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This pull request adds support for the `bfloat16` (bf16) data type to the test executor, including both Python and CUDA components. The changes ensure that `bfloat16` is handled consistently across argument parsing, data type conversion, and test kernel implementations. Additionally, the CUDA verification kernels are refactored to use parameterized tolerances for improved numerical accuracy checks. **Support for bfloat16 data type:** * Added handling for `bfloat16`/`bf16` in the Python test executor's argument parsing, data type conversion (`parse_dtype`, `dtype_to_mscclpp_dtype`), and help text. [[1]](diffhunk://#diff-e643968a8622d1603868a8ecf4b2fcd8108be1e404a3420bb7e2a6d51dc23fdcR27-R28) [[2]](diffhunk://#diff-e643968a8622d1603868a8ecf4b2fcd8108be1e404a3420bb7e2a6d51dc23fdcL122-R135) [[3]](diffhunk://#diff-e643968a8622d1603868a8ecf4b2fcd8108be1e404a3420bb7e2a6d51dc23fdcL246-R251) * Updated output to display the correct data type string for `bfloat16`. **CUDA kernel and test improvements:** * Included `bfloat16` headers and defined test data fill and gather kernels for `bfloat16` on both CUDA and HIP platforms. [[1]](diffhunk://#diff-e18b8becff1c3b234733f5ca3250a76ffdc5edddb302c2da098b64b00ba7cf88R8-R11) [[2]](diffhunk://#diff-e18b8becff1c3b234733f5ca3250a76ffdc5edddb302c2da098b64b00ba7cf88R35) [[3]](diffhunk://#diff-e18b8becff1c3b234733f5ca3250a76ffdc5edddb302c2da098b64b00ba7cf88R54-R59) [[4]](diffhunk://#diff-e18b8becff1c3b234733f5ca3250a76ffdc5edddb302c2da098b64b00ba7cf88R133) * Refactored verification kernels (`ALL_REDUCE`, `REDUCE_SCATTER`) to use an explicit tolerance parameter (`Eps`) and added correct tolerances for each data type, including `bfloat16`. [[1]](diffhunk://#diff-e18b8becff1c3b234733f5ca3250a76ffdc5edddb302c2da098b64b00ba7cf88L69-R85) [[2]](diffhunk://#diff-e18b8becff1c3b234733f5ca3250a76ffdc5edddb302c2da098b64b00ba7cf88L94-R113) These changes ensure full support for `bfloat16` in the test executor and improve the accuracy and maintainability of the CUDA test kernels. --------- Co-authored-by: Caio Rocha <caiorocha@microsof.com>
136 lines
9.7 KiB
Plaintext
136 lines
9.7 KiB
Plaintext
// Copyright (c) Microsoft Corporation.
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// Licensed under the MIT license.
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#include <assert.h>
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#if defined(__HIP_PLATFORM_AMD__)
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#include <hip/hip_bfloat16.h>
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#include <hip/hip_fp16.h>
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#else
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#include <cuda_bf16.h>
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#include <cuda_fp16.h>
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#endif
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// Numerical Recipes ranqd1, Chapter 7.1, §An Even Quicker Generator, Eq. 7.1.6
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// parameters from Knuth and H. W. Lewis
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static __device__ unsigned int ranqd1(unsigned int seed) {
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const unsigned int a = 1664525;
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const unsigned int c = 1013904223;
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return a * seed + c;
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}
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// fill/test kernel pairs must have the same thread block size to
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// match their random number series.
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#define FILL_DATA(FuncNameType, DataType) \
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extern "C" __global__ void __launch_bounds__(1024, 1) \
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fill_data_##FuncNameType(DataType* input_buf, size_t num_elems, int rank, int seq) { \
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unsigned int seed = (unsigned int)(blockIdx.x * blockDim.x + threadIdx.x + rank + seq); \
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for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_elems; i += blockDim.x * gridDim.x) { \
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seed = ranqd1(seed); \
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input_buf[i] = DataType(seed % blockDim.x) / DataType(blockDim.x); \
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} \
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}
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FILL_DATA(bfloat16, __nv_bfloat16)
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FILL_DATA(float16, __half)
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FILL_DATA(float32, float)
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FILL_DATA(int32, int)
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#define TEST_DATA_ALL_GATHER(FuncNameType, DataType) \
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extern "C" __global__ void __launch_bounds__(1024, 1) test_data_all_gather_##FuncNameType( \
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DataType* result_buf, DataType* test_buf, size_t num_elems, int num_ranks, int my_rank, int seq) { \
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for (int rank = 0; rank < num_ranks; rank++) { \
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size_t rank_offset = rank * num_elems; \
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unsigned int seed = (unsigned int)(blockIdx.x * blockDim.x + threadIdx.x + rank + seq); \
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for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_elems; i += blockDim.x * gridDim.x) { \
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seed = ranqd1(seed); \
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test_buf[rank_offset + i] = DataType(seed % blockDim.x) / DataType(blockDim.x); \
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assert(result_buf[rank_offset + i] == test_buf[rank_offset + i]); \
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} \
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} \
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}
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TEST_DATA_ALL_GATHER(bfloat16, __nv_bfloat16)
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TEST_DATA_ALL_GATHER(float16, __half)
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TEST_DATA_ALL_GATHER(float32, float)
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TEST_DATA_ALL_GATHER(int32, int)
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#define TEST_DATA_ALL_REDUCE(FuncNameType, DataType, Eps) \
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extern "C" __global__ void __launch_bounds__(1024, 1) test_data_all_reduce_##FuncNameType( \
