[TOPI] Use f-strings for string formatting, NFC

python/tvm/topi/arm_cpu/conv2d.py

@@ -33,10 +33,7 @@
     schedule_conv2d_spatial_pack_nchw,
     schedule_conv2d_spatial_pack_nhwc,
 )
-from .mprofile.dsp.conv2d import (
-    conv2d_nhwc_dsp_compute,
-    conv2d_nhwc_dsp_schedule,
-)
+from .mprofile.dsp.conv2d import conv2d_nhwc_dsp_compute, conv2d_nhwc_dsp_schedule
 
 
 @autotvm.register_topi_compute("conv2d_nchw_spatial_pack.arm_cpu")
@@ -267,13 +264,7 @@ def _schedule_winograd(cfg, s, output, last):
     if isinstance(U.op, tvm.te.ComputeOp):
         kernel, G = U.op.input_tensors
         s[G].compute_inline()
-        (
-            eps,
-            nu,
-            k,
-            c,
-            kk,
-        ) = s[U].op.axis
+        (eps, nu, k, c, kk) = s[U].op.axis
         if autotvm.GLOBAL_SCOPE.in_tuning:
             # kernel transformation will be pre-computed during compilation, so we skip
             # this part to make tuning records correct
@@ -364,7 +355,7 @@ def conv2d_nchw_winograd_nnpack(cfg, data, kernel, strides, padding, dilation, o
             tvm.contrib.nnpack.ConvolutionAlgorithm.WT_8x8_FP16,
         )
     else:
-        raise ValueError("Unsupported data type {} for conv2d winograd nnpack".format(dtype))
+        raise ValueError(f"Unsupported data type {dtype} for conv2d winograd nnpack")
 
 
 @autotvm.register_topi_schedule("conv2d_nchw_winograd_nnpack.arm_cpu")

python/tvm/topi/arm_cpu/conv2d_alter_op.py

@@ -152,9 +152,7 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
         )
         dispatch_ctx.update(target, new_workload, cfg)
         return relay.nn.conv2d(
-            inputs[0],
-            relay.Constant(tvm.nd.array(reshaped_new_kernel)),
-            **new_attrs,
+            inputs[0], relay.Constant(tvm.nd.array(reshaped_new_kernel)), **new_attrs
         )
 
     # Only microTVM does layout alteration for NHWC layout with real data types
@@ -167,7 +165,7 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
         CO, _, KH, KW = get_const_tuple(kernel.shape)
         VC = cfg["tile_co"].size[-1]
 
-        new_attrs["kernel_layout"] = "OIHW%do" % VC
+        new_attrs["kernel_layout"] = f"OIHW{VC}o"
 
         new_data = data
         new_kernel = te.placeholder((idxd(CO, VC), CI, KH, KW, VC), dtype=kernel.dtype)
@@ -275,7 +273,7 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
         CO, M, KH, KW = get_const_tuple(kernel.shape)
         VC = cfg["tile_co"].size[-1]
 
-        new_attrs["kernel_layout"] = "OIHW%do" % (cfg["tile_co"].size[-1])
+        new_attrs["kernel_layout"] = f"OIHW{cfg['tile_co'].size[-1]}o"
 
         # Store the same config for the altered operator (workload)
         new_data = data
@@ -309,10 +307,10 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
 
         # update new attrs
         new_attrs["channels"] = out_channel
-        new_attrs["data_layout"] = "NCHW%dc" % ic_bn
+        new_attrs["data_layout"] = f"NCHW{ic_bn}c"
         # (oc, ic, h, w) -> (OC, IC, h, w, ic, oc)
-        new_attrs["kernel_layout"] = "OIHW%di%do" % (ic_bn, oc_bn)
-        new_attrs["out_layout"] = "NCHW%dc" % oc_bn
+        new_attrs["kernel_layout"] = f"OIHW{ic_bn}i{oc_bn}o"
+        new_attrs["out_layout"] = f"NCHW{oc_bn}c"
 
         # Store altered operator's config
         new_data = te.placeholder(
@@ -353,9 +351,9 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
 
