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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/06/06 05:55:54 UTC
[GitHub] [tvm] csullivan opened a new pull request, #11589: [TE] Support schedulable TIR compute definitions in TOPI
csullivan opened a new pull request, #11589:
URL: https://github.com/apache/tvm/pull/11589
This PR adds `te.extern_primfunc` which provides the interface around TE ExternOp that allows a TVMScript defined schedulable TIR PrimFunc to be inlined into a TE compute graph. The result is that TIR can be used for compute definitions in Relay OpStrategies and, paired with meta-scheduler support in relay as introduced in #10578, these compute definitions can be scheduled and tuned as demonstrated in the attached tests.
Prior to this, compute definitions were limited to those definable in TE only. As a consequence of this patch and ongoing improvements to TVMScript meta-programming (#11097), TOPI can be extended to include compute and scheduling functions targeting schedulable TIR uniformly.
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[GitHub] [tvm] csullivan commented on a diff in pull request #11589: [TE] Support schedulable TIR compute definitions in TOPI
Posted by GitBox <gi...@apache.org>.
csullivan commented on code in PR #11589:
URL: https://github.com/apache/tvm/pull/11589#discussion_r892899360
##########
tests/python/unittest/test_meta_schedule_relay_tir_compute.py:
##########
@@ -0,0 +1,191 @@
+# 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.
+import numpy as np
+import tvm
+import tvm.testing
+import tvm.topi.testing
+
+from tvm.script import tir as T
+from tvm import tir, te, relay, topi, autotvm
+from tvm.relay.testing.temp_op_attr import TempOpAttr
+from tvm.meta_schedule import ApplyHistoryBest
+from tvm.meta_schedule.testing import apply_fixed_schedules
+
+
+def compute_tir_conv2d_nchw_oihw(data_shape, weight_shape, dtype):
+ assert dtype == "float32"
+ OC, IC, FH, FW = weight_shape
+
+ padding = (0, 0, 0, 0)
+ strides = (1, 1)
+ dilation = (1, 1)
+ output_shape = (
+ data_shape[0],
+ weight_shape[0],
+ (data_shape[2] - ((weight_shape[2] - 1) * dilation[0] + 1) + padding[0] + padding[1])
+ // strides[0]
+ + 1,
+ (data_shape[3] - ((weight_shape[3] - 1) * dilation[1] + 1) + padding[2] + padding[3])
+ // strides[1]
+ + 1,
+ )
+ N, K, BH, BW = output_shape
+
+ @T.prim_func
+ def conv2d(
+ a: T.handle,
+ filt: T.handle,
+ b: T.handle,
+ ) -> None:
+ T.func_attr({"global_symbol": "main", "tir.noalias": True})
+ A = T.match_buffer(a, data_shape, dtype=dtype)
+ Filter = T.match_buffer(filt, weight_shape, dtype=dtype)
+ B = T.match_buffer(b, output_shape, dtype=dtype)
Review Comment:
Thanks for the suggestion, and agreed that it looked a bit weird. I've made the change.
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[GitHub] [tvm] junrushao1994 commented on pull request #11589: [TE] Support schedulable TIR compute definitions in TOPI
Posted by GitBox <gi...@apache.org>.
junrushao1994 commented on PR #11589:
URL: https://github.com/apache/tvm/pull/11589#issuecomment-1150774199
@Hzfengsy for context, as a rule of thumb, usually we don't use "requested changes" if the comment is not emotional :-) in our case, it's just some syntactic nitpicking, so let's just use "comment" as review type
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[GitHub] [tvm] masahi commented on pull request #11589: [TE] Support schedulable TIR compute definitions in TOPI
Posted by GitBox <gi...@apache.org>.
masahi commented on PR #11589:
URL: https://github.com/apache/tvm/pull/11589#issuecomment-1147855415
cc @Hzfengsy
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[GitHub] [tvm] Hzfengsy commented on a diff in pull request #11589: [TE] Support schedulable TIR compute definitions in TOPI
Posted by GitBox <gi...@apache.org>.
