masahi commented on code in PR #14166:
URL: https://github.com/apache/tvm/pull/14166#discussion_r1122385560
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python/tvm/contrib/cutlass/gen_tensor_op.py:
##########
@@ -548,17 +549,20 @@ def get_batch_stride(stride_annot, arg0_idx, arg1_idx,
arg0_axis_idx, arg1_axis_
attrs["ElementInputB"] =
DataTypeTag[dtype_map[annotations[f"arg{rhs_arg_idx}_dtype"]]]
attrs["ElementOutput"] =
DataTypeTag[dtype_map[annotations["ret_dtype"]]]
- attrs["K"] = str(int(lhs_shape[batched_offset + 1]))
- attrs["M"] = get_dim(lhs_shape[batched_offset], lhs_arg, 0,
batched_offset)
+ attrs["K"] = str(int(lhs_shape[lhs_batched_offset + 1]))
+ attrs["M"] = get_dim(lhs_shape[lhs_batched_offset], lhs_arg, 0,
lhs_batched_offset)
if transposed:
- attrs["N"] = get_dim(rhs_shape[batched_offset], rhs_arg, 0,
batched_offset)
+ attrs["N"] = get_dim(rhs_shape[rhs_batched_offset], rhs_arg, 0,
rhs_batched_offset)
else:
- attrs["N"] = get_dim(rhs_shape[batched_offset + 1], rhs_arg, 1,
batched_offset)
+ attrs["N"] = get_dim(rhs_shape[rhs_batched_offset + 1], rhs_arg,
1, rhs_batched_offset)
if batched:
headers.append("cutlass/gemm/device/gemm_batched.h")
- attrs["batch"] = get_dim(lhs_shape[0], lhs_arg, 0)
+ # TODO: Support dynamic shape
+ # With the support of more general broadcasting in batch matmul,
+ # it needs more sophisticated code to handle dynamic shape
+ attrs["batch"] = str(int(annotations["batch"]))
Review Comment:
What do you mean by `more general broadcasting`? Like the conceptual "batch"
dimension spanning multiple axes?
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