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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/06/03 18:40:41 UTC
[GitHub] [tvm] elvin-n commented on a diff in pull request #11543: [OpenCL] Implement conv2d_winograd algorithm for Adreno
elvin-n commented on code in PR #11543:
URL: https://github.com/apache/tvm/pull/11543#discussion_r889214026
##########
python/tvm/topi/adreno/conv2d_alter_op.py:
##########
@@ -24,6 +24,7 @@
from tvm import te
from tvm import relay
from tvm import autotvm
+from .conv2d_nchw_winograd import _infer_tile_size
Review Comment:
move _infer_tile_size into adreno utils.py nad rename
##########
python/tvm/topi/adreno/conv2d_nchw_winograd.py:
##########
@@ -0,0 +1,481 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,unused-variable,unused-argument
+"""Winograd template for Adreno backend"""
Review Comment:
+ mention of NCHW especially because we have NHWC and desc is the same as here
##########
python/tvm/topi/adreno/conv2d_nchw_winograd.py:
##########
@@ -0,0 +1,481 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,unused-variable,unused-argument
+"""Winograd template for Adreno backend"""
+
+import logging
+import tvm
+from tvm import te
+from tvm import autotvm
+
+from tvm.topi import nn
+from tvm.topi.utils import get_const_int, get_const_tuple, traverse_inline
+from ..nn.winograd_util import winograd_transform_matrices
+from .utils import (
+ split_to_chunks,
+ pack_input,
+ pack_filter,
+ bind_data_copy,
+ get_texture_storage,
+)
+
+
+logger = logging.getLogger("conv2d_nchw_winograd")
+
+
+def _infer_tile_size(data):
Review Comment:
remove leading _
##########
python/tvm/topi/adreno/conv2d_nchw_winograd.py:
##########
@@ -0,0 +1,481 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,unused-variable,unused-argument
+"""Winograd template for Adreno backend"""
+
+import logging
+import tvm
+from tvm import te
+from tvm import autotvm
+
+from tvm.topi import nn
+from tvm.topi.utils import get_const_int, get_const_tuple, traverse_inline
+from ..nn.winograd_util import winograd_transform_matrices
+from .utils import (
+ split_to_chunks,
+ pack_input,
+ pack_filter,
+ bind_data_copy,
+ get_texture_storage,
+)
+
+
+logger = logging.getLogger("conv2d_nchw_winograd")
+
+
+def _infer_tile_size(data):
+ if len(data.shape) == 4:
+ N, CI, H, W = get_const_tuple(data.shape)
+ else:
+ N, CI, H, W, CB = get_const_tuple(data.shape)
+
+ if H % 8 == 0:
+ return 4
+ return 2
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd.image2d")
+def conv2d_nchw_winograd(cfg, data, kernel, strides, padding, dilation, out_dtype):
+ args = {"shared": False, "accumulator": "float16"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=False
+ )
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd_acc32.image2d")
+def conv2d_nchw_winograd_acc32(cfg, data, kernel, strides, padding, dilation, out_dtype):
+ args = {"shared": False, "accumulator": "float32"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=False
+ )
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd.image2d")
+def schedule_conv2d_nchw_winograd(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc16")
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd_acc32.image2d")
+def schedule_conv2d_nchw_winograd_acc32(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc32")
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd_without_weight_transform.image2d")
+def conv2d_nchw_winograd_without_weight_transform(
+ cfg, data, kernel, strides, padding, dilation, out_dtype
+):
+ args = {"shared": False, "accumulator": "float16"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=True
+ )
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd_without_weight_transform_acc32.image2d")
+def conv2d_nchw_winograd_without_weight_transform_acc32(
+ cfg, data, kernel, strides, padding, dilation, out_dtype
+):
+ args = {"shared": False, "accumulator": "float32"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=True
+ )
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd_without_weight_transform.image2d")
+def schedule_conv2d_nchw_winograd_without_weight_transform(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc16", pre_computed=True)
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd_without_weight_transform_acc32.image2d")
+def schedule_conv2d_nchw_winograd_without_weight_transform_acc32(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc32", pre_computed=True)
+
+
+def schedule_conv2d_nchw_winograd_impl(cfg, outs, tag, pre_computed=False):
+ outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
+ s = te.create_schedule([x.op for x in outs])
+
+ def _callback(op):
+ if op.tag == tag:
+ schedule_conv2d_winograd(cfg, s, op.output(0), pre_computed=pre_computed)
+
+ traverse_inline(s, outs[0].op, _callback)
+ return s
+
+
+def conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args, pre_computed
Review Comment:
1. `pre_computed` name is unclear - what was pre-computed?
2. Need to document this `pre_computed` param why it can be True and when it can be False
##########
python/tvm/topi/adreno/conv2d_nchw_winograd.py:
##########
@@ -0,0 +1,481 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,unused-variable,unused-argument
+"""Winograd template for Adreno backend"""
+
+import logging
+import tvm
+from tvm import te
+from tvm import autotvm
+
+from tvm.topi import nn
+from tvm.topi.utils import get_const_int, get_const_tuple, traverse_inline
+from ..nn.winograd_util import winograd_transform_matrices
+from .utils import (
+ split_to_chunks,
+ pack_input,