Hzfengsy commented on code in PR #11589:
URL: https://github.com/apache/tvm/pull/11589#discussion_r893112264
##########
tests/python/unittest/test_tir_te_extern_primfunc.py:
##########
@@ -0,0 +1,257 @@
+# 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.
+
+import sys
+import pytest
+import numpy as np
+
+import tvm
+import tvm.testing
+from tvm import tir, te, TVMError
+from tvm.script import tir as T
+from tvm.arith import _ffi_api as _ffi_arith_api
+from tvm.tir.schedule import _ffi_api as _ffi_schedule_api
+
+
+# TODO(csullivan): Additional tests cases needed:
+# - PrimFunc with 1 arg, inplace update
+# - PrimFunc with buffer that uses custom storage_scope
+
+
+@T.prim_func
+def func_1(A: T.Buffer[(16,), "float32"], C: T.Buffer[(1,), "float32"]):
+ for i in T.serial(
+ 0,
+ 16,
+ ):
+ with T.block():
+ B = T.alloc_buffer((1,), dtype="float32")
+ with T.block():
+ B[0] = A[i] * T.float32(2)
+ with T.block():
+ C[0] = C[0] + A[i] + B[0] + T.float32(1)
+ A[i] = B[0] + T.float32(1)
+
+
+def verify_func_1(module):
+ a_np = np.random.randint(low=-128, high=127, size=(16,)).astype(np.float32)
+ c_np = np.zeros((1,), dtype=np.float32)
+ a = tvm.nd.array(a_np, device=tvm.cpu(0))
+ c = tvm.nd.array(c_np, device=tvm.cpu(0))
+
+ module(a, c)
+ tvm.testing.assert_allclose(c_np + np.sum(3 * a_np + 1), c.numpy(), rtol=1e-4)
+ # also test in place update
+ tvm.testing.assert_allclose(a_np * 2 + 1, a.numpy(), rtol=1e-4)
+
+
+@T.prim_func
+def func_2(
+ C: T.Buffer[(1,), "float32"], A: T.Buffer[(16,), "float32"], D: T.Buffer[(2,), "float32"]
+):
+ for i in T.serial(
+ 0,
+ 16,
+ ):
+ with T.block():
+ B = T.alloc_buffer((1,), dtype="float32")
+ with T.block():
+ B[0] = A[i] * T.float32(2)
+ with T.block():
+ C[0] = C[0] + A[i] + B[0] + T.float32(1) + D[0]
+ A[i] = B[0] + T.float32(1) + D[1]
+
+
+def verify_func_2(module):
+ a_np = np.random.randint(low=-128, high=127, size=(16,)).astype(np.float32)
+ d_np = np.random.randint(low=-128, high=127, size=(2,)).astype(np.float32)
+ c_np = np.zeros((1,), dtype=np.float32)
+ a = tvm.nd.array(a_np, device=tvm.cpu(0))
+ d = tvm.nd.array(d_np, device=tvm.cpu(0))
+ c = tvm.nd.array(c_np, device=tvm.cpu(0))
+
+ module(c, a, d)
+ tvm.testing.assert_allclose(c_np + np.sum(3 * a_np + 1 + d_np[0]), c.numpy(), rtol=1e-4)
+ tvm.testing.assert_allclose(a_np * 2 + 1 + d_np[1], a.numpy(), rtol=1e-4)
+
+
+@T.prim_func
+def func_3(
+ C: T.Buffer[(1,), "float32"],
+ A: T.Buffer[(16,), "float32"],
+ D: T.Buffer[(2,), "float32"],
+ E: T.Buffer[(16,), "float32"],
+ F: T.Buffer[(16,), "float32"],
+):
+ for i in T.serial(
+ 0,
+ 16,
+ ):
+ with T.block():
+ B = T.alloc_buffer((1,), dtype="float32")
+ with T.block():
+ B[0] = A[i] * T.float32(2)
+ with T.block():
+ E[i] = A[i]
+ F[i] = E[i] + 1.0
+ C[0] = C[0] + A[i] + B[0] + T.float32(1) + D[0]
+ A[i] = B[0] + T.float32(1) + D[1]
+
+
+def verify_func_3(module):
+ a_np = np.random.randint(low=-128, high=127, size=(16,)).astype(np.float32)
+ d_np = np.random.randint(low=-128, high=127, size=(2,)).astype(np.float32)
+ c_np = np.zeros((1,), dtype=np.float32)
+ e_np = np.zeros((16,), dtype=np.float32)
+ f_np = np.zeros((16,), dtype=np.float32)
+ a = tvm.nd.array(a_np, device=tvm.cpu(0))
