add xlib.avecl

xlib/avecl/_internal/op/concat.py

@@ -0,0 +1,70 @@
+from ..backend import Kernel
+from ..HArgs import HArgs
+from ..HType import HType
+from ..HKernel import HKernel
+from ..info import ConcatInfo
+from ..SCacheton import SCacheton
+from ..Tensor import Tensor
+
+def concat(tensor_list, axis, dtype=None, output_t=None, is_add_to_output=False) -> Tensor:
+    """
+    arguments
+
+        tensor_list     Iterable
+
+        axis            Int
+
+        dtype           np.dtype
+
+        output_t            compute result to this Tensor.
+                            Tensor may be with different shape,
+                            but should match total size.
+                            gradfn will not be set.
+
+        is_add_to_output    add result to output_t if output_t is set.
+    """
+    tensor_list = tuple(tensor_list)
+    HArgs.check_zero_get_length(tensor_list)
+    HArgs.check_all_tensors(tensor_list)
+
+    device = HArgs.check_get_same_device(tensor_list)
+    shape_list, dtype_list, _ = HArgs.decompose(tensor_list)
+
+    op = SCacheton.get(_ConcatOp, shape_list, dtype_list, dtype, int(axis), False if output_t is None else is_add_to_output)
+
+    if output_t is None:
+        output_t = Tensor (op.info.o_shape, op.o_dtype, device=device)
+    elif output_t.shape.size != op.info.o_shape.size:
+        raise ValueError(f'output_t must have size {op.info.o_shape.size}')
+
+    for forward_krn,t in zip(op.forward_krns,tensor_list):
+        device.run_kernel(forward_krn, output_t.get_buffer(), t.get_buffer(), global_shape=(t.shape.size,) )
+
+    return output_t
+
+class _ConcatOp:
+    def __init__(self, shape_list, dtype_list, o_dtype, axis, is_add_to_output):
+        self.o_dtype = o_dtype = o_dtype if o_dtype is not None else HType.get_most_weighted_dtype (dtype_list)
+
+        self.info = info = ConcatInfo(shape_list, axis)
+
+        self.forward_krns = forward_krns = []
+
+        for i, (shape, dtype) in enumerate(zip(shape_list, dtype_list)):
+            forward_krn = Kernel(f"""
+
+{HKernel.define_tensor('O', info.o_shape, o_dtype )}
+{HKernel.define_tensor('I', shape, dtype)}
+
+__kernel void impl(__global O_PTR_TYPE* O_PTR_NAME, __global const I_PTR_TYPE* I_PTR_NAME)
+{{
+    size_t gid = get_global_id(0);
+
+    {HKernel.decompose_idx_to_axes_idxs('gid', 'I', shape.ndim)}
+
+    i{info.axis} += {info.axis_offsets[i]};
+
+    {'O_STORE_ADD' if is_add_to_output else 'O_GLOBAL_STORE'}( O_IDX({HKernel.axes_seq_enum('I', info.o_shape.ndim)}), I_GLOBAL_LOAD(gid) );
+}}
+""")
+            forward_krns.append(forward_krn)