+ pack_filter,
+ bind_data_copy,
+ get_texture_storage,
+)
+
+
+logger = logging.getLogger("conv2d_nchw_winograd")
+
+
+def _infer_tile_size(data):
+ if len(data.shape) == 4:
+ N, CI, H, W = get_const_tuple(data.shape)
+ else:
+ N, CI, H, W, CB = get_const_tuple(data.shape)
+
+ if H % 8 == 0:
+ return 4
+ return 2
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd.image2d")
+def conv2d_nchw_winograd(cfg, data, kernel, strides, padding, dilation, out_dtype):
+ args = {"shared": False, "accumulator": "float16"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=False
+ )
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd_acc32.image2d")
+def conv2d_nchw_winograd_acc32(cfg, data, kernel, strides, padding, dilation, out_dtype):
+ args = {"shared": False, "accumulator": "float32"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=False
+ )
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd.image2d")
+def schedule_conv2d_nchw_winograd(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc16")
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd_acc32.image2d")
+def schedule_conv2d_nchw_winograd_acc32(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc32")
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd_without_weight_transform.image2d")
+def conv2d_nchw_winograd_without_weight_transform(
+ cfg, data, kernel, strides, padding, dilation, out_dtype
+):
+ args = {"shared": False, "accumulator": "float16"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=True
+ )
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd_without_weight_transform_acc32.image2d")
+def conv2d_nchw_winograd_without_weight_transform_acc32(
+ cfg, data, kernel, strides, padding, dilation, out_dtype
+):
+ args = {"shared": False, "accumulator": "float32"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=True
+ )
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd_without_weight_transform.image2d")
+def schedule_conv2d_nchw_winograd_without_weight_transform(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc16", pre_computed=True)
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd_without_weight_transform_acc32.image2d")
+def schedule_conv2d_nchw_winograd_without_weight_transform_acc32(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc32", pre_computed=True)
+
+
+def schedule_conv2d_nchw_winograd_impl(cfg, outs, tag, pre_computed=False):
+ outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
+ s = te.create_schedule([x.op for x in outs])
+
+ def _callback(op):
+ if op.tag == tag:
+ schedule_conv2d_winograd(cfg, s, op.output(0), pre_computed=pre_computed)
+
+ traverse_inline(s, outs[0].op, _callback)
+ return s
+
+
+def conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args, pre_computed
+):
+ """Compute declaration for winograd"""
+ tile_size = _infer_tile_size(data)
+
+ if isinstance(dilation, int):
+ dilation_h = dilation_w = dilation
+ else:
+ dilation_h, dilation_w = dilation
+ HSTR, WSTR = (strides, strides) if isinstance(strides, int) else strides
+
+ convert_from4d = False
+ if len(data.shape) == 4:
+ N, DCI, H, W = get_const_tuple(data.shape)
+ if not pre_computed:
+ out_channels, CI, KH, KW = get_const_tuple(kernel.shape)
+ else:
+ alpha, _, CI, out_channels = get_const_tuple(kernel.shape)
+ KH = KW = alpha + 1 - tile_size
+
+ in_channel_chunks, in_channel_block, in_channel_tail = split_to_chunks(CI, 4)
+ out_channel_chunks, out_channel_block, out_channel_tail = split_to_chunks(out_channels, 4)
+ if autotvm.GLOBAL_SCOPE.in_tuning is True:
+ dshape = (N, in_channel_chunks, H, W, in_channel_block)
+ data = tvm.te.placeholder(dshape, data.dtype, name="data_placeholder")
+ if not pre_computed: # kernel tensor is raw tensor, do strict check
+ kshape = (out_channel_chunks, CI, KH, KW, out_channel_block)
+ kernel = tvm.te.placeholder(kshape, kernel.dtype, name="kernel_placeholder")
+ else:
+ kshape = (alpha, alpha, CI, out_channel_chunks, out_channel_block)
+ kernel = tvm.te.placeholder(kshape, kernel.dtype, name="kernel_placeholder")
+ else:
+ convert_from4d = True
+ data = pack_input(
+ data, "NCHW", N, in_channel_chunks, in_channel_block, in_channel_tail, H, W
+ )
+ if not pre_computed: # kernel tensor is raw tensor, do strict check
+ kernel = pack_filter(
+ kernel,
+ "OIHW",
+ out_channel_chunks,
+ out_channel_block,
+ out_channel_tail,
+ CI,
+ in_channel_chunks,
+ in_channel_block,
+ in_channel_tail,
+ KH,
+ KW,
+ )
+ else:
+ kernel = pack_filter(
+ kernel,
+ "HWIO",
+ out_channel_chunks,
+ out_channel_block,
+ out_channel_tail,
+ CI,
+ in_channel_chunks,
+ in_channel_block,
+ in_channel_tail,
+ alpha,
+ alpha,
+ )
+ N, DCI, H, W, CB = get_const_tuple(data.shape)
+ if not pre_computed: # kernel tensor is raw tensor, do strict check
+ CO, CI, KH, KW, COB = get_const_tuple(kernel.shape)
+ alpha = KW + tile_size - 1
+ assert HSTR == 1 and WSTR == 1 and KH == KW
+ else:
+ alpha, _, CI, CO, COB = get_const_tuple(kernel.shape)
+ KH = KW = alpha + 1 - tile_size
+ assert HSTR == 1 and WSTR == 1 and dilation_h == 1 and dilation_w == 1
+
+ if isinstance(N, tvm.tir.Any):
+ N = tvm.te.size_var("n")
+
+ if not isinstance(H, int) or not isinstance(W, int):
+ raise RuntimeError(
+ "adreno winograd conv2d doesn't support dynamic input\
+ height or width."
+ )
+
+ pt, pl, pb, pr = nn.get_pad_tuple(padding, (KH, KW))
+ data_pad = nn.pad(data, (0, 0, pt, pl, 0), (0, 0, pb, pr, 0), name="data_pad")
+
+ r = KW
+ m = tile_size
+ A, B, G = winograd_transform_matrices(m, r, out_dtype)
+
+ H = (H + pt + pb - KH) // HSTR + 1
+ W = (W + pl + pr - KW) // WSTR + 1