+ d = tvm.nd.array(d_np, device=tvm.cpu(0))
+ c = tvm.nd.array(c_np, device=tvm.cpu(0))
+ e = tvm.nd.array(e_np, device=tvm.cpu(0))
+ f = tvm.nd.array(f_np, device=tvm.cpu(0))
+
+ module(c, a, d, e, f)
+ tvm.testing.assert_allclose(c_np + np.sum(3 * a_np + 1 + d_np[0]), c.numpy(), rtol=1e-4)
+ tvm.testing.assert_allclose(a_np * 2 + 1 + d_np[1], a.numpy(), rtol=1e-4)
+ tvm.testing.assert_allclose(a_np, e.numpy(), rtol=1e-4)
+ tvm.testing.assert_allclose(a_np + 1, f.numpy(), rtol=1e-4)
+
+
+@T.prim_func
+def func_4(
+ C: T.Buffer[(1,), "float32"],
+ A: T.Buffer[(16,), "float32"],
+ F: T.Buffer[(16,), "float32"],
+ D: T.Buffer[(2,), "float32"],
+ E: T.Buffer[(16,), "float32"],
+):
+ for i in T.serial(
+ 0,
+ 16,
+ ):
Review Comment:
```suggestion
for i in range(16):
```
##########
tests/python/unittest/test_tir_te_extern_primfunc.py:
##########
@@ -0,0 +1,257 @@
+# 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.
+
+import sys
+import pytest
+import numpy as np
+
+import tvm
+import tvm.testing
+from tvm import tir, te, TVMError
+from tvm.script import tir as T
+from tvm.arith import _ffi_api as _ffi_arith_api
+from tvm.tir.schedule import _ffi_api as _ffi_schedule_api
+
+
+# TODO(csullivan): Additional tests cases needed:
+# - PrimFunc with 1 arg, inplace update
+# - PrimFunc with buffer that uses custom storage_scope
+
+
+@T.prim_func
+def func_1(A: T.Buffer[(16,), "float32"], C: T.Buffer[(1,), "float32"]):
+ for i in T.serial(
+ 0,
+ 16,
+ ):
Review Comment:
```suggestion
for i in range(16):
```
##########
tests/python/unittest/test_tir_te_extern_primfunc.py:
##########
@@ -0,0 +1,257 @@
+# 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.
+
+import sys
+import pytest
+import numpy as np
+
+import tvm
+import tvm.testing
+from tvm import tir, te, TVMError
+from tvm.script import tir as T
+from tvm.arith import _ffi_api as _ffi_arith_api
+from tvm.tir.schedule import _ffi_api as _ffi_schedule_api
+
+
+# TODO(csullivan): Additional tests cases needed:
+# - PrimFunc with 1 arg, inplace update
+# - PrimFunc with buffer that uses custom storage_scope
+
+
+@T.prim_func
+def func_1(A: T.Buffer[(16,), "float32"], C: T.Buffer[(1,), "float32"]):
+ for i in T.serial(
+ 0,
+ 16,
+ ):
+ with T.block():
+ B = T.alloc_buffer((1,), dtype="float32")
+ with T.block():
+ B[0] = A[i] * T.float32(2)
+ with T.block():
+ C[0] = C[0] + A[i] + B[0] + T.float32(1)
+ A[i] = B[0] + T.float32(1)
+
+
+def verify_func_1(module):
+ a_np = np.random.randint(low=-128, high=127, size=(16,)).astype(np.float32)
+ c_np = np.zeros((1,), dtype=np.float32)
+ a = tvm.nd.array(a_np, device=tvm.cpu(0))
+ c = tvm.nd.array(c_np, device=tvm.cpu(0))
+
+ module(a, c)
+ tvm.testing.assert_allclose(c_np + np.sum(3 * a_np + 1), c.numpy(), rtol=1e-4)
+ # also test in place update
+ tvm.testing.assert_allclose(a_np * 2 + 1, a.numpy(), rtol=1e-4)
+
+
+@T.prim_func
+def func_2(
+ C: T.Buffer[(1,), "float32"], A: T.Buffer[(16,), "float32"], D: T.Buffer[(2,), "float32"]
+):
+ for i in T.serial(
+ 0,
+ 16,
+ ):
Review Comment:
```suggestion
for i in range(16):
```
##########
tests/python/unittest/test_tir_te_extern_primfunc.py:
##########
@@ -0,0 +1,257 @@
+# 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.
