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https://github.com/tile-ai/tilelang.git
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Lei WangandZhiwen Mo
5a538f3b37
* Support packed subtype views in layout reshape * lint fix --------- Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk>
129 lines
3.7 KiB
Python
129 lines
3.7 KiB
Python
import tilelang.language as T
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from tilelang import tvm as tvm
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import tilelang.testing
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import tilelang as tl
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import pytest
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import torch
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def view_test(N, M, dtype, new_dtype=None):
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new_shape = [N // M, M]
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if new_dtype:
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from tvm import DataType
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dtype_src = DataType(dtype)
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dtype_dst = DataType(new_dtype)
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src_bits = dtype_src.bits
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dst_bits = dtype_dst.bits
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scale = src_bits / dst_bits
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new_shape[-1] = int(M * scale)
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@T.prim_func
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def main(
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A: T.Tensor((N,), dtype),
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B: T.Tensor(new_shape, new_dtype if new_dtype else dtype),
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):
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with T.Kernel(1) as _:
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A_viewed = T.view(A, new_shape, dtype=new_dtype)
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T.copy(A_viewed, B)
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return main
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def run_view(N, M, dtype, new_dtype=None):
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program = view_test(N, M, dtype, new_dtype)
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jit_kernel = tl.compile(program, out_idx=-1)
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profiler = jit_kernel.get_profiler()
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def ref_program(A):
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if new_dtype:
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torch_dtype = T.dtype(new_dtype).as_torch()
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return A.view(N // M, M).view(dtype=torch_dtype)
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return A.view(N // M, M)
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profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
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def test_reshape_view():
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# Test view with same dtype
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run_view(1024, 32, T.float32)
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run_view(2048, 64, T.float16)
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# Test view with dtype conversion
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run_view(1024, 32, T.float32, T.float16)
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run_view(2048, 64, T.float16, T.float32)
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def view_shape_mismatch_test(N, M, dtype, new_dtype=None):
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new_shape = [N // M, M + 1]
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if new_dtype:
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from tvm import DataType
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dtype_src = DataType(dtype)
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dtype_dst = DataType(new_dtype)
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src_bits = dtype_src.bits
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dst_bits = dtype_dst.bits
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scale = src_bits / dst_bits
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new_shape[-1] = int(M * scale)
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@T.prim_func
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def main(
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A: T.Tensor((N,), dtype),
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B: T.Tensor(new_shape, new_dtype if new_dtype else dtype),
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):
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with T.Kernel(1) as _:
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A_viewed = T.view(A, new_shape, dtype=new_dtype)
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T.copy(A_viewed, B)
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return main
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def test_view_shape_mismatch():
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with pytest.raises(AssertionError):
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view_shape_mismatch_test(1024, 32, T.float32)
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def test_view_subbyte_dtype_change():
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A = tvm.tir.decl_buffer((16, 32), "float4_e2m1fn", name="A")
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A_viewed = T.view(A, (16, 16), dtype=T.uint8)
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assert str(A_viewed.dtype) == "uint8"
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assert tuple(int(dim) for dim in A_viewed.shape) == (16, 16)
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assert A_viewed.data.same_as(A.data)
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def fp4_to_uint8_view_test(rows_per_cta=16, mask_k=256):
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@T.prim_func
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def main(
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A: T.Tensor((rows_per_cta, mask_k), T.bfloat16),
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B: T.Tensor((rows_per_cta, mask_k // 2), T.uint8),
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):
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with T.Kernel(1, threads=256) as _:
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A_frag = T.alloc_fragment((rows_per_cta, mask_k), T.bfloat16)
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B_shared_fp4 = T.alloc_shared((rows_per_cta, mask_k), T.float4_e2m1fn)
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B_shared_uint8 = T.view(B_shared_fp4, (rows_per_cta, mask_k // 2), dtype=T.uint8)
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T.copy(A, A_frag)
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for i, j in T.Parallel(rows_per_cta, mask_k):
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B_shared_fp4[i, j] = T.cast(A_frag[i, j], T.float4_e2m1fn)
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T.copy(B_shared_uint8, B)
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return main
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@tilelang.testing.requires_cuda
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@tilelang.testing.requires_cuda_compute_version_ge(10, 0)
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def test_view_shared_fp4_to_uint8_compile():
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program = fp4_to_uint8_view_test()
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kernel = tl.compile(program, out_idx=-1)
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src = kernel.get_kernel_source()
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assert "fp4_e2" in src
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dummy_input = torch.randn((16, 256), device="cuda", dtype=torch.bfloat16)
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output = kernel(dummy_input)
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assert output.shape == (16, 128)
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assert output.dtype == torch.uint8
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if __name__ == "__main__":
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tilelang.testing.main()
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