+ nH, nW = (H + m - 1) // m, (W + m - 1) // m
+
+ P = N * nH * nW if isinstance(N, int) else nH * nW
+
+ # transform kernel
+ if not pre_computed:
+ r_kh = te.reduce_axis((0, KH), name="r_kh")
+ r_kw = te.reduce_axis((0, KW), name="r_kw")
+ kernel_pack = te.compute(
+ (alpha, alpha, CI, CO, COB),
+ lambda eps, nu, ci, co, cob: te.sum(
+ kernel[co][ci][r_kh][r_kw][cob] * G[eps][r_kh] * G[nu][r_kw], axis=[r_kh, r_kw]
+ ),
+ name="kernel_pack",
+ )
+ else:
+ kernel_pack = kernel
+
+ idxdiv = tvm.tir.indexdiv
+ idxmod = tvm.tir.indexmod
+ N, CI, H, W, CB = get_const_tuple(data.shape)
+
+ # pack input tile
+ input_tile = te.compute(
+ (alpha, alpha, CI, P, CB),
+ lambda eps, nu, c, p, cb: data_pad[idxdiv(p, (nH * nW))][c][
+ idxmod(idxdiv(p, nW), nH) * m + eps
+ ][idxmod(p, nW) * m + nu][cb],
+ name="d",
+ )
+
+ # transform data
+ r_a = te.reduce_axis((0, alpha), "r_a")
+ r_b = te.reduce_axis((0, alpha), "r_a")
+ data_pack = te.compute(
+ (P, CI, alpha, alpha, CB),
+ lambda p, ci, eps, nu, cb: te.sum(
+ input_tile[r_a][r_b][ci][p][cb] * B[r_a][eps] * B[r_b][nu], axis=[r_a, r_b]
+ ),
+ name="data_pack",
+ )
+
+ # repack transformed data
+ data_pack_trans = te.compute(
+ (alpha, alpha, CI, P, CB),
+ lambda eps, nu, c, p, cb: data_pack[p][c][eps][nu][cb],
+ name="data_pack_trans",
+ )
+
+ # do batch gemm
+ ci = te.reduce_axis((0, CI), name="ci")
+ cb = te.reduce_axis((0, CB), name="cb")
+ bgemm = te.compute(
+ (alpha, alpha, CO, P, COB),
+ lambda eps, nu, co, p, cob: te.sum(
+ (
+ kernel_pack[eps][nu][ci * CB + cb][co][cob] * data_pack_trans[eps][nu][ci][p][cb]
+ ).astype(args["accumulator"]),
+ axis=[ci, cb],
+ ),
+ name="bgemm",
+ )
+
+ # inverse transform
+ r_a = te.reduce_axis((0, alpha), "r_a")
+ r_b = te.reduce_axis((0, alpha), "r_a")
+ inverse = te.compute(
+ (CO, P, m, m, COB),
+ lambda co, p, vh, vw, cob: te.sum(
+ bgemm[r_a][r_b][co][p][cob] * (A[r_a][vh] * A[r_b][vw]).astype(args["accumulator"]),
+ axis=[r_a, r_b],
+ ),
+ name="inverse",
+ )
+
+ # output
+ if convert_from4d and autotvm.GLOBAL_SCOPE.in_tuning is False:
+ output = te.compute(
+ (N, out_channels, H, W),
+ lambda n, c, h, w: inverse[c // CB][n * nH * nW + idxdiv(h, m) * nW + idxdiv(w, m)][
+ idxmod(h, m)
+ ][idxmod(w, m)][c % CB].astype(out_dtype),
+ name="output",
+ tag="cast_from_acc" + args["accumulator"][-2:],
+ )
+ else:
+ output = te.compute(
+ (N, CO, H, W, COB),
+ lambda n, co, h, w, cob: inverse[co][n * nH * nW + idxdiv(h, m) * nW + idxdiv(w, m)][
+ idxmod(h, m)
+ ][idxmod(w, m)][cob].astype(out_dtype),
+ name="output",
+ tag="cast_from_acc" + args["accumulator"][-2:],
+ )
+
+ if isinstance(N, int):
+ cfg.add_flop(2 * N * CO * COB * H * W * CI * CB * KH * KW)
+
+ return output
+
+
+def schedule_conv2d_winograd(cfg, s, output, pre_computed):
+ """Schedule winograd template"""
+ inverse = s[output].op.input_tensors[0]
+ bgemm, A = s[inverse].op.input_tensors
+ kernel_pack, data_pack_trans = s[bgemm].op.input_tensors
+ data_pack = s[data_pack_trans].op.input_tensors[0]
+ input_tile, B = s[data_pack].op.input_tensors
+ pad_data = s[input_tile].op.input_tensors[0]
+
+ # data transform
+ s[B].compute_inline()
+ s[A].compute_inline()
+
+ # probably will improve real topology execution
+ if autotvm.GLOBAL_SCOPE.in_tuning:
+ # Padding to texture
+ AA = s.cache_read(pad_data, get_texture_storage(pad_data.shape), [input_tile])
+ bind_data_copy(s[AA])
+
+ s[input_tile].compute_inline()
+
+ OL = s.cache_write(data_pack, "local")
+ c, p, eps, nu, cb = s[data_pack].op.axis
+ fused = s[data_pack].fuse(c, p, eps, nu)
+ bx, tx = s[data_pack].split(fused, 128)
+ s[data_pack].vectorize(cb)
+ s[data_pack].bind(bx, te.thread_axis("blockIdx.x"))
+ s[data_pack].bind(tx, te.thread_axis("threadIdx.x"))
+
+ c, p, eps, nu, cb = s[OL].op.axis
+ r_a, r_b = s[OL].op.reduce_axis
+ s[OL].unroll(eps)
+ s[OL].unroll(nu)
+ s[OL].unroll(r_a)
+ s[OL].unroll(r_b)
+ s[OL].vectorize(cb)
+ s[OL].compute_at(s[data_pack], tx)
+ s[data_pack].set_scope(get_texture_storage(data_pack.shape))
+
+ s[data_pack_trans].compute_inline()
+
+ # transform kernel
+ if not pre_computed:
+ kernel, G = s[kernel_pack].op.input_tensors
+ eps, nu, ci, co, cob = s[kernel_pack].op.axis
+ if autotvm.GLOBAL_SCOPE.in_tuning:
+ # skip this part during tuning to make recrods accurate
+ # this part will be pre-computed during pre-compute optimization pass
+ s[G].pragma(s[G].op.axis[0], "debug_skip_region")
+ s[kernel_pack].pragma(eps, "debug_skip_region")
+ else:
+ s[G].compute_inline()
+ r_a, r_b = s[kernel_pack].op.reduce_axis
+ for axis in [eps, nu, r_a, r_b]:
+ s[kernel_pack].unroll(axis)
+
+ fused = s[kernel_pack].fuse(ci, co)
+ bb, tt = s[kernel_pack].split(fused, 128)
+ s[kernel_pack].reorder(bb, tt, eps, nu, r_a, r_b, cob)
+ s[kernel_pack].vectorize(cob)
+ s[kernel_pack].bind(bb, te.thread_axis("blockIdx.x"))
+ s[kernel_pack].bind(tt, te.thread_axis("threadIdx.x"))
+ else:
+ kernel = kernel_pack
+
+ if isinstance(kernel.op, tvm.te.ComputeOp) and "filter_pack" in kernel.op.tag:
+ # manage scheduling of datacopy
+ pack_data = pad_data.op.input_tensors[0]
+ bind_data_copy(s[pack_data])
+ bind_data_copy(s[kernel])
+ elif isinstance(kernel.op, tvm.te.ComputeOp) and "dilate" in kernel.op.tag:
+ s[kernel].compute_inline()
+ s[pad_data].compute_inline()
+
+ ##### space definition begin #####
+ cfg.define_knob("auto_unroll_max_step", [0, 4, 16])
+ b1, b2, y, x, cb = s[bgemm].op.axis
+ rcc = s[bgemm].op.reduce_axis[0]
+ alpha = get_const_int(b1.dom.extent)
+
+ cfg.define_split(
+ "tile_y", y, num_outputs=3, filter=lambda entry: entry.size[2] <= 64 and entry.size[1] <= 8