+
+import sys
+import pytest
+import numpy as np
+
+import tvm
+import tvm.testing
+from tvm import tir, te, TVMError
+from tvm.script import tir as T
+from tvm.arith import _ffi_api as _ffi_arith_api
+from tvm.tir.schedule import _ffi_api as _ffi_schedule_api
+
+
+# TODO(csullivan): Additional tests cases needed:
+# - PrimFunc with 1 arg, inplace update
+# - PrimFunc with buffer that uses custom storage_scope
+
+
+@T.prim_func
+def func_1(A: T.Buffer[(16,), "float32"], C: T.Buffer[(1,), "float32"]):
+ for i in T.serial(
+ 0,
+ 16,
+ ):
+ with T.block():
+ B = T.alloc_buffer((1,), dtype="float32")
+ with T.block():
+ B[0] = A[i] * T.float32(2)
+ with T.block():
+ C[0] = C[0] + A[i] + B[0] + T.float32(1)
+ A[i] = B[0] + T.float32(1)
+
+
+def verify_func_1(module):
+ a_np = np.random.randint(low=-128, high=127, size=(16,)).astype(np.float32)
+ c_np = np.zeros((1,), dtype=np.float32)
+ a = tvm.nd.array(a_np, device=tvm.cpu(0))
+ c = tvm.nd.array(c_np, device=tvm.cpu(0))
+
+ module(a, c)
+ tvm.testing.assert_allclose(c_np + np.sum(3 * a_np + 1), c.numpy(), rtol=1e-4)
+ # also test in place update
+ tvm.testing.assert_allclose(a_np * 2 + 1, a.numpy(), rtol=1e-4)
+
+
+@T.prim_func
+def func_2(
+ C: T.Buffer[(1,), "float32"], A: T.Buffer[(16,), "float32"], D: T.Buffer[(2,), "float32"]
+):
+ for i in T.serial(
+ 0,
+ 16,
+ ):
+ with T.block():
+ B = T.alloc_buffer((1,), dtype="float32")
+ with T.block():
+ B[0] = A[i] * T.float32(2)
+ with T.block():
+ C[0] = C[0] + A[i] + B[0] + T.float32(1) + D[0]
+ A[i] = B[0] + T.float32(1) + D[1]
+
+
+def verify_func_2(module):
+ a_np = np.random.randint(low=-128, high=127, size=(16,)).astype(np.float32)
+ d_np = np.random.randint(low=-128, high=127, size=(2,)).astype(np.float32)
+ c_np = np.zeros((1,), dtype=np.float32)
+ a = tvm.nd.array(a_np, device=tvm.cpu(0))
+ d = tvm.nd.array(d_np, device=tvm.cpu(0))
+ c = tvm.nd.array(c_np, device=tvm.cpu(0))
+
+ module(c, a, d)
+ tvm.testing.assert_allclose(c_np + np.sum(3 * a_np + 1 + d_np[0]), c.numpy(), rtol=1e-4)
+ tvm.testing.assert_allclose(a_np * 2 + 1 + d_np[1], a.numpy(), rtol=1e-4)
+
+
+@T.prim_func
+def func_3(
+ C: T.Buffer[(1,), "float32"],
+ A: T.Buffer[(16,), "float32"],
+ D: T.Buffer[(2,), "float32"],
+ E: T.Buffer[(16,), "float32"],
+ F: T.Buffer[(16,), "float32"],
+):
+ for i in T.serial(
+ 0,
+ 16,
+ ):
Review Comment:
```suggestion
for i in range(16):
```
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[GitHub] [tvm] Hzfengsy commented on a diff in pull request #11589: [TE] Support schedulable TIR compute definitions in TOPI
Posted by GitBox <gi...@apache.org>.