         # update new attrs
         new_attrs["channels"] = out_channel
-        new_attrs["data_layout"] = "NCHW%dc" % ic_bn
-        new_attrs["kernel_layout"] = "OIHW1i%do" % oc_bn
-        new_attrs["out_layout"] = "NCHW%dc" % oc_bn
+        new_attrs["data_layout"] = f"NCHW{ic_bn}c"
+        new_attrs["kernel_layout"] = f"OIHW1i{oc_bn}o"
+        new_attrs["out_layout"] = f"NCHW{oc_bn}c"
 
         # Store altered operator's config.
         new_data = te.placeholder(
@@ -407,9 +405,9 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
 
         # update new attrs
         new_attrs["channels"] = out_channel
-        new_attrs["data_layout"] = "NCHW%dc" % ic_bn
-        new_attrs["kernel_layout"] = "OIHW{:n}i{:n}o{:n}i".format(ic_bn // n_elems, oc_bn, n_elems)
-        new_attrs["out_layout"] = "NCHW%dc" % oc_bn
+        new_attrs["data_layout"] = f"NCHW{ic_bn}c"
+        new_attrs["kernel_layout"] = f"OIHW{ic_bn // n_elems:n}i{oc_bn:n}o{n_elems:n}i"
+        new_attrs["out_layout"] = f"NCHW{oc_bn}c"
 
         # Store altered operator's config.
         new_data = te.placeholder(

python/tvm/topi/cuda/batch_matmul.py

@@ -342,7 +342,7 @@ def _schedule_batch_matmul_int8(cfg, s, output):
     _, N, _ = get_const_tuple(input_y.shape)
 
     k_factor = 4
-    assert K % k_factor == 0, "Input dimension must divide {}".format(k_factor)
+    assert K % k_factor == 0, f"Input dimension must divide {k_factor}"
     if K % 16 == 0:
         k_factor = 16
 

python/tvm/topi/cuda/conv2d_int8.py

@@ -90,7 +90,7 @@ def conv2d_NCHWc_int8(cfg, data, kernel, stride, padding, dilation, layout, out_
         batch, channels, height, width = get_const_tuple(data.shape)
         assert (
             channels % ic_block_factor == 0
-        ), "Number of input channels should be multiple of {}".format(ic_block_factor)
+        ), f"Number of input channels should be multiple of {ic_block_factor}"
         packed_data = te.compute(
             (batch, channels // ic_block_factor, height, width, ic_block_factor),
             lambda n, c, h, w, vc: data[n, c * ic_block_factor + vc, h, w],
@@ -100,7 +100,7 @@ def conv2d_NCHWc_int8(cfg, data, kernel, stride, padding, dilation, layout, out_
         out_channels, in_channels, kernel_h, kernel_w = get_const_tuple(kernel.shape)
         assert (
             out_channels % oc_block_factor == 0
-        ), "Number of output channels should be multiple of {}".format(oc_block_factor)
+        ), f"Number of output channels should be multiple of {oc_block_factor}"
         packed_kernel = te.compute(
             (
                 out_channels // oc_block_factor,

python/tvm/topi/cuda/conv3d_direct.py

@@ -31,7 +31,7 @@ def schedule_direct_conv3d_cuda(cfg, s, conv, layout, workload_name):
     elif layout == "NDHWC":
         n, d, y, x, f = s[conv].op.axis
     else:
-        raise ValueError("not support this layout {} yet".format(layout))
+        raise ValueError(f"not support this layout {layout} yet")
     rc, rd, ry, rx = s[conv].op.reduce_axis
     cfg.define_split("tile_f", f, num_outputs=4)
     cfg.define_split("tile_d", d, num_outputs=4)

python/tvm/topi/cuda/dense.py

@@ -29,13 +29,7 @@
 
 
 def _matmul_cublas_common(
-    cfg,
-    tensor_a,
-    tensor_b,
-    bias=None,
-    out_dtype=None,
-    transpose_a=False,
-    transpose_b=False,
+    cfg, tensor_a, tensor_b, bias=None, out_dtype=None, transpose_a=False, transpose_b=False
 ):
     assert len(tensor_a.shape) == 2 and len(tensor_b.shape) == 2, "only support 2-dim matmul"
     if bias is not None:
@@ -58,13 +52,7 @@ def _matmul_cublas_common(
 