+ )
+ cfg.define_split(
+ "tile_x",
+ x,
+ num_outputs=3,
+ filter=lambda entry: entry.size[2] <= 64 and entry.size[1] >= 4 and entry.size[1] <= 8,
+ )
+ cfg.define_split("tile_rc", rcc, num_outputs=2)
+ # cfg.multi_filter(
+ # filter=lambda entity: entity["tile_y"].size[2] * entity["tile_x"].size[2] in range(32,1024)
+ # )
+ ##### space definition end #####
+
+ # batch gemm
+ OL = s.cache_write(bgemm, "local")
+ if (
+ autotvm.GLOBAL_SCOPE.in_tuning
+ or isinstance(kernel.op, tvm.te.ComputeOp)
+ and "filter_pack" in kernel.op.tag
+ ):
+ BB = s.cache_read(kernel_pack, get_texture_storage(kernel_pack.shape), [OL])
+ bind_data_copy(s[BB])
+
+ by = s[bgemm].fuse(b1, b2, y)
+
+ # tile and bind spatial axes
+ bgemm_scope, by = s[bgemm].split(by, nparts=1)
+ by, vy, ty = cfg["tile_y"].apply(s, bgemm, by)
+ bx, vx, tx = cfg["tile_x"].apply(s, bgemm, x)
+ s[bgemm].bind(by, te.thread_axis("blockIdx.y"))
+ s[bgemm].bind(bx, te.thread_axis("blockIdx.x"))
+ s[bgemm].bind(vy, te.thread_axis("vthread"))
+ s[bgemm].bind(vx, te.thread_axis("vthread"))
+ s[bgemm].bind(ty, te.thread_axis("threadIdx.y"))
+ s[bgemm].bind(tx, te.thread_axis("threadIdx.x"))
+ s[bgemm].reorder(bgemm_scope, by, bx, vy, vx, ty, tx, cb)
+ s[bgemm].vectorize(cb)
+ s[bgemm].set_scope(get_texture_storage(bgemm.shape))
+
+ # tile reduction axes
+ s[OL].compute_at(s[bgemm], tx)
+ b1, b2, y, x, cb = s[OL].op.axis
+ (rcc, rcb) = s[OL].op.reduce_axis
+ b = s[OL].fuse(b1, b2)
+ s[OL].reorder(b, y, x, rcc, rcb, cb)
+ # TODO: commented unroll improves performance of vgg16 for fp16_fp32
Review Comment:
better to remove this comment
##########
tests/cpp-runtime/opencl/opencl_texture_pool_test.cc:
##########
@@ -0,0 +1,148 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#include <gtest/gtest.h>
+#include <tvm/runtime/container/optional.h>
+
+#include "../src/runtime/opencl/opencl_common.h"
+#include "../src/runtime/texture.h"
+
+using namespace tvm::runtime;
+using namespace tvm::runtime::cl;
+
+class PoolWrapper : public Pool {
Review Comment:
add comment why this class was required
##########
python/tvm/topi/adreno/conv2d_nchw_winograd.py:
##########
@@ -0,0 +1,481 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,unused-variable,unused-argument
+"""Winograd template for Adreno backend"""
+
+import logging
+import tvm
+from tvm import te
+from tvm import autotvm
+
+from tvm.topi import nn
+from tvm.topi.utils import get_const_int, get_const_tuple, traverse_inline
+from ..nn.winograd_util import winograd_transform_matrices
+from .utils import (
+ split_to_chunks,
+ pack_input,
+ pack_filter,
+ bind_data_copy,
+ get_texture_storage,
+)
+
+
+logger = logging.getLogger("conv2d_nchw_winograd")
+
+
+def _infer_tile_size(data):
Review Comment:
add description
##########
tests/python/contrib/test_opencl/conftest.py:
##########
@@ -0,0 +1,37 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+""" OpenCL testing fixtures used to deduce testing argument
+ values from testing parameters """
+
+
+import pytest
+
+import tvm
+import tvm.testing
+
+pytest_plugins = [
+ "tvm.contrib.hexagon.pytest_plugin",
+]
+# def pytest_addoption(parser):
Review Comment:
are these comments required here?
##########
python/tvm/topi/adreno/conv2d_nchw_winograd.py:
##########
@@ -0,0 +1,481 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,unused-variable,unused-argument
+"""Winograd template for Adreno backend"""
+
+import logging
+import tvm
+from tvm import te
+from tvm import autotvm
+
+from tvm.topi import nn
+from tvm.topi.utils import get_const_int, get_const_tuple, traverse_inline
+from ..nn.winograd_util import winograd_transform_matrices
+from .utils import (
+ split_to_chunks,
+ pack_input,
+ pack_filter,
+ bind_data_copy,
+ get_texture_storage,
+)
+
+
+logger = logging.getLogger("conv2d_nchw_winograd")
+
+
+def _infer_tile_size(data):
+ if len(data.shape) == 4:
+ N, CI, H, W = get_const_tuple(data.shape)
+ else:
+ N, CI, H, W, CB = get_const_tuple(data.shape)
+
+ if H % 8 == 0:
+ return 4
+ return 2
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd.image2d")
+def conv2d_nchw_winograd(cfg, data, kernel, strides, padding, dilation, out_dtype):
+ args = {"shared": False, "accumulator": "float16"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=False
+ )
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd_acc32.image2d")
+def conv2d_nchw_winograd_acc32(cfg, data, kernel, strides, padding, dilation, out_dtype):
+ args = {"shared": False, "accumulator": "float32"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=False
+ )
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd.image2d")
+def schedule_conv2d_nchw_winograd(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc16")
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd_acc32.image2d")
+def schedule_conv2d_nchw_winograd_acc32(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc32")
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd_without_weight_transform.image2d")
+def conv2d_nchw_winograd_without_weight_transform(
+ cfg, data, kernel, strides, padding, dilation, out_dtype
+):
+ args = {"shared": False, "accumulator": "float16"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=True
+ )
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd_without_weight_transform_acc32.image2d")
+def conv2d_nchw_winograd_without_weight_transform_acc32(
+ cfg, data, kernel, strides, padding, dilation, out_dtype
+):
+ args = {"shared": False, "accumulator": "float32"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=True
