mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-28 07:43:01 +08:00
Tong WU
a148d62a41
* [Feature] Add vectorized float16 and float32 conversion support in CUDA codegen * Implemented handling for conversions between float16 and float32 types, specifically for vectorized operations using __half22float2 and __float22half2_rn. * Enhanced the existing code to support both directions of conversion based on the lane count. * Improved overall type handling in the VisitExpr_ method for better compatibility with TileLang. * [Feature] Add float32 to float8 conversion support in CUDA codegen * Implemented handling for conversion from float32 to float8 (E4M3/E5M2) in the VisitExpr_ method. * Added vectorized conversion support using __nv_cvt_float2_to_fp8x2 for float2 to fp8x2 transformations. * Enhanced type handling for better compatibility with TileLang, particularly for float8 types. * lint * fix a bug * [Enhancement] Support lanes=4 cases and add unit test for vectorized cast * lint * [Feature] Refactor bf16 convertion operations and remove legacy compile flags * lint
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
import torch
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import tilelang.testing
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import tilelang.language as T
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str2dtype = {
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"float32": torch.float32,
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"float16": torch.float16,
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"bfloat16": torch.bfloat16,
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"float8_e4m3": torch.float8_e4m3fn,
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"float8_e5m2": torch.float8_e5m2,
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}
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@tilelang.jit
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def vectorized_cast_kernel(M: int, dtype_A: str, dtype_B: str):
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assert M % 256 == 0
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@T.prim_func
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def main(
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A: T.Tensor[(M), dtype_A], # noqa: F821
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B: T.Tensor[(M), dtype_B], # noqa: F821
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):
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with T.Kernel(1, threads=128):
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T.copy(A, B)
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return main
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def run_vectorized_cast(src_dtype_str: str, dst_dtype_str: str, check_str: str, lanes: int = 2):
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"""Run the vectorized cast kernel and check the correctness.
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Args:
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src_dtype_str: The source data type string.
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dst_dtype_str: The destination data type string.
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check_str: Used to ensure vectorized cast is used.
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lanes: The number of lanes of the source and destination data types.
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"""
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M = 128 * lanes
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kernel = vectorized_cast_kernel(M, src_dtype_str, dst_dtype_str)
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A = torch.randn(M, dtype=str2dtype[src_dtype_str]).cuda()
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B = torch.zeros(M, dtype=str2dtype[dst_dtype_str]).cuda()
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kernel(A, B)
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torch.testing.assert_close(A.to(str2dtype[dst_dtype_str]), B)
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code = kernel.get_kernel_source()
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assert check_str in code, \
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f"Cast {src_dtype_str} to {dst_dtype_str} with {lanes=} is not vectorized!"
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def test_vectorized_cast():
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# fp32 -> fp16
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run_vectorized_cast("float32", "float16", "__float22half2_rn", 2)
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run_vectorized_cast("float32", "float16", "__float22half2_rn", 4)
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# fp16 -> fp32
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run_vectorized_cast("float16", "float32", "__half22float2", 2)
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run_vectorized_cast("float16", "float32", "__half22float2", 4)
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# fp32 -> fp8_e4m3
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run_vectorized_cast("float32", "float8_e4m3", "__nv_cvt_float2_to_fp8x2", 2)
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run_vectorized_cast("float32", "float8_e4m3", "__nv_cvt_float2_to_fp8x2", 4)
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# fp32 -> fp8_e5m2
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run_vectorized_cast("float32", "float8_e5m2", "__nv_cvt_float2_to_fp8x2", 2)
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run_vectorized_cast("float32", "float8_e5m2", "__nv_cvt_float2_to_fp8x2", 4)
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# fp32 -> bf16
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run_vectorized_cast("float32", "bfloat16", "__float22bfloat162_rn", 2)
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run_vectorized_cast("float32", "bfloat16", "__float22bfloat162_rn", 4)
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# bf16 -> fp32
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run_vectorized_cast("bfloat16", "float32", "__bfloat1622float2", 2)
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run_vectorized_cast("bfloat16", "float32", "__bfloat1622float2", 4)
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if __name__ == "__main__":
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tilelang.testing.main()
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