Add flags to configure whether to fallback to the driver for ptx -> SASS.
When the flags are set to false, XLA GPU backend will fail during compilation if there is an error with using ptxas directly. It is preferable to not fallback to the driver, and these flags allow to get a visible error instead of silently falling back to a possibly suboptimal code generation. PiperOrigin-RevId: 309902633 Change-Id: Ib5104fe78ad2768c7ea7176c2f241968b7bdc4e3
This commit is contained in:
Adrian Kuegel
TensorFlower Gardener
parent
7c39403bc7
commit
d37fe1dd40
@ -64,6 +64,9 @@ DebugOptions DefaultDebugOptionsIgnoringFlags() {
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opts.set_xla_force_host_platform_device_count(1);
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opts.set_xla_gpu_deterministic_reductions(false);
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opts.set_xla_cpu_enable_xprof_traceme(true);
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// TODO(b/155295372): disable ptxas fallback by default.
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opts.set_xla_gpu_unsafe_fallback_to_driver_on_ptxas_not_found(true);
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opts.set_xla_gpu_unsafe_fallback_to_driver_on_ptxas_error(true);
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return opts;
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}
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@ -219,340 +222,347 @@ static void AllocateFlags() {
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return true;
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};
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flag_objects = new std::vector<tensorflow::Flag>({
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tensorflow::Flag(
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"xla_cpu_enable_fast_math",
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bool_setter_for(&DebugOptions::set_xla_cpu_enable_fast_math),
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flag_values->xla_cpu_enable_fast_math(),
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"Enable unsafe fast-math optimizations in the CPU compiler; "
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"this may produce faster code at the expense of some accuracy."),
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tensorflow::Flag(
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"xla_cpu_fast_math_honor_nans",
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bool_setter_for(&DebugOptions::set_xla_cpu_fast_math_honor_nans),
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flag_values->xla_cpu_fast_math_honor_nans(),
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"When xla_cpu_enable_fast_math is true then this controls whether we "
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"allow operations to produce NaNs. Ignored when "
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"xla_cpu_enable_fast_math is false."),
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tensorflow::Flag(
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"xla_cpu_fast_math_honor_infs",
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bool_setter_for(&DebugOptions::set_xla_cpu_fast_math_honor_infs),
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flag_values->xla_cpu_fast_math_honor_infs(),
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"When xla_cpu_enable_fast_math is true then this controls whether we "
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"allow operations to produce infinites. Ignored when "
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"xla_cpu_enable_fast_math is false."),
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tensorflow::Flag(
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"xla_cpu_fast_math_honor_division",
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bool_setter_for(&DebugOptions::set_xla_cpu_fast_math_honor_division),
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flag_values->xla_cpu_fast_math_honor_division(),
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"When xla_cpu_enable_fast_math is true then this controls whether "
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"we forbid to use multiplication by the reciprocal instead of "
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"division. Ignored when xla_cpu_enable_fast_math is false."),
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tensorflow::Flag(
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"xla_cpu_fast_math_honor_functions",
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bool_setter_for(&DebugOptions::set_xla_cpu_fast_math_honor_functions),
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flag_values->xla_cpu_fast_math_honor_functions(),
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"When xla_cpu_enable_fast_math is true then this controls whether "
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"we forbid to approximate calculations for functions. Ignored when "
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"xla_cpu_enable_fast_math is false."),
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tensorflow::Flag(
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"xla_gpu_enable_fast_min_max",
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bool_setter_for(&DebugOptions::set_xla_gpu_enable_fast_min_max),
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flag_values->xla_gpu_enable_fast_min_max(),
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"Enable fast floating point min/max lowering that does not propagate "
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"NaNs."),
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tensorflow::Flag(
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"xla_llvm_enable_alias_scope_metadata",
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bool_setter_for(
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&DebugOptions::set_xla_llvm_enable_alias_scope_metadata),
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flag_values->xla_llvm_enable_alias_scope_metadata(),
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"In LLVM-based backends, enable the emission of "
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"!alias.scope metadata in the generated IR."),
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tensorflow::Flag(
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"xla_llvm_enable_noalias_metadata",
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bool_setter_for(&DebugOptions::set_xla_llvm_enable_noalias_metadata),
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flag_values->xla_llvm_enable_noalias_metadata(),
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"In LLVM-based backends, enable the emission of "
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"!noalias metadata in the generated IR."),
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tensorflow::Flag(
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"xla_llvm_enable_invariant_load_metadata",
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bool_setter_for(
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&DebugOptions::set_xla_llvm_enable_invariant_load_metadata),
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flag_values->xla_llvm_enable_invariant_load_metadata(),
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"In LLVM-based backends, enable the emission of "
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"!invariant.load metadata in "
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"the generated IR."),
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tensorflow::Flag(
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"xla_llvm_disable_expensive_passes",
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bool_setter_for(&DebugOptions::set_xla_llvm_disable_expensive_passes),
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flag_values->xla_llvm_disable_expensive_passes(),
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"In LLVM-based backends, disable a custom set of "
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"expensive optimization passes."),
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tensorflow::Flag(
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"xla_backend_optimization_level",
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int32_setter_for(&DebugOptions::set_xla_backend_optimization_level),
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flag_values->xla_backend_optimization_level(),
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"Numerical optimization level for the XLA compiler backend."),
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tensorflow::Flag(
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"xla_disable_hlo_passes", setter_for_xla_disable_hlo_passes, "",
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"Comma-separated list of hlo passes to be disabled. These names "
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"must exactly match the passes' names; no whitespace around "
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"commas."),
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tensorflow::Flag(
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"xla_enable_hlo_passes_only", setter_for_xla_enable_hlo_passes_only,
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"",