+ )
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd_without_weight_transform.image2d")
+def schedule_conv2d_nchw_winograd_without_weight_transform(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc16", pre_computed=True)
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd_without_weight_transform_acc32.image2d")
+def schedule_conv2d_nchw_winograd_without_weight_transform_acc32(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc32", pre_computed=True)
+
+
+def schedule_conv2d_nchw_winograd_impl(cfg, outs, tag, pre_computed=False):
+ outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
+ s = te.create_schedule([x.op for x in outs])
+
+ def _callback(op):
+ if op.tag == tag:
+ schedule_conv2d_winograd(cfg, s, op.output(0), pre_computed=pre_computed)
+
+ traverse_inline(s, outs[0].op, _callback)
+ return s
+
+
+def conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args, pre_computed
+):
+ """Compute declaration for winograd"""
+ tile_size = _infer_tile_size(data)
+
+ if isinstance(dilation, int):
+ dilation_h = dilation_w = dilation
+ else:
+ dilation_h, dilation_w = dilation
+ HSTR, WSTR = (strides, strides) if isinstance(strides, int) else strides
+
+ convert_from4d = False
+ if len(data.shape) == 4:
+ N, DCI, H, W = get_const_tuple(data.shape)
+ if not pre_computed:
+ out_channels, CI, KH, KW = get_const_tuple(kernel.shape)
+ else:
+ alpha, _, CI, out_channels = get_const_tuple(kernel.shape)
+ KH = KW = alpha + 1 - tile_size
+
+ in_channel_chunks, in_channel_block, in_channel_tail = split_to_chunks(CI, 4)
+ out_channel_chunks, out_channel_block, out_channel_tail = split_to_chunks(out_channels, 4)
+ if autotvm.GLOBAL_SCOPE.in_tuning is True:
+ dshape = (N, in_channel_chunks, H, W, in_channel_block)
+ data = tvm.te.placeholder(dshape, data.dtype, name="data_placeholder")
+ if not pre_computed: # kernel tensor is raw tensor, do strict check
+ kshape = (out_channel_chunks, CI, KH, KW, out_channel_block)
+ kernel = tvm.te.placeholder(kshape, kernel.dtype, name="kernel_placeholder")
+ else:
+ kshape = (alpha, alpha, CI, out_channel_chunks, out_channel_block)
+ kernel = tvm.te.placeholder(kshape, kernel.dtype, name="kernel_placeholder")
+ else:
+ convert_from4d = True
+ data = pack_input(
+ data, "NCHW", N, in_channel_chunks, in_channel_block, in_channel_tail, H, W
+ )
+ if not pre_computed: # kernel tensor is raw tensor, do strict check
+ kernel = pack_filter(
+ kernel,
+ "OIHW",
+ out_channel_chunks,
+ out_channel_block,
+ out_channel_tail,
+ CI,
+ in_channel_chunks,
+ in_channel_block,
+ in_channel_tail,
+ KH,
+ KW,
+ )
+ else:
+ kernel = pack_filter(
+ kernel,
+ "HWIO",
+ out_channel_chunks,
+ out_channel_block,
+ out_channel_tail,
+ CI,
+ in_channel_chunks,
+ in_channel_block,
+ in_channel_tail,
+ alpha,
+ alpha,
+ )
+ N, DCI, H, W, CB = get_const_tuple(data.shape)
+ if not pre_computed: # kernel tensor is raw tensor, do strict check
+ CO, CI, KH, KW, COB = get_const_tuple(kernel.shape)
+ alpha = KW + tile_size - 1
+ assert HSTR == 1 and WSTR == 1 and KH == KW
+ else:
+ alpha, _, CI, CO, COB = get_const_tuple(kernel.shape)
+ KH = KW = alpha + 1 - tile_size
+ assert HSTR == 1 and WSTR == 1 and dilation_h == 1 and dilation_w == 1
+
+ if isinstance(N, tvm.tir.Any):
+ N = tvm.te.size_var("n")
+
+ if not isinstance(H, int) or not isinstance(W, int):
+ raise RuntimeError(
+ "adreno winograd conv2d doesn't support dynamic input\
+ height or width."
+ )
+
+ pt, pl, pb, pr = nn.get_pad_tuple(padding, (KH, KW))
+ data_pad = nn.pad(data, (0, 0, pt, pl, 0), (0, 0, pb, pr, 0), name="data_pad")
+
+ r = KW
+ m = tile_size
+ A, B, G = winograd_transform_matrices(m, r, out_dtype)
+
+ H = (H + pt + pb - KH) // HSTR + 1
+ W = (W + pl + pr - KW) // WSTR + 1
+ nH, nW = (H + m - 1) // m, (W + m - 1) // m
+
+ P = N * nH * nW if isinstance(N, int) else nH * nW
+
+ # transform kernel
+ if not pre_computed:
+ r_kh = te.reduce_axis((0, KH), name="r_kh")
+ r_kw = te.reduce_axis((0, KW), name="r_kw")
+ kernel_pack = te.compute(
+ (alpha, alpha, CI, CO, COB),
+ lambda eps, nu, ci, co, cob: te.sum(
+ kernel[co][ci][r_kh][r_kw][cob] * G[eps][r_kh] * G[nu][r_kw], axis=[r_kh, r_kw]
+ ),
+ name="kernel_pack",
+ )
+ else:
+ kernel_pack = kernel
+
+ idxdiv = tvm.tir.indexdiv
+ idxmod = tvm.tir.indexmod
+ N, CI, H, W, CB = get_const_tuple(data.shape)
+
+ # pack input tile
+ input_tile = te.compute(
+ (alpha, alpha, CI, P, CB),
+ lambda eps, nu, c, p, cb: data_pad[idxdiv(p, (nH * nW))][c][
+ idxmod(idxdiv(p, nW), nH) * m + eps
+ ][idxmod(p, nW) * m + nu][cb],
+ name="d",
+ )
+
+ # transform data
+ r_a = te.reduce_axis((0, alpha), "r_a")
+ r_b = te.reduce_axis((0, alpha), "r_a")
+ data_pack = te.compute(
+ (P, CI, alpha, alpha, CB),
+ lambda p, ci, eps, nu, cb: te.sum(
+ input_tile[r_a][r_b][ci][p][cb] * B[r_a][eps] * B[r_b][nu], axis=[r_a, r_b]
+ ),
+ name="data_pack",
+ )
+
+ # repack transformed data
+ data_pack_trans = te.compute(
+ (alpha, alpha, CI, P, CB),
+ lambda eps, nu, c, p, cb: data_pack[p][c][eps][nu][cb],
+ name="data_pack_trans",
+ )
+
+ # do batch gemm
+ ci = te.reduce_axis((0, CI), name="ci")
+ cb = te.reduce_axis((0, CB), name="cb")
+ bgemm = te.compute(
+ (alpha, alpha, CO, P, COB),
+ lambda eps, nu, co, p, cob: te.sum(
+ (
+ kernel_pack[eps][nu][ci * CB + cb][co][cob] * data_pack_trans[eps][nu][ci][p][cb]
+ ).astype(args["accumulator"]),
+ axis=[ci, cb],
+ ),
+ name="bgemm",
+ )
+
+ # inverse transform
+ r_a = te.reduce_axis((0, alpha), "r_a")
+ r_b = te.reduce_axis((0, alpha), "r_a")
+ inverse = te.compute(
+ (CO, P, m, m, COB),