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DataType* result_buf, DataType* test_buf, size_t num_elems, int num_ranks, int my_rank, int seq) { \
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for (int rank = 0; rank < num_ranks; rank++) { \
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unsigned int seed = (unsigned int)(blockIdx.x * blockDim.x + threadIdx.x + rank + seq); \
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for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_elems; i += blockDim.x * gridDim.x) { \
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if (rank == 0) { \
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test_buf[i] = 0; \
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} \
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seed = ranqd1(seed); \
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test_buf[i] += DataType(seed % blockDim.x) / DataType(blockDim.x); \
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} \
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} \
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for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_elems; i += blockDim.x * gridDim.x) { \
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float expected = float(test_buf[i]); \
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float result = float(result_buf[i]); \
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float tol = Eps * num_ranks * (1.0f + abs(expected)); \
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assert(abs(result - expected) <= tol); \
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} \
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}
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TEST_DATA_ALL_REDUCE(bfloat16, __nv_bfloat16, 7.8125e-3f)
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TEST_DATA_ALL_REDUCE(float16, __half, 9.765625e-4f)
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TEST_DATA_ALL_REDUCE(float32, float, 1.1920929e-7f)
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TEST_DATA_ALL_REDUCE(int32, int, 0.0f)
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#define TEST_DATA_REDUCE_SCATTER(FuncNameType, DataType, Eps) \
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extern "C" __global__ void __launch_bounds__(1024, 1) test_data_reduce_scatter_##FuncNameType( \
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DataType* result_buf, DataType* test_buf, size_t num_elems, int num_ranks, int my_rank, int seq) { \
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int nem_elems_per_rank = num_elems / num_ranks; \
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int offset = nem_elems_per_rank * my_rank; \
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for (int rank = 0; rank < num_ranks; rank++) { \
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unsigned int seed = (unsigned int)(blockIdx.x * blockDim.x + threadIdx.x + rank + seq); \
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for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_elems; i += blockDim.x * gridDim.x) { \
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if (rank == 0) { \
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test_buf[i] = 0; \
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} \
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seed = ranqd1(seed); \
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test_buf[i] += DataType(seed % blockDim.x) / DataType(blockDim.x); \
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} \
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} \
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for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_elems; i += blockDim.x * gridDim.x) { \
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if (i >= offset && i < offset + nem_elems_per_rank) { \
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float expected = float(test_buf[i]); \
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float result = float(result_buf[i - offset]); \
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float tol = Eps * num_ranks * (1.0f + abs(expected)); \
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assert(abs(result - expected) <= tol); \
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} \
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} \
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}
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TEST_DATA_REDUCE_SCATTER(bfloat16, __nv_bfloat16, 7.8125e-3f)
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TEST_DATA_REDUCE_SCATTER(float16, __half, 9.765625e-4f)
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TEST_DATA_REDUCE_SCATTER(float32, float, 1.1920929e-7f)
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TEST_DATA_REDUCE_SCATTER(int32, int, 0.0f)
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#define TEST_DATA_ALL_TO_ALL(FuncNameType, DataType) \
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extern "C" __global__ void __launch_bounds__(1024, 1) test_data_all_to_all_##FuncNameType( \
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DataType* result_buf, DataType* test_buf, size_t num_elems, int num_ranks, int my_rank, int seq) { \
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int nem_elems_per_rank = num_elems / num_ranks; \
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int offset = nem_elems_per_rank * my_rank; \
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for (int rank = 0; rank < num_ranks; rank++) { \
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size_t rank_offset = rank * nem_elems_per_rank; \
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unsigned int seed = (unsigned int)(blockIdx.x * blockDim.x + threadIdx.x + rank + seq); \
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for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_elems; i += blockDim.x * gridDim.x) { \
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seed = ranqd1(seed); \
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if (i >= my_rank * nem_elems_per_rank && i < (my_rank + 1) * nem_elems_per_rank) { \
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test_buf[rank_offset + i - offset] = DataType(seed % blockDim.x) / DataType(blockDim.x); \
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assert(result_buf[rank_offset + i - offset] == test_buf[rank_offset + i - offset]); \
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} \
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} \
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} \
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}
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TEST_DATA_ALL_TO_ALL(bfloat16, __nv_bfloat16)
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TEST_DATA_ALL_TO_ALL(float16, __half)
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TEST_DATA_ALL_TO_ALL(float32, float)
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TEST_DATA_ALL_TO_ALL(int32, int) |