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"Comma-separated list of hlo passes to be enabled. These names "
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"must exactly match the passes' names; no whitespace around "
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"commas. The unspecified passes are all disabled."),
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tensorflow::Flag(
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"xla_disable_all_hlo_passes",
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bool_setter_for(&DebugOptions::set_xla_disable_all_hlo_passes), false,
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"Disables all HLO passes. Notes that some passes are necessary for "
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"correctness and the invariants that must be satisfied by 'fully "
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"optimized' HLO are different for different devices and may change "
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"over time. The only 'guarantee', such as it is, is that if you "
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"compile XLA and dump the optimized HLO for some graph, you should "
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"be able to run it again on the same device with the same build of "
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"XLA."),
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tensorflow::Flag(
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"xla_embed_ir_in_executable",
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bool_setter_for(&DebugOptions::set_xla_embed_ir_in_executable),
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flag_values->xla_embed_ir_in_executable(),
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"Embed the compiler IR as a string in the executable."),
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tensorflow::Flag(
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"xla_eliminate_hlo_implicit_broadcast",
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bool_setter_for(
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&DebugOptions::set_xla_eliminate_hlo_implicit_broadcast),
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flag_values->xla_eliminate_hlo_implicit_broadcast(),
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"Eliminate implicit broadcasts when lowering user "
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"computations to HLO instructions; use explicit "
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"broadcast instead."),
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tensorflow::Flag(
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"xla_cpu_multi_thread_eigen",
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bool_setter_for(&DebugOptions::set_xla_cpu_multi_thread_eigen),
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flag_values->xla_cpu_multi_thread_eigen(),
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"When generating calls to Eigen in the CPU backend, "
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"use multi-threaded Eigen mode."),
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tensorflow::Flag("xla_gpu_cuda_data_dir",
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flag_values->mutable_xla_gpu_cuda_data_dir(),
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"If non-empty, specifies a local directory containing "
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"ptxas and nvvm libdevice files; otherwise we use "
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"those from runfile directories."),
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tensorflow::Flag("xla_gpu_ftz",
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bool_setter_for(&DebugOptions::set_xla_gpu_ftz),
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flag_values->xla_gpu_ftz(),
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"If true, flush-to-zero semantics are enabled in the "
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"code generated for GPUs."),
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tensorflow::Flag(
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"xla_gpu_disable_multi_streaming",
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bool_setter_for(&DebugOptions::set_xla_gpu_disable_multi_streaming),
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flag_values->xla_gpu_disable_multi_streaming(),
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"If true, multi-streaming in the GPU backend is disabled."),
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tensorflow::Flag(
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"xla_gpu_max_kernel_unroll_factor",
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int32_setter_for(&DebugOptions::set_xla_gpu_max_kernel_unroll_factor),
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flag_values->xla_gpu_max_kernel_unroll_factor(),
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"Specify the maximum kernel unroll factor for the GPU backend."),
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tensorflow::Flag("xla_gpu_ptx_file", setter_for_xla_gpu_ptx_file, "",
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"If non-empty, specifies a file containing ptx to use. "
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"The filename prefix must have the same pattern as PTX "
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"dumped by XLA. This allows to match one specific "
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"module. General workflow. Get the generated module "
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"ptx from XLA. Modify it. Then pass it back via this "
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"option."),
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tensorflow::Flag(
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"xla_test_all_output_layouts",
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bool_setter_for(&DebugOptions::set_xla_test_all_output_layouts),
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flag_values->xla_test_all_output_layouts(),
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"Let ClientLibraryTestBase::ComputeAndCompare* test "
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"all permutations of output layouts. For example, with "
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"a 3D shape, all permutations of the set {0, 1, 2} are "
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"tried."),
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tensorflow::Flag(
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"xla_test_all_input_layouts",
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bool_setter_for(&DebugOptions::set_xla_test_all_input_layouts),
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flag_values->xla_test_all_input_layouts(),
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"Let ClientLibraryTestBase::ComputeAndCompare* test "
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"all permutations of *input* layouts. For example, for "
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"2 input arguments with 2D shape and 4D shape, the "
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"computation will run 2! * 4! times for every possible "
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"layouts"),
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tensorflow::Flag(
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"xla_hlo_profile",
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bool_setter_for(&DebugOptions::set_xla_hlo_profile),
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flag_values->xla_hlo_profile(),
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"Instrument the computation to collect per-HLO cycle counts"),
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tensorflow::Flag("xla_backend_extra_options",
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setter_for_xla_backend_extra_options, "",
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"Extra options to pass to a backend; "
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"comma-separated list of 'key=val' strings (=val "
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"may be omitted); no whitespace around commas."),
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tensorflow::Flag(
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"xla_gpu_use_cudnn_batchnorm",
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bool_setter_for(&DebugOptions::set_xla_gpu_use_cudnn_batchnorm),
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flag_values->xla_gpu_use_cudnn_batchnorm(),
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"Allows the GPU backend to implement batchnorm HLOs using cudnn, "
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"rather than expanding them to a soup of HLOs."),
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flag_objects = new std::vector<tensorflow::Flag>();
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flag_objects->reserve(55);
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// Don't use an initializer list for initializing the vector; this would
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// create a temporary copy, and exceeds the stack space when compiling with
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// certain configurations.
