[FRONTEND] fix broadcasting for where (#729)
Fixes #532, all 3 inputs to where have to be broadcast together.
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@@ -410,9 +410,34 @@ def test_where(dtype):
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assert (z == to_numpy(z_tri)).all()
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def test_where_broadcast():
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@triton.jit
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def where_kernel(cond_ptr, a_ptr, out_ptr, BLOCK_SIZE: tl.constexpr):
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xoffsets = tl.reshape(tl.arange(0, BLOCK_SIZE), [BLOCK_SIZE, 1])
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yoffsets = tl.reshape(tl.arange(0, BLOCK_SIZE), [1, BLOCK_SIZE])
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mask = tl.load(cond_ptr + yoffsets)
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vals = tl.load(a_ptr + yoffsets + BLOCK_SIZE * xoffsets)
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res = tl.where(mask, vals, 0.)
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tl.store(out_ptr + yoffsets + BLOCK_SIZE * xoffsets, res)
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SIZE = 32
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dtype = 'float32'
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rs = RandomState(17)
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x = numpy_random((SIZE, SIZE), dtype_str=dtype, rs=rs)
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mask = numpy_random(SIZE, 'bool', rs=rs)
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z = np.where(mask, x, 0)
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cond_tri = to_triton(mask, device="cuda")
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x_tri = to_triton(x, device='cuda', dst_type=dtype)
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z_tri = to_triton(np.empty((SIZE, SIZE), dtype=z.dtype), device='cuda', dst_type=dtype)
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where_kernel[(1,)](cond_tri, x_tri, z_tri, SIZE)
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assert (z == to_numpy(z_tri)).all()
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# ---------------
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# test unary ops
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# ---------------
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@pytest.mark.parametrize("dtype_x, expr", [
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(dtype_x, ' -x') for dtype_x in dtypes_with_bfloat16
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] + [
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@@ -973,8 +973,9 @@ def where(condition: tl.tensor,
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builder: ir.builder) -> tl.tensor:
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condition = cast(condition, tl.int1, builder)
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if condition.type.is_block():
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x = broadcast_impl_shape(x, condition.type.get_block_shapes(), builder)
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y = broadcast_impl_shape(y, condition.type.get_block_shapes(), builder)
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condition, x = broadcast_impl_value(condition, x, builder)
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x, y = broadcast_impl_value(x, y, builder)
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condition, x = broadcast_impl_value(condition, x, builder)
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x, y = binary_op_type_checking_impl(x, y, builder, True, True)
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ret_ty = x.type
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