 @autotvm.register_topi_compute("matmul_cublas.cuda")
 def matmul_cublas(
-    cfg,
-    tensor_a,
-    tensor_b,
-    bias=None,
-    out_dtype=None,
-    transpose_a=False,
-    transpose_b=False,
+    cfg, tensor_a, tensor_b, bias=None, out_dtype=None, transpose_a=False, transpose_b=False
 ):
     """Matmul operator on CUDA with CUBLAS"""
     return _matmul_cublas_common(cfg, tensor_a, tensor_b, bias, out_dtype, transpose_a, transpose_b)
@@ -142,7 +130,7 @@ def _schedule_dense_int8(cfg, s, output):
     out_dim, _ = get_const_tuple(weight.shape)
 
     in_dim_factor = 4
-    assert in_dim % in_dim_factor == 0, "Input dimension must divide {}".format(in_dim_factor)
+    assert in_dim % in_dim_factor == 0, f"Input dimension must divide {in_dim_factor}"
     if in_dim % 16 == 0:
         in_dim_factor = 16
 

python/tvm/topi/cuda/group_conv2d_nchw.py

@@ -245,10 +245,10 @@ def group_conv2d_NCHWc_int8(
         assert out_channels % groups == 0, "output channels must divide group size"
         assert (
             channels % ic_block_factor == 0
-        ), "Number of input channels per group must divide {}".format(ic_block_factor)
+        ), f"Number of input channels per group must divide {ic_block_factor}"
         assert (
             out_channels % oc_block_factor == 0
-        ), "Number of output channels per group must divide {}".format(oc_block_factor)
+        ), f"Number of output channels per group must divide {oc_block_factor}"
 
         packed_data = te.compute(
             (batch, channels // ic_block_factor, height, width, ic_block_factor),
@@ -282,14 +282,10 @@ def group_conv2d_NCHWc_int8(
     # Shall we pad the channels to avoid raising assertions?
     assert (
         groups <= oc_chunk
-    ), "Number of groups {} should be less than " "output channel chunk size {}".format(
-        groups, oc_chunk
-    )
+    ), f"Number of groups {groups} should be less than output channel chunk size {oc_chunk}"
     assert (
         groups <= ic_chunk
-    ), "Number of groups {} should be less than " "input channel chunk size {}".format(
-        groups, ic_chunk
-    )
+    ), f"Number of groups {groups} should be less than input channel chunk size {ic_chunk}"
 
     if isinstance(stride, int):
         stride_h = stride_w = stride

python/tvm/topi/cuda/scan.py

@@ -31,7 +31,7 @@
 
 def _get_thrust_func_name(tvmop):
     tvmop_to_thrust_func_name = {tvm.tir.generic.add: "tvm.contrib.thrust.sum_scan"}
-    assert tvmop in tvmop_to_thrust_func_name, "{} not supported by thrust".format(tvmop)
+    assert tvmop in tvmop_to_thrust_func_name, f"{tvmop} not supported by thrust"
     return tvmop_to_thrust_func_name[tvmop]
 
 

python/tvm/topi/cuda/softmax.py

@@ -44,10 +44,7 @@ def _schedule_softmax(softmax_op, s, outs, tgt):
         expsum = softmax_op.input_tensors[2]
     else:
         raise ValueError(
-            "Tag is expected to be softmax_output or log_softmax_output. \
-                         Got {0}".format(
-                op_tag
-            )
+            f"Tag is expected to be softmax_output or log_softmax_output. Got {op_tag}"
         )
 
     # The nvptx and rocm backends only supports 32-bits warp shuffle

python/tvm/topi/cuda/sparse.py

@@ -159,10 +159,9 @@ def gen_ir(data, w_data, w_indices, w_indptr, out):
         bs_m = bs_n
         mb = m // bs_m
         mi = warp_size
-        assert (
-            mb >= mi
-        ), "Number of block rows in dense matrix must be larger than warp size: {} vs {}.".format(
-            warp_size, mb
+        assert mb >= mi, (
+            f"Number of block rows in dense matrix must be larger than warp size: "
+            f"{warp_size} vs {mb}."
         )
         mo = ceil_div(mb, mi)
         ni = 1  # TODO(tkonolige): how do I compute the number of warps per block?