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flag_objects->push_back(tensorflow::Flag(
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"xla_cpu_enable_fast_math",
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bool_setter_for(&DebugOptions::set_xla_cpu_enable_fast_math),
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flag_values->xla_cpu_enable_fast_math(),
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"Enable unsafe fast-math optimizations in the CPU compiler; this may "
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"produce faster code at the expense of some accuracy."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_cpu_fast_math_honor_nans",
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bool_setter_for(&DebugOptions::set_xla_cpu_fast_math_honor_nans),
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flag_values->xla_cpu_fast_math_honor_nans(),
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"When xla_cpu_enable_fast_math is true then this controls whether we "
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"allow operations to produce NaNs. Ignored when "
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"xla_cpu_enable_fast_math is false."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_cpu_fast_math_honor_infs",
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bool_setter_for(&DebugOptions::set_xla_cpu_fast_math_honor_infs),
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flag_values->xla_cpu_fast_math_honor_infs(),
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"When xla_cpu_enable_fast_math is true then this controls whether we "
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"allow operations to produce infinites. Ignored when "
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"xla_cpu_enable_fast_math is false."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_cpu_fast_math_honor_division",
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bool_setter_for(&DebugOptions::set_xla_cpu_fast_math_honor_division),
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flag_values->xla_cpu_fast_math_honor_division(),
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"When xla_cpu_enable_fast_math is true then this controls whether we "
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"forbid to use multiplication by the reciprocal instead of division. "
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"Ignored when xla_cpu_enable_fast_math is false."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_cpu_fast_math_honor_functions",
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bool_setter_for(&DebugOptions::set_xla_cpu_fast_math_honor_functions),
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flag_values->xla_cpu_fast_math_honor_functions(),
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"When xla_cpu_enable_fast_math is true then this controls whether we "
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"forbid to approximate calculations for functions. Ignored when "
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"xla_cpu_enable_fast_math is false."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_gpu_enable_fast_min_max",
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bool_setter_for(&DebugOptions::set_xla_gpu_enable_fast_min_max),
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flag_values->xla_gpu_enable_fast_min_max(),
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"Enable fast floating point min/max lowering that does not propagate "
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"NaNs."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_llvm_enable_alias_scope_metadata",
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bool_setter_for(&DebugOptions::set_xla_llvm_enable_alias_scope_metadata),
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flag_values->xla_llvm_enable_alias_scope_metadata(),
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"In LLVM-based backends, enable the emission of !alias.scope metadata in "
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"the generated IR."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_llvm_enable_noalias_metadata",
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bool_setter_for(&DebugOptions::set_xla_llvm_enable_noalias_metadata),
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flag_values->xla_llvm_enable_noalias_metadata(),
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"In LLVM-based backends, enable the emission of !noalias metadata in the "
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"generated IR."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_llvm_enable_invariant_load_metadata",
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bool_setter_for(
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&DebugOptions::set_xla_llvm_enable_invariant_load_metadata),
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flag_values->xla_llvm_enable_invariant_load_metadata(),
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"In LLVM-based backends, enable the emission of !invariant.load metadata "
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"in the generated IR."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_llvm_disable_expensive_passes",
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bool_setter_for(&DebugOptions::set_xla_llvm_disable_expensive_passes),
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flag_values->xla_llvm_disable_expensive_passes(),
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"In LLVM-based backends, disable a custom set of expensive optimization "
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"passes."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_backend_optimization_level",
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int32_setter_for(&DebugOptions::set_xla_backend_optimization_level),
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flag_values->xla_backend_optimization_level(),
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"Numerical optimization level for the XLA compiler backend."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_disable_hlo_passes", setter_for_xla_disable_hlo_passes, "",
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"Comma-separated list of hlo passes to be disabled. These names must "
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"exactly match the passes' names; no whitespace around commas."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_enable_hlo_passes_only", setter_for_xla_enable_hlo_passes_only, "",
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"Comma-separated list of hlo passes to be enabled. These names must "
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"exactly match the passes' names; no whitespace around commas. The "
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"unspecified passes are all disabled."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_disable_all_hlo_passes",
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bool_setter_for(&DebugOptions::set_xla_disable_all_hlo_passes), false,
