINE_TENSORRT_SONAME_MINOR = "#define NV_TENSORRT_SONAME_MINOR"
@@ -73,6 +89,8 @@ def _at_least_version(actual_version, required_version):
    return actual >= required
 def _get_tensorrt_headers(tensorrt_version):
+    if _at_least_version(tensorrt_version, "8.6"):
+        return _TF_TENSORRT_HEADERS_V8_6
    if _at_least_version(tensorrt_version, "8"):
        return _TF_TENSORRT_HEADERS_V8
    if _at_least_version(tensorrt_version, "6"):

--- a/third_party/xla/third_party/gpus/find_cuda_config.py
+++ b/third_party/xla/third_party/gpus/find_cuda_config.py
@@ -53,6 +53,12 @@ tf_<library>_header_dir: ...
 tf_<library>_library_dir: ...
 """
+# You can use the following command to regenerate the base64 version of this
+# script:
+# cat third_party/tensorflow/third_party/gpus/find_cuda_config.oss.py |
+#   pigz -z | base64 -w0 >
+#   third_party/tensorflow/third_party/gpus/find_cuda_config.py.gz.base64.oss
 import io
 import os
 import glob
@@ -528,7 +534,7 @@ def _find_tensorrt_config(base_paths, required_version):
  library_path = _find_library(base_paths, "nvinfer", tensorrt_version)
  return {
-      "tensorrt_version": tensorrt_version,
+      "tensorrt_version": header_version,
      "tensorrt_include_dir": os.path.dirname(header_path),
      "tensorrt_library_dir": os.path.dirname(library_path),
  }

--- a/third_party/xla/third_party/gpus/find_cuda_config.py.gz.base64
+++ b/third_party/xla/third_party/gpus/find_cuda_config.py.gz.base64
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
\ No newline at end of file
--- a/third_party/xla/third_party/tensorrt/tensorrt_configure.bzl
+++ b/third_party/xla/third_party/tensorrt/tensorrt_configure.bzl
@@ -47,6 +47,22 @@ _TF_TENSORRT_HEADERS_V8 = [
    "NvInferRuntimeCommon.h",
    "NvInferPluginUtils.h",
 ]
+_TF_TENSORRT_HEADERS_V8_6 = [
+    "NvInfer.h",
+    "NvInferConsistency.h",
+    "NvInferConsistencyImpl.h",
+    "NvInferImpl.h",
+    "NvInferLegacyDims.h",
+    "NvInferPlugin.h",
+    "NvInferPluginUtils.h",
+    "NvInferRuntime.h",
+    "NvInferRuntimeBase.h",
+    "NvInferRuntimeCommon.h",
+    "NvInferRuntimePlugin.h",
+    "NvInferSafeRuntime.h",
+    "NvInferVersion.h",
+    "NvUtils.h",
+]
 _DEFINE_TENSORRT_SONAME_MAJOR = "#define NV_TENSORRT_SONAME_MAJOR"
 _DEFINE_TENSORRT_SONAME_MINOR = "#define NV_TENSORRT_SONAME_MINOR"
@@ -73,6 +89,8 @@ def _at_least_version(actual_version, required_version):
    return actual >= required
 def _get_tensorrt_headers(tensorrt_version):
+    if _at_least_version(tensorrt_version, "8.6"):
+        return _TF_TENSORRT_HEADERS_V8_6
    if _at_least_version(tensorrt_version, "8"):
        return _TF_TENSORRT_HEADERS_V8
    if _at_least_version(tensorrt_version, "6"):

--- a/third_party/xla/third_party/tsl/third_party/gpus/find_cuda_config.py
+++ b/third_party/xla/third_party/tsl/third_party/gpus/find_cuda_config.py
@@ -53,6 +53,12 @@ tf_<library>_header_dir: ...
 tf_<library>_library_dir: ...
 """
+# You can use the following command to regenerate the base64 version of this
+# script:
+# cat third_party/tensorflow/third_party/gpus/find_cuda_config.oss.py |
+#   pigz -z | base64 -w0 >
+#   third_party/tensorflow/third_party/gpus/find_cuda_config.py.gz.base64.oss
 import io
 import os
 import glob
@@ -528,7 +534,7 @@ def _find_tensorrt_config(base_paths, required_version):
  library_path = _find_library(base_paths, "nvinfer", tensorrt_version)
  return {
-      "tensorrt_version": tensorrt_version,
+      "tensorrt_version": header_version,
      "tensorrt_include_dir": os.path.dirname(header_path),
      "tensorrt_library_dir": os.path.dirname(library_path),
  }

--- a/third_party/xla/third_party/tsl/third_party/gpus/find_cuda_config.py.gz.base64
+++ b/third_party/xla/third_party/tsl/third_party/gpus/find_cuda_config.py.gz.base64
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
\ No newline at end of file
--- a/third_party/xla/third_party/tsl/third_party/tensorrt/tensorrt_configure.bzl
+++ b/third_party/xla/third_party/tsl/third_party/tensorrt/tensorrt_configure.bzl
@@ -47,6 +47,22 @@ _TF_TENSORRT_HEADERS_V8 = [
    "NvInferRuntimeCommon.h",
    "NvInferPluginUtils.h",
 ]
+_TF_TENSORRT_HEADERS_V8_6 = [
+    "NvInfer.h",
+    "NvInferConsistency.h",
+    "NvInferConsistencyImpl.h",
+    "NvInferImpl.h",
+    "NvInferLegacyDims.h",
+    "NvInferPlugin.h",
+    "NvInferPluginUtils.h",
+    "NvInferRuntime.h",
+    "NvInferRuntimeBase.h",
+    "NvInferRuntimeCommon.h",
+    "NvInferRuntimePlugin.h",
+    "NvInferSafeRuntime.h",
+    "NvInferVersion.h",
+    "NvUtils.h",
+]
 _DEFINE_TENSORRT_SONAME_MAJOR = "#define NV_TENSORRT_SONAME_MAJOR"
 _DEFINE_TENSORRT_SONAME_MINOR = "#define NV_TENSORRT_SONAME_MINOR"
@@ -73,6 +89,8 @@ def _at_least_version(actual_version, required_version):
    return actual >= required
 def _get_tensorrt_headers(tensorrt_version):
+    if _at_least_version(tensorrt_version, "8.6"):
+        return _TF_TENSORRT_HEADERS_V8_6
    if _at_least_version(tensorrt_version, "8"):
        return _TF_TENSORRT_HEADERS_V8
    if _at_least_version(tensorrt_version, "6"):