python/tvm/topi/generic/conv2d.py

@@ -45,14 +45,12 @@ def fallback_schedule_cpu_common_int8(cfg, wkl, int32_lanes, num_int8_elements):
     dilated_kernel_w = (wkl.kernel_w - 1) * wkl.dilation_w + 1
     out_width = (wkl.width + pl + pr - dilated_kernel_w) // WSTR + 1
 
-    assert wkl.out_filter % int32_lanes == 0, "wkl.out_filter=%d, int32_lanes=%d" % (
-        wkl.out_filter,
-        int32_lanes,
-    )
-    assert wkl.in_filter % num_int8_elements == 0, "wkl.in_filter=%d, num_int8_elements=%d" % (
-        wkl.in_filter,
-        num_int8_elements,
-    )
+    assert (
+        wkl.out_filter % int32_lanes == 0
+    ), f"wkl.out_filter={wkl.out_filter}, int32_lanes={int32_lanes}"
+    assert (
+        wkl.in_filter % num_int8_elements == 0
+    ), f"wkl.in_filter={wkl.in_filter}, num_int8_elements={num_int8_elements}"
 
     oc_bn = int32_lanes if int32_lanes >= num_int8_elements else num_int8_elements
     ic_bn = 1
@@ -93,14 +91,12 @@ def fallback_schedule_cpu_1x1_int8(cfg, wkl, int32_lanes, num_int8_elements):
     out_height = (wkl.height + pt + pb - wkl.kernel_h) // HSTR + 1
     out_width = (wkl.width + pl + pr - wkl.kernel_w) // WSTR + 1
 
-    assert wkl.out_filter % int32_lanes == 0, "wkl.out_filter=%d, int32_lanes=%d" % (
-        wkl.out_filter,
-        int32_lanes,
-    )
-    assert wkl.in_filter % num_int8_elements == 0, "wkl.in_filter=%d, num_int8_elements=%d" % (
-        wkl.in_filter,
-        num_int8_elements,
-    )
+    assert (
+        wkl.out_filter % int32_lanes == 0
+    ), f"wkl.out_filter={wkl.out_filter}, int32_lanes={int32_lanes}"
+    assert (
+        wkl.in_filter % num_int8_elements == 0
+    ), f"wkl.in_filter={wkl.in_filter}, num_int8_elements={num_int8_elements}"
 
     oc_bn = int32_lanes if int32_lanes >= num_int8_elements else num_int8_elements
     ic_bn = 1
@@ -118,7 +114,7 @@ def fallback_schedule_cpu_1x1_int8(cfg, wkl, int32_lanes, num_int8_elements):
                     cfg["tile_oh"] = OtherOptionEntity(oh_factor)
                     cfg["tile_ow"] = SplitEntity([out_width // ow_factor, ow_factor])
                     return
-    raise ValueError("cannot decide default schedule for workload: {}".format(wkl))
+    raise ValueError(f"cannot decide default schedule for workload: {wkl}")
 
 
 def schedule_conv_NCHWc_cpu_common_int8(
@@ -257,7 +253,7 @@ def schedule_conv_NCHWc_cpu_common_int8(
             oc_chunk, oc_block = s[O].split(oc, factor=oc_bn)
             s[O].reorder(oc_chunk, oh, ow_chunk, ow_block, oc_block)
         else:
-            raise ValueError("Unsupported output ndim: %s" % out_ndim)
+            raise ValueError(f"Unsupported output ndim: {out_ndim}")
         parallel_axis = s[O].fuse(batch, oc_chunk, oh)
         if inline_fused:
             s[C].compute_at(s[O], ow_block)
@@ -382,7 +378,7 @@ def schedule_conv_NCHWc_cpu_1x1_int8(
             oh_outer, oh_inner = s[O].split(oh, factor=oh_factor)
             ow_outer, ow_inner = s[O].split(ow, factor=ow_factor)
         else:
-            raise ValueError("Unsupported output ndim: %s" % out_ndim)
+            raise ValueError(f"Unsupported output ndim: {out_ndim}")
 
         s[O].reorder(oc_chunk, oh_outer, ow_outer, oh_inner, ow_inner, oc_block)
         parallel_axis = s[O].fuse(batch, oc_chunk, oh_outer)