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"Disables all HLO passes. Notes that some passes are necessary for "
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"correctness and the invariants that must be satisfied by 'fully "
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"optimized' HLO are different for different devices and may change "
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"over time. The only 'guarantee', such as it is, is that if you compile "
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"XLA and dump the optimized HLO for some graph, you should be able to "
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"run it again on the same device with the same build of XLA."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_embed_ir_in_executable",
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bool_setter_for(&DebugOptions::set_xla_embed_ir_in_executable),
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flag_values->xla_embed_ir_in_executable(),
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"Embed the compiler IR as a string in the executable."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_eliminate_hlo_implicit_broadcast",
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bool_setter_for(&DebugOptions::set_xla_eliminate_hlo_implicit_broadcast),
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flag_values->xla_eliminate_hlo_implicit_broadcast(),
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"Eliminate implicit broadcasts when lowering user computations to HLO "
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"instructions; use explicit broadcast instead."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_cpu_multi_thread_eigen",
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bool_setter_for(&DebugOptions::set_xla_cpu_multi_thread_eigen),
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flag_values->xla_cpu_multi_thread_eigen(),
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"When generating calls to Eigen in the CPU backend, use multi-threaded "
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"Eigen mode."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_gpu_cuda_data_dir", flag_values->mutable_xla_gpu_cuda_data_dir(),
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"If non-empty, specifies a local directory containing ptxas and nvvm "
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"libdevice files; otherwise we use those from runfile directories."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_gpu_ftz", bool_setter_for(&DebugOptions::set_xla_gpu_ftz),
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flag_values->xla_gpu_ftz(),
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"If true, flush-to-zero semantics are enabled in the code generated for "
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"GPUs."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_gpu_disable_multi_streaming",
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bool_setter_for(&DebugOptions::set_xla_gpu_disable_multi_streaming),
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flag_values->xla_gpu_disable_multi_streaming(),
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"If true, multi-streaming in the GPU backend is disabled."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_gpu_max_kernel_unroll_factor",
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int32_setter_for(&DebugOptions::set_xla_gpu_max_kernel_unroll_factor),
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flag_values->xla_gpu_max_kernel_unroll_factor(),
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"Specify the maximum kernel unroll factor for the GPU backend."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_gpu_ptx_file", setter_for_xla_gpu_ptx_file, "",
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"If non-empty, specifies a file containing ptx to use. The filename "
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"prefix must have the same pattern as PTX dumped by XLA. This allows to "
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"match one specific module. General workflow. Get the generated module "
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"ptx from XLA. Modify it. Then pass it back via this option."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_test_all_output_layouts",
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bool_setter_for(&DebugOptions::set_xla_test_all_output_layouts),
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flag_values->xla_test_all_output_layouts(),
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"Let ClientLibraryTestBase::ComputeAndCompare* test all permutations of "
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"output layouts. For example, with a 3D shape, all permutations of the "
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"set {0, 1, 2} are tried."));
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flag_objects->push_back(tensorflow::Flag(
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"xla_test_all_input_layouts",
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bool_setter_for(&DebugOptions::set_xla_test_all_input_layouts),
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flag_values->xla_test_all_input_layouts(),
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"Let ClientLibraryTestBase::ComputeAndCompare* test all permutations of "
|
||||
"*input* layouts. For example, for 2 input arguments with 2D shape and "
|
||||
"4D shape, the computation will run 2! * 4! times for every possible "
|
||||
"layouts"));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_hlo_profile", bool_setter_for(&DebugOptions::set_xla_hlo_profile),
|
||||
flag_values->xla_hlo_profile(),
|
||||
"Instrument the computation to collect per-HLO cycle counts"));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_backend_extra_options", setter_for_xla_backend_extra_options, "",
|
||||
"Extra options to pass to a backend; comma-separated list of 'key=val' "
|
||||
"strings (=val may be omitted); no whitespace around commas."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_gpu_use_cudnn_batchnorm",
|
||||
bool_setter_for(&DebugOptions::set_xla_gpu_use_cudnn_batchnorm),
|
||||
flag_values->xla_gpu_use_cudnn_batchnorm(),
|
||||
"Allows the GPU backend to implement batchnorm HLOs using cudnn, rather "
|
||||
"than expanding them to a soup of HLOs."));
|
||||
flag_objects->push_back(
|
||||
tensorflow::Flag("xla_cpu_use_mkl_dnn",
|
||||
bool_setter_for(&DebugOptions::set_xla_cpu_use_mkl_dnn),
|
||||
flag_values->xla_cpu_use_mkl_dnn(),
|
||||
"Generate calls to MKL-DNN in the CPU backend."),
|
||||
tensorflow::Flag(
|
||||
"xla_gpu_crash_on_verification_failures",
|
||||
bool_setter_for(
|
||||
&DebugOptions::set_xla_gpu_crash_on_verification_failures),
|
||||
flag_values->xla_gpu_crash_on_verification_failures(),
|
||||
"Crashes the program on extra verification failures, e.g. cuDNN "
|
||||
"cross checking failures"),
|
||||
tensorflow::Flag(
|
||||
"xla_gpu_autotune_level",
|
||||
int32_setter_for(&DebugOptions::set_xla_gpu_autotune_level),
|
||||
flag_values->xla_gpu_autotune_level(),
|
||||
"Set GEMM and Convolution auto-tuning level."
