91 lines
2.6 KiB
Python
91 lines
2.6 KiB
Python
"""Test: try llvm.inline_asm matching cvt_i8_bf16 pattern exactly."""
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import torch
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import cutlass.cute as cute
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import cutlass.torch as cutlass_torch
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from cutlass.cutlass_dsl import dsl_user_op
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from cutlass._mlir.dialects import llvm
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from cutlass.cute.typing import Float32, Int32
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import cutlass
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# Approach 1: llvm.inline_asm with Int32._mlir_type (matching cvt_i8_bf16 pattern)
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@dsl_user_op
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def f32_to_i32_rni_v1(x: Float32, *, loc=None, ip=None) -> Int32:
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val_i32 = llvm.inline_asm(
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Int32._mlir_type(),
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[Float32(x).ir_value(loc=loc, ip=ip)],
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"cvt.rni.s32.f32 $0, $1;",
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"=r,f",
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has_side_effects=False,
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is_align_stack=False,
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asm_dialect=llvm.AsmDialect.AD_ATT,
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loc=loc,
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ip=ip,
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)
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return Int32(val_i32)
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# Approach 2: llvm.inline_asm without asm_dialect (default)
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@dsl_user_op
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def f32_to_i32_rni_v2(x: Float32, *, loc=None, ip=None) -> Int32:
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val_i32 = llvm.inline_asm(
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Int32._mlir_type(),
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[Float32(x).ir_value(loc=loc, ip=ip)],
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"cvt.rni.s32.f32 $0, $1;",
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"=r,f",
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has_side_effects=False,
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is_align_stack=False,
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loc=loc,
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ip=ip,
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)
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return Int32(val_i32)
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# Approach 3: Using multi-line block like cvt_i8_bf16
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@dsl_user_op
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def f32_to_i32_rni_v3(x: Float32, *, loc=None, ip=None) -> Int32:
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val_i32 = llvm.inline_asm(
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Int32._mlir_type(),
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[Float32(x).ir_value(loc=loc, ip=ip)],
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"{\n\tcvt.rni.s32.f32 $0, $1;\n\t}",
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"=r,f",
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has_side_effects=False,
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is_align_stack=False,
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loc=loc,
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ip=ip,
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)
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return Int32(val_i32)
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KERNELS = {
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"v1_mlir_type": (f32_to_i32_rni_v1, None),
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"v2_no_asm_dialect": (f32_to_i32_rni_v2, None),
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"v3_multiline": (f32_to_i32_rni_v3, None),
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}
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import sys
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approach = sys.argv[1] if len(sys.argv) > 1 else "v1_mlir_type"
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func = KERNELS[approach][0]
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@cute.kernel
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def test_k(inp: cute.Tensor, out: cute.Tensor):
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tidx, _, _ = cute.arch.thread_idx()
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if tidx == Int32(0):
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x = cute.arch.load(inp.iterator, Float32)
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r = func(x)
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cute.arch.store(out.iterator, r)
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if __name__ == "__main__":
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x = torch.tensor([3.7], dtype=torch.float32, device='cuda')
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o = torch.zeros(1, dtype=torch.int32, device='cuda')
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xc = cutlass_torch.from_dlpack(x).mark_layout_dynamic(leading_dim=0)
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oc = cutlass_torch.from_dlpack(o).mark_layout_dynamic(leading_dim=0)
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print(f"Approach: {approach}")
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print("Compiling...")
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compiled = cute.compile(test_k, xc, oc)
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print("Running...")
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compiled(xc, oc)
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print(f"Result: {o.item()} (expected 4)")
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