+ lambda co, p, vh, vw, cob: te.sum(
+ bgemm[r_a][r_b][co][p][cob] * (A[r_a][vh] * A[r_b][vw]).astype(args["accumulator"]),
+ axis=[r_a, r_b],
+ ),
+ name="inverse",
+ )
+
+ # output
+ if convert_from4d and autotvm.GLOBAL_SCOPE.in_tuning is False:
+ output = te.compute(
+ (N, out_channels, H, W),
+ lambda n, c, h, w: inverse[c // CB][n * nH * nW + idxdiv(h, m) * nW + idxdiv(w, m)][
+ idxmod(h, m)
+ ][idxmod(w, m)][c % CB].astype(out_dtype),
+ name="output",
+ tag="cast_from_acc" + args["accumulator"][-2:],
+ )
+ else:
+ output = te.compute(
+ (N, CO, H, W, COB),
+ lambda n, co, h, w, cob: inverse[co][n * nH * nW + idxdiv(h, m) * nW + idxdiv(w, m)][
+ idxmod(h, m)
+ ][idxmod(w, m)][cob].astype(out_dtype),
+ name="output",
+ tag="cast_from_acc" + args["accumulator"][-2:],
+ )
+
+ if isinstance(N, int):
+ cfg.add_flop(2 * N * CO * COB * H * W * CI * CB * KH * KW)
+
+ return output
+
+
+def schedule_conv2d_winograd(cfg, s, output, pre_computed):
+ """Schedule winograd template"""
+ inverse = s[output].op.input_tensors[0]
+ bgemm, A = s[inverse].op.input_tensors
+ kernel_pack, data_pack_trans = s[bgemm].op.input_tensors
+ data_pack = s[data_pack_trans].op.input_tensors[0]
+ input_tile, B = s[data_pack].op.input_tensors
+ pad_data = s[input_tile].op.input_tensors[0]
+
+ # data transform
+ s[B].compute_inline()
+ s[A].compute_inline()
+
+ # probably will improve real topology execution
+ if autotvm.GLOBAL_SCOPE.in_tuning:
+ # Padding to texture
+ AA = s.cache_read(pad_data, get_texture_storage(pad_data.shape), [input_tile])
+ bind_data_copy(s[AA])
+
+ s[input_tile].compute_inline()
+
+ OL = s.cache_write(data_pack, "local")
+ c, p, eps, nu, cb = s[data_pack].op.axis
+ fused = s[data_pack].fuse(c, p, eps, nu)
+ bx, tx = s[data_pack].split(fused, 128)
+ s[data_pack].vectorize(cb)
+ s[data_pack].bind(bx, te.thread_axis("blockIdx.x"))
+ s[data_pack].bind(tx, te.thread_axis("threadIdx.x"))
+
+ c, p, eps, nu, cb = s[OL].op.axis
+ r_a, r_b = s[OL].op.reduce_axis
+ s[OL].unroll(eps)
+ s[OL].unroll(nu)
+ s[OL].unroll(r_a)
+ s[OL].unroll(r_b)
+ s[OL].vectorize(cb)
+ s[OL].compute_at(s[data_pack], tx)
+ s[data_pack].set_scope(get_texture_storage(data_pack.shape))
+
+ s[data_pack_trans].compute_inline()
+
+ # transform kernel
+ if not pre_computed:
+ kernel, G = s[kernel_pack].op.input_tensors
+ eps, nu, ci, co, cob = s[kernel_pack].op.axis
+ if autotvm.GLOBAL_SCOPE.in_tuning:
+ # skip this part during tuning to make recrods accurate
+ # this part will be pre-computed during pre-compute optimization pass
+ s[G].pragma(s[G].op.axis[0], "debug_skip_region")
+ s[kernel_pack].pragma(eps, "debug_skip_region")
+ else:
+ s[G].compute_inline()
+ r_a, r_b = s[kernel_pack].op.reduce_axis
+ for axis in [eps, nu, r_a, r_b]:
+ s[kernel_pack].unroll(axis)
+
+ fused = s[kernel_pack].fuse(ci, co)
+ bb, tt = s[kernel_pack].split(fused, 128)
+ s[kernel_pack].reorder(bb, tt, eps, nu, r_a, r_b, cob)
+ s[kernel_pack].vectorize(cob)
+ s[kernel_pack].bind(bb, te.thread_axis("blockIdx.x"))
+ s[kernel_pack].bind(tt, te.thread_axis("threadIdx.x"))
+ else:
+ kernel = kernel_pack
+
+ if isinstance(kernel.op, tvm.te.ComputeOp) and "filter_pack" in kernel.op.tag:
+ # manage scheduling of datacopy
+ pack_data = pad_data.op.input_tensors[0]
+ bind_data_copy(s[pack_data])
+ bind_data_copy(s[kernel])
+ elif isinstance(kernel.op, tvm.te.ComputeOp) and "dilate" in kernel.op.tag:
+ s[kernel].compute_inline()
+ s[pad_data].compute_inline()
+
+ ##### space definition begin #####
Review Comment:
why space definition is so deep in schedule? don't we tune anything in previous stages?
##########
python/tvm/topi/adreno/conv2d_nchw_winograd.py:
##########
@@ -0,0 +1,481 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,unused-variable,unused-argument
+"""Winograd template for Adreno backend"""
+
+import logging
+import tvm
+from tvm import te
+from tvm import autotvm
+
+from tvm.topi import nn
+from tvm.topi.utils import get_const_int, get_const_tuple, traverse_inline
+from ..nn.winograd_util import winograd_transform_matrices
+from .utils import (
+ split_to_chunks,
+ pack_input,
+ pack_filter,
+ bind_data_copy,
+ get_texture_storage,
+)
+
+
+logger = logging.getLogger("conv2d_nchw_winograd")
+
+
+def _infer_tile_size(data):
+ if len(data.shape) == 4:
+ N, CI, H, W = get_const_tuple(data.shape)
+ else:
+ N, CI, H, W, CB = get_const_tuple(data.shape)
+
+ if H % 8 == 0:
+ return 4
+ return 2
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd.image2d")
+def conv2d_nchw_winograd(cfg, data, kernel, strides, padding, dilation, out_dtype):
+ args = {"shared": False, "accumulator": "float16"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=False
+ )
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd_acc32.image2d")
+def conv2d_nchw_winograd_acc32(cfg, data, kernel, strides, padding, dilation, out_dtype):
+ args = {"shared": False, "accumulator": "float32"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=False
+ )
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd.image2d")
+def schedule_conv2d_nchw_winograd(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc16")
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd_acc32.image2d")
+def schedule_conv2d_nchw_winograd_acc32(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc32")
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd_without_weight_transform.image2d")
+def conv2d_nchw_winograd_without_weight_transform(
+ cfg, data, kernel, strides, padding, dilation, out_dtype
+):
+ args = {"shared": False, "accumulator": "float16"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=True