python/tvm/topi/hexagon/conv2d_alter_op.py

@@ -51,9 +51,9 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
         new_attrs = {k: attrs[k] for k in attrs.keys()}
 
         new_attrs["channels"] = out_channel
-        new_attrs["data_layout"] = "NCHW%dc" % ic_bn
-        new_attrs["kernel_layout"] = "OIHW{:n}i{:n}o{:n}i".format(ic_bn // n_elems, oc_bn, n_elems)
-        new_attrs["out_layout"] = "NCHW%dc" % oc_bn
+        new_attrs["data_layout"] = f"NCHW{ic_bn}c"
+        new_attrs["kernel_layout"] = f"OIHW{ic_bn // n_elems:n}i{oc_bn:n}o{n_elems:n}i"
+        new_attrs["out_layout"] = f"NCHW{oc_bn}c"
 
         return relay.nn.contrib_conv2d_nchwc(*inputs, **new_attrs)
 

python/tvm/topi/hexagon/qnn/conv2d_alter_op.py

@@ -44,9 +44,9 @@ def _alter_qnn_conv2d_layout(attrs, inputs, tinfos, _out_type):
 
         new_attrs = dict(attrs)
         new_attrs["channels"] = out_channel
-        new_attrs["data_layout"] = "NCHW%dc" % ic_bn
-        new_attrs["kernel_layout"] = "OIHW{:n}i{:n}o{:n}i".format(ic_bn // n_elems, oc_bn, n_elems)
-        new_attrs["out_layout"] = "NCHW%dc" % oc_bn
+        new_attrs["data_layout"] = f"NCHW{ic_bn}c"
+        new_attrs["kernel_layout"] = f"OIHW{ic_bn // n_elems:n}i{oc_bn:n}o{n_elems:n}i"
+        new_attrs["out_layout"] = f"NCHW{oc_bn}c"
 
         return relay.qnn.op.conv2d(*inputs, **new_attrs)
 

python/tvm/topi/hls/nn.py

@@ -43,7 +43,7 @@ def traverse(OP):
                 Out = outs[0].op.output(0)
                 s[Conv2d].compute_at(s[Out], s[Out].op.axis[1])
         else:
-            raise RuntimeError("Unsupported operator: %s" % OP.tag)
+            raise RuntimeError(f"Unsupported operator: {OP.tag}")
 
     traverse(outs[0].op)
 
@@ -223,7 +223,7 @@ def traverse(OP):
                 Out = outs[0].op.output(0)
                 s[Reduce].compute_at(s[Out], s[Out].op.axis[0])
         else:
-            raise RuntimeError("Unsupported operator: %s" % OP.tag)
+            raise RuntimeError(f"Unsupported operator: {OP.tag}")
 
     traverse(outs[0].op)
 
@@ -264,10 +264,7 @@ def schedule_softmax(outs):
         expsum = softmax.op.input_tensors[2]
     else:
         raise ValueError(
-            "Tag is expected to be softmax_output or log_softmax_output. \
-                         Got {0}".format(
-                op_tag
-            )
+            f"Tag is expected to be softmax_output or log_softmax_output. Got {op_tag}"
         )
 
     if exp is not None:
@@ -315,7 +312,7 @@ def traverse(OP):
                 Out = outs[0].op.output(0)
                 s[Dense].compute_at(s[Out], s[Out].op.axis[1])
         else:
-            raise RuntimeError("Unsupported operator: %s" % OP.tag)
+            raise RuntimeError(f"Unsupported operator: {OP.tag}")
 
     traverse(outs[0].op)
 
@@ -358,7 +355,7 @@ def traverse(OP):
                 Out = outs[0].op.output(0)
                 s[Pool].compute_at(s[Out], s[Out].op.axis[1])
         else:
-            raise RuntimeError("Unsupported operator: %s" % OP.tag)
+            raise RuntimeError(f"Unsupported operator: {OP.tag}")
 
     traverse(outs[0].op)
 
@@ -401,7 +398,7 @@ def traverse(OP):
                 Out = outs[0].op.output(0)
                 s[Pool].compute_at(s[Out], s[Out].op.axis[1])
         else:
-            raise RuntimeError("Unsupported operator: %s" % OP.tag)
+            raise RuntimeError(f"Unsupported operator: {OP.tag}")
 
     traverse(outs[0].op)