|
||||
"0 = off; 1 = on; 2 = on+init; 3 = on+init+reinit; 4 = "
|
||||
"on+init+reinit+check."),
|
||||
tensorflow::Flag(
|
||||
"xla_force_host_platform_device_count",
|
||||
int32_setter_for(
|
||||
&DebugOptions::set_xla_force_host_platform_device_count),
|
||||
flag_values->xla_force_host_platform_device_count(),
|
||||
"Force the host platform to pretend that there are these many "
|
||||
"host \"devices\". All of these host devices are backed by the same"
|
||||
"threadpool. Setting this to anything other than 1 can increase "
|
||||
"overhead from context switching but we let the user override this "
|
||||
"behavior to help run tests on the host that run models in parallel "
|
||||
"across multiple devices."),
|
||||
tensorflow::Flag(
|
||||
"xla_gpu_disable_gpuasm_optimizations",
|
||||
bool_setter_for(
|
||||
&DebugOptions::set_xla_gpu_disable_gpuasm_optimizations),
|
||||
flag_values->xla_gpu_disable_gpuasm_optimizations(),
|
||||
"In XLA:GPU run ptxas in -O0 (default is -O3)."),
|
||||
tensorflow::Flag(
|
||||
"xla_fuel", setter_for_xla_fuel, /*default_value_for_display=*/"",
|
||||
"Sets compiler fuel, useful for bisecting bugs in passes. Format "
|
||||
"--xla_fuel=PASS1=NUM1,PASS2=NUM2,..."),
|
||||
|
||||
tensorflow::Flag(
|
||||
"xla_dump_to", string_setter_for(&DebugOptions::set_xla_dump_to),
|
||||
flag_values->xla_dump_to(),
|
||||
"Directory into which debugging data is written. If not specified "
|
||||
"but another dumping flag is passed, data will be written to stdout. "
|
||||
" To explicitly write to stdout, set this to \"-\". The values "
|
||||
"\"sponge\" and \"test_undeclared_outputs_dir\" have a special "
|
||||
"meaning: They cause us to dump into the directory specified by the "
|
||||
"environment variable TEST_UNDECLARED_OUTPUTS_DIR."),
|
||||
tensorflow::Flag(
|
||||
"xla_dump_hlo_as_text",
|
||||
bool_setter_for(&DebugOptions::set_xla_dump_hlo_as_text),
|
||||
flag_values->xla_dump_hlo_as_text(),
|
||||
"Dumps HLO modules as text before and after optimizations. Results "
|
||||
"are written to the --xla_dump_to dir, or, if no dir is specified, "
|
||||
"to stdout."),
|
||||
tensorflow::Flag(
|
||||
"xla_dump_hlo_as_proto",
|
||||
bool_setter_for(&DebugOptions::set_xla_dump_hlo_as_proto),
|
||||
flag_values->xla_dump_hlo_as_proto(),
|
||||
"Dumps HLO modules as HloProtos to the directory specified by "
|
||||
"--xla_dump_to."),
|
||||
tensorflow::Flag(
|
||||
"xla_dump_hlo_as_dot",
|
||||
bool_setter_for(&DebugOptions::set_xla_dump_hlo_as_dot),
|
||||
flag_values->xla_dump_hlo_as_dot(),
|
||||
"Dumps HLO modules rendered as dot files to the directory "
|
||||
"specified by --xla_dump_to."),
|
||||
"Generate calls to MKL-DNN in the CPU backend."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_gpu_crash_on_verification_failures",
|
||||
bool_setter_for(
|
||||
&DebugOptions::set_xla_gpu_crash_on_verification_failures),
|
||||
flag_values->xla_gpu_crash_on_verification_failures(),
|
||||
"Crashes the program on extra verification failures, e.g. cuDNN cross "
|
||||
"checking failures"));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_gpu_autotune_level",
|
||||
int32_setter_for(&DebugOptions::set_xla_gpu_autotune_level),
|
||||
flag_values->xla_gpu_autotune_level(),
|
||||
"Set GEMM and Convolution auto-tuning level. 0 = off; 1 = on; 2 = "
|
||||
"on+init; 3 = on+init+reinit; 4 = on+init+reinit+check."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_force_host_platform_device_count",
|
||||
int32_setter_for(&DebugOptions::set_xla_force_host_platform_device_count),
|
||||
flag_values->xla_force_host_platform_device_count(),
|
||||
"Force the host platform to pretend that there are these many host "
|
||||
"\"devices\". All of these host devices are backed by the same "
|
||||
"threadpool. Setting this to anything other than 1 can increase overhead "
|
||||
"from context switching but we let the user override this behavior to "
|
||||
"help run tests on the host that run models in parallel across multiple "
|
||||
"devices."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_gpu_disable_gpuasm_optimizations",
|
||||
bool_setter_for(&DebugOptions::set_xla_gpu_disable_gpuasm_optimizations),
|
||||
flag_values->xla_gpu_disable_gpuasm_optimizations(),
|
||||
"In XLA:GPU run ptxas in -O0 (default is -O3)."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_fuel", setter_for_xla_fuel, /*default_value_for_display=*/"",
|
||||
"Sets compiler fuel, useful for bisecting bugs in passes. Format "
|
||||
"--xla_fuel=PASS1=NUM1,PASS2=NUM2,..."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_dump_to", string_setter_for(&DebugOptions::set_xla_dump_to),