+ )
+
+
+@autotvm.register_topi_compute("conv2d_nchw_winograd_without_weight_transform_acc32.image2d")
+def conv2d_nchw_winograd_without_weight_transform_acc32(
+ cfg, data, kernel, strides, padding, dilation, out_dtype
+):
+ args = {"shared": False, "accumulator": "float32"}
+ return conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args=args, pre_computed=True
+ )
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd_without_weight_transform.image2d")
+def schedule_conv2d_nchw_winograd_without_weight_transform(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc16", pre_computed=True)
+
+
+@autotvm.register_topi_schedule("conv2d_nchw_winograd_without_weight_transform_acc32.image2d")
+def schedule_conv2d_nchw_winograd_without_weight_transform_acc32(cfg, outs):
+ return schedule_conv2d_nchw_winograd_impl(cfg, outs, tag="cast_from_acc32", pre_computed=True)
+
+
+def schedule_conv2d_nchw_winograd_impl(cfg, outs, tag, pre_computed=False):
+ outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
+ s = te.create_schedule([x.op for x in outs])
+
+ def _callback(op):
+ if op.tag == tag:
+ schedule_conv2d_winograd(cfg, s, op.output(0), pre_computed=pre_computed)
+
+ traverse_inline(s, outs[0].op, _callback)
+ return s
+
+
+def conv2d_nchw_winograd_comp(
+ cfg, data, kernel, strides, padding, dilation, out_dtype, args, pre_computed
+):
+ """Compute declaration for winograd"""
+ tile_size = _infer_tile_size(data)
+
+ if isinstance(dilation, int):
+ dilation_h = dilation_w = dilation
+ else:
+ dilation_h, dilation_w = dilation
+ HSTR, WSTR = (strides, strides) if isinstance(strides, int) else strides
+
+ convert_from4d = False
+ if len(data.shape) == 4:
+ N, DCI, H, W = get_const_tuple(data.shape)
+ if not pre_computed:
+ out_channels, CI, KH, KW = get_const_tuple(kernel.shape)
+ else:
+ alpha, _, CI, out_channels = get_const_tuple(kernel.shape)
+ KH = KW = alpha + 1 - tile_size
+
+ in_channel_chunks, in_channel_block, in_channel_tail = split_to_chunks(CI, 4)
+ out_channel_chunks, out_channel_block, out_channel_tail = split_to_chunks(out_channels, 4)
+ if autotvm.GLOBAL_SCOPE.in_tuning is True:
+ dshape = (N, in_channel_chunks, H, W, in_channel_block)
+ data = tvm.te.placeholder(dshape, data.dtype, name="data_placeholder")
+ if not pre_computed: # kernel tensor is raw tensor, do strict check
+ kshape = (out_channel_chunks, CI, KH, KW, out_channel_block)
+ kernel = tvm.te.placeholder(kshape, kernel.dtype, name="kernel_placeholder")
+ else:
+ kshape = (alpha, alpha, CI, out_channel_chunks, out_channel_block)
+ kernel = tvm.te.placeholder(kshape, kernel.dtype, name="kernel_placeholder")
+ else:
+ convert_from4d = True
+ data = pack_input(
+ data, "NCHW", N, in_channel_chunks, in_channel_block, in_channel_tail, H, W
+ )
+ if not pre_computed: # kernel tensor is raw tensor, do strict check
+ kernel = pack_filter(
+ kernel,
+ "OIHW",
+ out_channel_chunks,
+ out_channel_block,
+ out_channel_tail,
+ CI,
+ in_channel_chunks,
+ in_channel_block,
+ in_channel_tail,
+ KH,
+ KW,
+ )
+ else:
+ kernel = pack_filter(
+ kernel,
+ "HWIO",
+ out_channel_chunks,
+ out_channel_block,
+ out_channel_tail,
+ CI,
+ in_channel_chunks,
+ in_channel_block,
+ in_channel_tail,
+ alpha,
+ alpha,
+ )
+ N, DCI, H, W, CB = get_const_tuple(data.shape)
+ if not pre_computed: # kernel tensor is raw tensor, do strict check
+ CO, CI, KH, KW, COB = get_const_tuple(kernel.shape)
+ alpha = KW + tile_size - 1
+ assert HSTR == 1 and WSTR == 1 and KH == KW
+ else:
+ alpha, _, CI, CO, COB = get_const_tuple(kernel.shape)
+ KH = KW = alpha + 1 - tile_size
+ assert HSTR == 1 and WSTR == 1 and dilation_h == 1 and dilation_w == 1
+
+ if isinstance(N, tvm.tir.Any):
+ N = tvm.te.size_var("n")
+
+ if not isinstance(H, int) or not isinstance(W, int):
+ raise RuntimeError(
+ "adreno winograd conv2d doesn't support dynamic input\
+ height or width."
+ )
+
+ pt, pl, pb, pr = nn.get_pad_tuple(padding, (KH, KW))
+ data_pad = nn.pad(data, (0, 0, pt, pl, 0), (0, 0, pb, pr, 0), name="data_pad")
+
+ r = KW
+ m = tile_size
+ A, B, G = winograd_transform_matrices(m, r, out_dtype)
+
+ H = (H + pt + pb - KH) // HSTR + 1
+ W = (W + pl + pr - KW) // WSTR + 1
+ nH, nW = (H + m - 1) // m, (W + m - 1) // m
+
+ P = N * nH * nW if isinstance(N, int) else nH * nW
+
+ # transform kernel
+ if not pre_computed:
+ r_kh = te.reduce_axis((0, KH), name="r_kh")
+ r_kw = te.reduce_axis((0, KW), name="r_kw")
+ kernel_pack = te.compute(
+ (alpha, alpha, CI, CO, COB),
+ lambda eps, nu, ci, co, cob: te.sum(
+ kernel[co][ci][r_kh][r_kw][cob] * G[eps][r_kh] * G[nu][r_kw], axis=[r_kh, r_kw]
+ ),
+ name="kernel_pack",
+ )
+ else:
+ kernel_pack = kernel
+
+ idxdiv = tvm.tir.indexdiv
+ idxmod = tvm.tir.indexmod
+ N, CI, H, W, CB = get_const_tuple(data.shape)
+
+ # pack input tile
+ input_tile = te.compute(
+ (alpha, alpha, CI, P, CB),
+ lambda eps, nu, c, p, cb: data_pad[idxdiv(p, (nH * nW))][c][
+ idxmod(idxdiv(p, nW), nH) * m + eps
+ ][idxmod(p, nW) * m + nu][cb],
+ name="d",
+ )
+
+ # transform data
+ r_a = te.reduce_axis((0, alpha), "r_a")
+ r_b = te.reduce_axis((0, alpha), "r_a")
+ data_pack = te.compute(
+ (P, CI, alpha, alpha, CB),
+ lambda p, ci, eps, nu, cb: te.sum(
+ input_tile[r_a][r_b][ci][p][cb] * B[r_a][eps] * B[r_b][nu], axis=[r_a, r_b]
+ ),
+ name="data_pack",
+ )
+
+ # repack transformed data
+ data_pack_trans = te.compute(
+ (alpha, alpha, CI, P, CB),
+ lambda eps, nu, c, p, cb: data_pack[p][c][eps][nu][cb],
+ name="data_pack_trans",
+ )
+
+ # do batch gemm
+ ci = te.reduce_axis((0, CI), name="ci")