Hzfengsy commented on code in PR #11589:
URL: https://github.com/apache/tvm/pull/11589#discussion_r890772118
##########
src/arith/domain_touched.cc:
##########
@@ -34,18 +35,48 @@ namespace arith {
using namespace tir;
+struct LoadAccess {
Review Comment:
Can we use `using LoadAccess = std::vector<std::vector<IntSet>> set;`?
##########
src/te/operation/create_primfunc.cc:
##########
@@ -395,62 +395,75 @@ Stmt GenerateStmtFromExternOp(const te::ExternOp& extern_op, CreateFuncInfo* inf
/*annotations=*/extern_op->attrs));
}
-PrimFunc CreatePrimFunc(const Array<te::Tensor>& arg_list) {
- // Step 1. Create tensor read graph.
+Array<te::Operation> CollectOrderedOps(const Array<te::Tensor>& arg_list) {
Array<te::Operation> arg_ops;
for (const te::Tensor& arg : arg_list) {
arg_ops.push_back(arg->op);
}
te::ReadGraph g = te::CreateReadGraph(arg_ops);
Array<te::Operation> order = te::PostDFSOrder(arg_ops, g);
- // Step 2. Checking all Operations are supported.
for (const te::Operation& op : order) {
if (!(op->IsInstance<te::PlaceholderOpNode>() || op->IsInstance<te::ComputeOpNode>() ||
op->IsInstance<te::ExternOpNode>()))
LOG(FATAL) << "TypeError: Unsupported Operation: " << op->GetTypeKey() << ". "
<< "Only te.placeholder and te.compute are allowed for now.";
}
+ return order;
+}
- // Infomations used in CreatePrimFunc and its sub-functions.
- CreateFuncInfo info(arg_list);
- // Root body stmts.
- Array<Stmt> root_stmts;
- // Analyzer
- arith::Analyzer analyzer;
+void InitializeBufferBinds(const Array<te::Operation>& ordered_ops, CreateFuncInfo* info) {
+ // Process any TE operations which contain user defined buffers
+ for (const auto& op : ordered_ops) {
+ // Initialize the tensor2buffer binds map with buffers defined by the te.extern
+ if (const auto extern_op = op.as<te::ExternOpNode>()) {
Review Comment:
```suggestion
if (const auto* extern_op = op.as<te::ExternOpNode>()) {
```
##########
tests/python/unittest/test_meta_schedule_relay_tir_compute.py:
##########
@@ -0,0 +1,191 @@
+# 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.