|
||||
flag_values->xla_dump_to(),
|
||||
"Directory into which debugging data is written. If not specified but "
|
||||
"another dumping flag is passed, data will be written to stdout. To "
|
||||
"explicitly write to stdout, set this to \"-\". The values \"sponge\" "
|
||||
"and \"test_undeclared_outputs_dir\" have a special meaning: They cause "
|
||||
"us to dump into the directory specified by the environment variable "
|
||||
"TEST_UNDECLARED_OUTPUTS_DIR."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_dump_hlo_as_text",
|
||||
bool_setter_for(&DebugOptions::set_xla_dump_hlo_as_text),
|
||||
flag_values->xla_dump_hlo_as_text(),
|
||||
"Dumps HLO modules as text before and after optimizations. Results are "
|
||||
"written to the --xla_dump_to dir, or, if no dir is specified, to "
|
||||
"stdout."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_dump_hlo_as_proto",
|
||||
bool_setter_for(&DebugOptions::set_xla_dump_hlo_as_proto),
|
||||
flag_values->xla_dump_hlo_as_proto(),
|
||||
"Dumps HLO modules as HloProtos to the directory specified by "
|
||||
"--xla_dump_to."));
|
||||
flag_objects->push_back(
|
||||
tensorflow::Flag("xla_dump_hlo_as_dot",
|
||||
bool_setter_for(&DebugOptions::set_xla_dump_hlo_as_dot),
|
||||
flag_values->xla_dump_hlo_as_dot(),
|
||||
"Dumps HLO modules rendered as dot files to the "
|
||||
"directory specified by --xla_dump_to."));
|
||||
flag_objects->push_back(
|
||||
tensorflow::Flag("xla_dump_hlo_as_html",
|
||||
bool_setter_for(&DebugOptions::set_xla_dump_hlo_as_html),
|
||||
flag_values->xla_dump_hlo_as_html(),
|
||||
"Dumps HLO modules rendered as HTML files to the "
|
||||
"directory specified by --xla_dump_to."),
|
||||
tensorflow::Flag(
|
||||
"xla_dump_hlo_as_url",
|
||||
bool_setter_for(&DebugOptions::set_xla_dump_hlo_as_url),
|
||||
flag_values->xla_dump_hlo_as_url(),
|
||||
"Tries to dump HLO modules rendered as URLs to stdout (and also to "
|
||||
"the directory specified by --xla_dump_to). This is not implemented "
|
||||
"by default; you need to add a plugin which calls "
|
||||
"RegisterGraphToURLRenderer()."),
|
||||
tensorflow::Flag(
|
||||
"xla_dump_hlo_snapshots",
|
||||
bool_setter_for(&DebugOptions::set_xla_dump_hlo_snapshots),
|
||||
flag_values->xla_dump_hlo_snapshots(),
|
||||
"Every time an HLO module is run, dumps an HloSnapshot to the "
|
||||
"directory specified by --xla_dump_to."),
|
||||
tensorflow::Flag(
|
||||
"xla_dump_hlo_module_re",
|
||||
string_setter_for(&DebugOptions::set_xla_dump_hlo_module_re),
|
||||
flag_values->xla_dump_hlo_module_re(),
|
||||
"Limits dumping only to modules which match this regular expression. "
|
||||
" Default is to dump all modules."),
|
||||
tensorflow::Flag(
|
||||
"xla_dump_hlo_pass_re",
|
||||
string_setter_for(&DebugOptions::set_xla_dump_hlo_pass_re),
|
||||
flag_values->xla_dump_hlo_pass_re(),
|
||||
"If specified, dumps HLO before and after optimization passes which "
|
||||
"match this regular expression, in addition to dumping at the very "
|
||||
"beginning and end of compilation."),
|
||||
tensorflow::Flag(
|
||||
"xla_dump_include_timestamp",
|
||||
bool_setter_for(&DebugOptions::set_xla_dump_include_timestamp),
|
||||
flag_values->xla_dump_include_timestamp(),
|
||||
"If specified, includes a timestamp in the dumped filenames."),
|
||||
tensorflow::Flag(
|
||||
"xla_dump_max_hlo_modules",
|
||||
int32_setter_for(&DebugOptions::set_xla_dump_max_hlo_modules),
|
||||
flag_values->xla_dump_max_hlo_modules(),
|
||||
"Max number of hlo module dumps in a directory. Set to < 0 for "
|
||||
"unbounded."),
|
||||
tensorflow::Flag(
|
||||
"xla_hlo_graph_addresses",
|
||||
bool_setter_for(&DebugOptions::set_xla_hlo_graph_addresses),
|
||||
flag_values->xla_hlo_graph_addresses(),
|
||||
"When rendering graphs (--xla_dump_hlo_as_{dot,html,url}), displays "
|
||||
"the address in memory of each HloInstruction object."),
|
||||
tensorflow::Flag(
|
||||
"xla_hlo_graph_sharding_color",
|
||||
bool_setter_for(&DebugOptions::set_xla_hlo_graph_sharding_color),
|
||||
flag_values->xla_hlo_graph_sharding_color(),
|
||||
"Assign colors based on sharding assignments when generating the "
|
||||
"HLO graphs."),
|
||||
tensorflow::Flag(
|
||||
"xla_allow_excess_precision",
|
||||
bool_setter_for(&DebugOptions::set_xla_allow_excess_precision),
|
||||
flag_values->xla_allow_excess_precision(),
|
||||
"Allow xla to increase the output precision of an instruction."),
|
||||
tensorflow::Flag(
|
||||
"xla_gpu_force_conv_nchw",
|
||||
bool_setter_for(&DebugOptions::set_xla_gpu_force_conv_nchw),
|
||||
flag_values->xla_gpu_force_conv_nchw(),
|
||||
"For cuDNN convolutions, always NCHW layouts."),
