[Unity][Parser] Check well-formedness in the parser (#16569) * Check well-formedness in the parser * Correct packed funcs in NN frontend * Support the check_well_formed optional argument to I.ir_module * Also check well-formedness in TIR * Enable normalization for individual Relax functions and PrimFuncs * Use the error raised by the TIR well-formed checker for the message * Fix tvmscript test failures * Whitespace * Fix errors in verify_well_formed test * Include a more helpful error message * Fix TIR test failures * Address well-formed failures in test_tir_specialize * Correct well-formedness error in test_tir_analysis_oob * Correct further well-formedness failures * Remove __tvm_meta__ from test case to avoid parsing error * Avoid circular import in entryy.py * Formatting fixes * lint fix * Add pylint exceptions * Fix whitespace * Fix more failed test cases * Catch inappropriate use of decl_function instead of segfaulting * Fix test_lower.py * Mark purity in test_relax_2d_buffer_allocation.py * Mark purity in test_dma_builtin.py * Remove __tvm_meta___ from test_tir_usmp_analysis_extract_bufferinfo.py * Suppress well-formed check in test_tir_transform_convert_blocks_to_opaque.py * Remove __tvm_meta__ in test_tir_usmp_algo.py * Remove __tvm_meta__ from more USMP tests * Fix incorrect var in test_tir_transform_storage_flatten.py * Remove all remaining instances of __tvm_meta__ * Fix purity error in test_dataflow_pattern.py * Fix purity error in test_ast_printer * Fix test_arith_domain_touched example * Okay to set check_well_formed to True in test_tir_analysis_identify_mcmcpy * Define variable in test_tir_analysis_oob * Typo fix * Add explanatory comment to test case * Define the undefined vars in test_tir_transform_common_subexpr_elim * Exception no longer necessary in test_tir_transform_inject_rolling_buffer * Remove unnecessary check exemption in test_tir_transform_convert_ssa * Avoid checking exemption in test_inject_ptx_ldg32 * Note special case in test_distributed_transform_propagate_sharding * Exempt well-formed error in dlight/test_benchmark * Exempt well-formedness errors in test_ethosu/, mostly uninitialized vars * Whitespace * Include non-CUDA GPUs in IsScheduledOnGPU * Fix thread binding bug by changing thread binding var dtype * Include overrides in test_runtime_builtin_paged_attention_kv_cache.py * add exemptions in test_ethosu/test_replace_conv2d * Add more ethosu exemptions * More exemptions for ethosu tests * Remove unused reference * Indicate purity in test_transform_rewrite_cuda_graph * Indicate purity in test_transform_normalize * Reorder MergeSharedMemoryAllocations in GPU codegen * Add target parameter for FP8StorageLegalize and FP8ComputeLegalize * Don't re-import Target in tvm/tir/transform/transform.py
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Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends.
TVM is licensed under the Apache-2.0 license.
Check out the TVM Documentation site for installation instructions, tutorials, examples, and more. The Getting Started with TVM tutorial is a great place to start.
TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. Check out the Contributor Guide.
We learned a lot from the following projects when building TVM.