+ cb = te.reduce_axis((0, CB), name="cb")
+ bgemm = te.compute(
+ (alpha, alpha, CO, P, COB),
+ lambda eps, nu, co, p, cob: te.sum(
+ (
+ kernel_pack[eps][nu][ci * CB + cb][co][cob] * data_pack_trans[eps][nu][ci][p][cb]
+ ).astype(args["accumulator"]),
+ axis=[ci, cb],
+ ),
+ name="bgemm",
+ )
+
+ # inverse transform
+ r_a = te.reduce_axis((0, alpha), "r_a")
+ r_b = te.reduce_axis((0, alpha), "r_a")
+ inverse = te.compute(
+ (CO, P, m, m, COB),
+ lambda co, p, vh, vw, cob: te.sum(
+ bgemm[r_a][r_b][co][p][cob] * (A[r_a][vh] * A[r_b][vw]).astype(args["accumulator"]),
+ axis=[r_a, r_b],
+ ),
+ name="inverse",
+ )
+
+ # output
+ if convert_from4d and autotvm.GLOBAL_SCOPE.in_tuning is False:
+ output = te.compute(
+ (N, out_channels, H, W),
+ lambda n, c, h, w: inverse[c // CB][n * nH * nW + idxdiv(h, m) * nW + idxdiv(w, m)][
+ idxmod(h, m)
+ ][idxmod(w, m)][c % CB].astype(out_dtype),
+ name="output",
+ tag="cast_from_acc" + args["accumulator"][-2:],
+ )
+ else:
+ output = te.compute(
+ (N, CO, H, W, COB),
+ lambda n, co, h, w, cob: inverse[co][n * nH * nW + idxdiv(h, m) * nW + idxdiv(w, m)][
+ idxmod(h, m)
+ ][idxmod(w, m)][cob].astype(out_dtype),
+ name="output",
+ tag="cast_from_acc" + args["accumulator"][-2:],
+ )
+
+ if isinstance(N, int):
+ cfg.add_flop(2 * N * CO * COB * H * W * CI * CB * KH * KW)
+
+ return output
+
+
+def schedule_conv2d_winograd(cfg, s, output, pre_computed):
+ """Schedule winograd template"""
+ inverse = s[output].op.input_tensors[0]
+ bgemm, A = s[inverse].op.input_tensors
+ kernel_pack, data_pack_trans = s[bgemm].op.input_tensors
+ data_pack = s[data_pack_trans].op.input_tensors[0]
+ input_tile, B = s[data_pack].op.input_tensors
+ pad_data = s[input_tile].op.input_tensors[0]
+
+ # data transform
+ s[B].compute_inline()
+ s[A].compute_inline()
+
+ # probably will improve real topology execution
+ if autotvm.GLOBAL_SCOPE.in_tuning:
+ # Padding to texture
+ AA = s.cache_read(pad_data, get_texture_storage(pad_data.shape), [input_tile])
+ bind_data_copy(s[AA])
+
+ s[input_tile].compute_inline()
+
+ OL = s.cache_write(data_pack, "local")
+ c, p, eps, nu, cb = s[data_pack].op.axis
+ fused = s[data_pack].fuse(c, p, eps, nu)
+ bx, tx = s[data_pack].split(fused, 128)
+ s[data_pack].vectorize(cb)
+ s[data_pack].bind(bx, te.thread_axis("blockIdx.x"))
+ s[data_pack].bind(tx, te.thread_axis("threadIdx.x"))
+
+ c, p, eps, nu, cb = s[OL].op.axis
+ r_a, r_b = s[OL].op.reduce_axis
+ s[OL].unroll(eps)
+ s[OL].unroll(nu)
+ s[OL].unroll(r_a)
+ s[OL].unroll(r_b)
+ s[OL].vectorize(cb)
+ s[OL].compute_at(s[data_pack], tx)
+ s[data_pack].set_scope(get_texture_storage(data_pack.shape))
+
+ s[data_pack_trans].compute_inline()
+
+ # transform kernel
+ if not pre_computed:
+ kernel, G = s[kernel_pack].op.input_tensors
+ eps, nu, ci, co, cob = s[kernel_pack].op.axis
+ if autotvm.GLOBAL_SCOPE.in_tuning:
+ # skip this part during tuning to make recrods accurate
+ # this part will be pre-computed during pre-compute optimization pass
+ s[G].pragma(s[G].op.axis[0], "debug_skip_region")
+ s[kernel_pack].pragma(eps, "debug_skip_region")
+ else:
+ s[G].compute_inline()
+ r_a, r_b = s[kernel_pack].op.reduce_axis
+ for axis in [eps, nu, r_a, r_b]:
+ s[kernel_pack].unroll(axis)
+
+ fused = s[kernel_pack].fuse(ci, co)
+ bb, tt = s[kernel_pack].split(fused, 128)
+ s[kernel_pack].reorder(bb, tt, eps, nu, r_a, r_b, cob)
+ s[kernel_pack].vectorize(cob)
+ s[kernel_pack].bind(bb, te.thread_axis("blockIdx.x"))
+ s[kernel_pack].bind(tt, te.thread_axis("threadIdx.x"))
+ else:
+ kernel = kernel_pack
+
+ if isinstance(kernel.op, tvm.te.ComputeOp) and "filter_pack" in kernel.op.tag:
+ # manage scheduling of datacopy
+ pack_data = pad_data.op.input_tensors[0]
+ bind_data_copy(s[pack_data])
+ bind_data_copy(s[kernel])
+ elif isinstance(kernel.op, tvm.te.ComputeOp) and "dilate" in kernel.op.tag:
+ s[kernel].compute_inline()
+ s[pad_data].compute_inline()
+
+ ##### space definition begin #####
+ cfg.define_knob("auto_unroll_max_step", [0, 4, 16])
+ b1, b2, y, x, cb = s[bgemm].op.axis
+ rcc = s[bgemm].op.reduce_axis[0]
+ alpha = get_const_int(b1.dom.extent)
+
+ cfg.define_split(
+ "tile_y", y, num_outputs=3, filter=lambda entry: entry.size[2] <= 64 and entry.size[1] <= 8
+ )
+ cfg.define_split(
+ "tile_x",
+ x,
+ num_outputs=3,
+ filter=lambda entry: entry.size[2] <= 64 and entry.size[1] >= 4 and entry.size[1] <= 8,
+ )
+ cfg.define_split("tile_rc", rcc, num_outputs=2)
+ # cfg.multi_filter(
Review Comment:
no multi_filter so far, better to remove mentioning, or add TODO
##########
python/tvm/topi/adreno/conv2d_nhwc_winograd.py:
##########
@@ -0,0 +1,480 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,unused-variable,unused-argument
+"""Winograd template for Adreno backend"""
Review Comment:
same as in previous file
##########
tests/python/relay/test_conv2d_nchw_texture.py:
##########
@@ -392,3 +392,81 @@ def test_conv2d_yolov3_v2_nchw_3c():
}
build_run_compare(mod, params, {"data": input_shape}, dtype, target)
+
+
+def test_conv2d_vgg16_winograd():
Review Comment:
can we verify in the test if winograd was selected? I.e. take json after compilation and look for winograd in the functin names?
##########
src/runtime/texture.h:
##########
@@ -94,6 +94,25 @@ inline bool IsTextureStorage(std::string scope) {
return scope.find("texture") != std::string::npos;
}
+class TVM_DLL Pool {
Review Comment:
Pool -> TexturePool? since this class is responsible only for caching of textures, not buffers
Since we are moving to header :)
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