|
||||
tensorflow::Flag("xla_gpu_algorithm_blacklist_path",
|
||||
string_setter_for(
|
||||
&DebugOptions::set_xla_gpu_algorithm_blacklist_path),
|
||||
flag_values->xla_gpu_algorithm_blacklist_path(),
|
||||
"An AlgorithmBlacklist text proto file as a blacklist "
|
||||
"of convolutions to avoid to use."),
|
||||
tensorflow::Flag(
|
||||
"xla_gpu_deterministic_reductions",
|
||||
bool_setter_for(&DebugOptions::set_xla_gpu_deterministic_reductions),
|
||||
flag_values->xla_gpu_deterministic_reductions(),
|
||||
"Always run deterministic reductions on GPU"),
|
||||
tensorflow::Flag(
|
||||
"xla_tpu_detect_nan",
|
||||
bool_setter_for(&DebugOptions::set_xla_tpu_detect_nan),
|
||||
flag_values->xla_tpu_detect_nan(),
|
||||
"Trigger error on execution on TPU if a NAN value is detected"),
|
||||
tensorflow::Flag(
|
||||
"xla_tpu_detect_inf",
|
||||
bool_setter_for(&DebugOptions::set_xla_tpu_detect_inf),
|
||||
flag_values->xla_tpu_detect_inf(),
|
||||
"Trigger error on execution on TPU if a INF value is detected"),
|
||||
tensorflow::Flag(
|
||||
"xla_cpu_enable_xprof_traceme",
|
||||
bool_setter_for(&DebugOptions::set_xla_cpu_enable_xprof_traceme),
|
||||
flag_values->xla_cpu_enable_xprof_traceme(),
|
||||
"If true, XLA CPU generates code to call "
|
||||
"TraceMe::Activity{Start|End} around HLO operations."),
|
||||
});
|
||||
"directory specified by --xla_dump_to."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_dump_hlo_as_url",
|
||||
bool_setter_for(&DebugOptions::set_xla_dump_hlo_as_url),
|
||||
flag_values->xla_dump_hlo_as_url(),
|
||||
"Tries to dump HLO modules rendered as URLs to stdout (and also to the "
|
||||
"directory specified by --xla_dump_to). This is not implemented by "
|
||||
"default; you need to add a plugin which calls "
|
||||
"RegisterGraphToURLRenderer()."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_dump_hlo_snapshots",
|
||||
bool_setter_for(&DebugOptions::set_xla_dump_hlo_snapshots),
|
||||
flag_values->xla_dump_hlo_snapshots(),
|
||||
"Every time an HLO module is run, dumps an HloSnapshot to the directory "
|
||||
"specified by --xla_dump_to."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_dump_hlo_module_re",
|
||||
string_setter_for(&DebugOptions::set_xla_dump_hlo_module_re),
|
||||
flag_values->xla_dump_hlo_module_re(),
|
||||
"Limits dumping only to modules which match this regular expression. "
|
||||
"Default is to dump all modules."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_dump_hlo_pass_re",
|
||||
string_setter_for(&DebugOptions::set_xla_dump_hlo_pass_re),
|
||||
flag_values->xla_dump_hlo_pass_re(),
|
||||
"If specified, dumps HLO before and after optimization passes which "
|
||||
"match this regular expression, in addition to dumping at the very "
|
||||
"beginning and end of compilation."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_dump_include_timestamp",
|
||||
bool_setter_for(&DebugOptions::set_xla_dump_include_timestamp),
|
||||
flag_values->xla_dump_include_timestamp(),
|
||||
"If specified, includes a timestamp in the dumped filenames."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_dump_max_hlo_modules",
|
||||
int32_setter_for(&DebugOptions::set_xla_dump_max_hlo_modules),
|
||||
flag_values->xla_dump_max_hlo_modules(),
|
||||
"Max number of hlo module dumps in a directory. Set to < 0 for "
|
||||
"unbounded."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_hlo_graph_addresses",
|
||||
bool_setter_for(&DebugOptions::set_xla_hlo_graph_addresses),
|
||||
flag_values->xla_hlo_graph_addresses(),
|
||||
"When rendering graphs (--xla_dump_hlo_as_{dot,html,url}), displays "
|
||||
"the address in memory of each HloInstruction object."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_hlo_graph_sharding_color",
|
||||
bool_setter_for(&DebugOptions::set_xla_hlo_graph_sharding_color),
|
||||
flag_values->xla_hlo_graph_sharding_color(),
|
||||
"Assign colors based on sharding assignments when generating the HLO "
|
||||
"graphs."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_allow_excess_precision",
|
||||
bool_setter_for(&DebugOptions::set_xla_allow_excess_precision),
|
||||
flag_values->xla_allow_excess_precision(),
|
||||
"Allow xla to increase the output precision of an instruction."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_gpu_force_conv_nchw",
|
||||
bool_setter_for(&DebugOptions::set_xla_gpu_force_conv_nchw),
|
||||
flag_values->xla_gpu_force_conv_nchw(),
|
||||
"For cuDNN convolutions, always NCHW layouts."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_gpu_algorithm_blacklist_path",
|
||||
string_setter_for(&DebugOptions::set_xla_gpu_algorithm_blacklist_path),