+import numpy as np
+import tvm
+import tvm.testing
+import tvm.topi.testing
+
+from tvm.script import tir as T
+from tvm import tir, te, relay, topi, autotvm
+from tvm.relay.testing.temp_op_attr import TempOpAttr
+from tvm.meta_schedule import ApplyHistoryBest
+from tvm.meta_schedule.testing import apply_fixed_schedules
+
+
+def compute_tir_conv2d_nchw_oihw(data_shape, weight_shape, dtype):
+ assert dtype == "float32"
+ OC, IC, FH, FW = weight_shape
+
+ padding = (0, 0, 0, 0)
+ strides = (1, 1)
+ dilation = (1, 1)
+ output_shape = (
+ data_shape[0],
+ weight_shape[0],
+ (data_shape[2] - ((weight_shape[2] - 1) * dilation[0] + 1) + padding[0] + padding[1])
+ // strides[0]
+ + 1,
+ (data_shape[3] - ((weight_shape[3] - 1) * dilation[1] + 1) + padding[2] + padding[3])
+ // strides[1]
+ + 1,
+ )
+ N, K, BH, BW = output_shape
+
+ @T.prim_func
+ def conv2d(
+ a: T.handle,
+ filt: T.handle,
+ b: T.handle,
+ ) -> None:
+ T.func_attr({"global_symbol": "main", "tir.noalias": True})
+ A = T.match_buffer(a, data_shape, dtype=dtype)
+ Filter = T.match_buffer(filt, weight_shape, dtype=dtype)
+ B = T.match_buffer(b, output_shape, dtype=dtype)
Review Comment:
It looks wired after `black` formatting. You can add `# fmt: off` and `# fmt: on` to turn off formatting for a part of python codes
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[GitHub] [tvm] csullivan commented on a diff in pull request #11589: [TE] Support schedulable TIR compute definitions in TOPI
Posted by GitBox <gi...@apache.org>.
csullivan commented on code in PR #11589:
URL: https://github.com/apache/tvm/pull/11589#discussion_r892894778
##########
src/arith/domain_touched.cc:
##########
@@ -34,18 +35,48 @@ namespace arith {
using namespace tir;
+struct LoadAccess {
Review Comment:
I'm currently using the struct type as sugar for compile-time lookup with std::get to index into the std::tuple. That said I took your advice an added an alias for the buffer touches. Happy to do it whichever way is preferable, I just found the tuple to be nice here.
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[GitHub] [tvm] Hzfengsy commented on a diff in pull request #11589: [TE] Support schedulable TIR compute definitions in TOPI
Posted by GitBox <gi...@apache.org>.
Hzfengsy commented on code in PR #11589:
URL: https://github.com/apache/tvm/pull/11589#discussion_r893110963
##########
src/arith/domain_touched.cc:
##########
@@ -34,18 +35,48 @@ namespace arith {
using namespace tir;
+struct LoadAccess {
Review Comment:
I don't have strong opinions about this. Both approaches are clear enough!
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[GitHub] [tvm] junrushao1994 merged pull request #11589: [TE] Support schedulable TIR compute definitions in TOPI
Posted by GitBox <gi...@apache.org>.
junrushao1994 merged PR #11589:
URL: https://github.com/apache/tvm/pull/11589
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[GitHub] [tvm] Hzfengsy commented on pull request #11589: [TE] Support schedulable TIR compute definitions in TOPI
Posted by GitBox <gi...@apache.org>.
Hzfengsy commented on PR #11589:
URL: https://github.com/apache/tvm/pull/11589#issuecomment-1151828407
Thanks for the reminder of @junrushao1994. And I'm sorry for bringing ambiguity to @csullivan. I will change the `Changes requested` into `Approve` and we can merge it if we want.
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[GitHub] [tvm] csullivan commented on pull request #11589: [TE] Support schedulable TIR compute definitions in TOPI
Posted by GitBox <gi...@apache.org>.
csullivan commented on PR #11589:
URL: https://github.com/apache/tvm/pull/11589#issuecomment-1154081448
Thanks @Hzfengsy. I think I prefer `T.serial` personally but I continue to be impressed by the TVMScript parser. I agree the linter made this a bit ugly. I want to add some additional tests in a follow up so I'll happily address the formatting then and consider the change to `range`.
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[GitHub] [tvm] Hzfengsy commented on pull request #11589: [TE] Support schedulable TIR compute definitions in TOPI
Posted by GitBox <gi...@apache.org>.
Hzfengsy commented on PR #11589:
URL: https://github.com/apache/tvm/pull/11589#issuecomment-1154089285
> Thanks @Hzfengsy. I think I prefer `T.serial` personally but I continue to be impressed by the TVMScript parser. I agree the linter made this a bit ugly. I want to add some additional tests in a follow up so I'll happily address the formatting then and consider the change to `range`.
`T.serial` is good too! Just make it in one line, e.g. `for i in T.serial(0, 16):` :)
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