|
||||
flag_values->xla_gpu_algorithm_blacklist_path(),
|
||||
"An AlgorithmBlacklist text proto file as a blacklist of convolutions to "
|
||||
"avoid to use."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_gpu_deterministic_reductions",
|
||||
bool_setter_for(&DebugOptions::set_xla_gpu_deterministic_reductions),
|
||||
flag_values->xla_gpu_deterministic_reductions(),
|
||||
"Always run deterministic reductions on GPU"));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_tpu_detect_nan",
|
||||
bool_setter_for(&DebugOptions::set_xla_tpu_detect_nan),
|
||||
flag_values->xla_tpu_detect_nan(),
|
||||
"Trigger error on execution on TPU if a NAN value is detected"));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_tpu_detect_inf",
|
||||
bool_setter_for(&DebugOptions::set_xla_tpu_detect_inf),
|
||||
flag_values->xla_tpu_detect_inf(),
|
||||
"Trigger error on execution on TPU if a INF value is detected"));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_cpu_enable_xprof_traceme",
|
||||
bool_setter_for(&DebugOptions::set_xla_cpu_enable_xprof_traceme),
|
||||
flag_values->xla_cpu_enable_xprof_traceme(),
|
||||
"If true, XLA CPU generates code to call "
|
||||
"TraceMe::Activity{Start|End} around HLO operations."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_gpu_unsafe_fallback_to_driver_on_ptxas_not_found",
|
||||
bool_setter_for(
|
||||
&DebugOptions::
|
||||
set_xla_gpu_unsafe_fallback_to_driver_on_ptxas_not_found),
|
||||
flag_values->xla_gpu_unsafe_fallback_to_driver_on_ptxas_not_found(),
|
||||
"If true, XLA GPU falls back to the driver if ptxas is not found. Note "
|
||||
"that falling back to the driver can have drawbacks like using more "
|
||||
"memory and/or other bugs during compilation, so we recommend setting "
|
||||
"this flag to false."));
|
||||
flag_objects->push_back(tensorflow::Flag(
|
||||
"xla_gpu_unsafe_fallback_to_driver_on_ptxas_error",
|
||||
bool_setter_for(
|
||||
&DebugOptions::set_xla_gpu_unsafe_fallback_to_driver_on_ptxas_error),
|
||||
flag_values->xla_gpu_unsafe_fallback_to_driver_on_ptxas_error(),
|
||||
"If true, XLA GPU falls back to the driver if there is an error when "
|
||||
"running ptxas. Note that falling back to the driver can have drawbacks "
|
||||
"like using more memory and/or other bugs during compilation, so we "
|
||||
"recommend setting this flag to false."));
|
||||
ParseFlagsFromEnvAndDieIfUnknown("XLA_FLAGS", *flag_objects);
|
||||
}
|
||||
|
||||
|
||||
@ -402,10 +402,25 @@ std::vector<uint8> NVPTXCompiler::CompileGpuAsmOrGetCachedResult(
|
||||
"using $PATH.",
|
||||
hlo_module_config);
|
||||
}
|
||||
CHECK(hlo_module_config.debug_options()
|
||||
.xla_gpu_unsafe_fallback_to_driver_on_ptxas_not_found())
|
||||
<< "There was an error when trying to compile ptx into sass "
|
||||
"code. If you want to try falling back to the GPU driver to "
|
||||
"jit compile ptx, you can use the flag "
|
||||
"--xla_gpu_unsafe_fallback_to_driver_on_ptxas_not_found."
|
||||
" Use at your own risk though, it has known drawbacks like "
|
||||
"increased memory consumption.";
|
||||
} else {
|
||||
LOG(ERROR) << "Error during compilation of ptx to sass: "
|
||||
<< maybe_cubin.status()
|
||||
<< ". Falling back to the GPU driver.";
|
||||
<< maybe_cubin.status();
|
||||
CHECK(hlo_module_config.debug_options()
|
||||
.xla_gpu_unsafe_fallback_to_driver_on_ptxas_error())
|
||||
<< "There was an error when trying to compile ptx into sass "
|
||||
"code. If you want to try falling back to the GPU driver to "
|
||||
"jit compile ptx, you can use the flag "
|
||||
"--xla_gpu_unsafe_fallback_to_driver_on_ptxas_error."
|
||||
" Use at your own risk though, it has known drawbacks like "
|
||||
"increased memory consumption.";
|
||||
}
|
||||
|
||||
// We're going to use the driver to JIT our PTX->SASS, so warn if
|
||||
|
||||
@ -272,7 +272,15 @@ message DebugOptions {
|
||||
// True if TraceMe annotations are enabled for XLA:CPU.
|
||||
bool xla_cpu_enable_xprof_traceme = 137;
|
||||
|
||||
// Next id: 138
|
||||
// It is usually preferable to not fallback to the driver; it can consume more
|
||||
// memory, or have bugs.
|
||||
bool xla_gpu_unsafe_fallback_to_driver_on_ptxas_not_found = 138;
|
||||
|
||||
// It is usually preferable to not fallback to the driver; it can consume more
|
||||
// memory, or have bugs.
|
||||
bool xla_gpu_unsafe_fallback_to_driver_on_ptxas_error = 139;
|
||||
|
||||
// Next id: 140
|
||||
|
||||
// Extra options to pass to the compilation backend (e.g. LLVM); specific
|
||||
// interpretation of these values is left to the backend.
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user