comaniac commented on a change in pull request #9737:
URL: https://github.com/apache/tvm/pull/9737#discussion_r769005791
##########
File path: python/tvm/contrib/cutlass/gen_conv2d.py
##########
@@ -121,27 +131,70 @@ def get_default(self, out_dtype):
data_type = gemm_profile_result["data_type"]
return create_conv2d_operator([tile_description], data_type,
[alignment])[0]
+ def check_align(self, op_name, C, K):
+ """Filter out kernels that cannot be supported."""
+ aligns = re.findall(r"align[1|2|4|8]", op_name)
+ assert len(aligns) == 1
+ align = int(aligns[0][-1])
+ return all([dim % align == 0 for dim in [C, K]])
+
def profile(
- self, d_shape, w_shape, out_shape, out_dtype, profile_all=True,
use_multiprocessing=False
+ self,
+ d_shape,
+ w_shape,
+ padding,
+ stride,
+ dilation,
+ out_dtype,
+ profile_all=True,
+ use_multiprocessing=False,
):
"""Profile and select the best kernel from candidate kernels.
If profile_all is False, return immediately after the first applicable
kernel is found.
If use_multiprocessing is True, compile all profiler executables in
parallel.
"""
- B, _, _, IC = d_shape
+ N, H, W, IC = d_shape
OC, R, S, _ = w_shape
- _, P, Q, _ = out_shape
+ workload = (
+ N,
+ H,
+ W,
+ IC,
+ OC,
+ R,
+ S,
+ padding[0],
+ padding[1],
+ stride[0],
+ stride[1],
+ dilation[0],
+ dilation[1],
+ )
- M = B * P * Q
- N = OC
- K = R * S * IC
+ if workload in self.cache:
+ return self.cache[workload]
- gemm_profile_result = self.gemm_profiler.profile(
- M, N, K, out_dtype, profile_all=profile_all,
use_multiprocessing=use_multiprocessing
- )
+ ops = GENERATOR_FUNC_TABLE[self.sm](out_dtype,
op_creator=create_conv2d_operator)
+ ops = list(filter(lambda op: self.check_align(op["name"], IC, OC),
ops))
- tile_description = gemm_profile_result["tile_description"]
- alignment = gemm_profile_result["alignment"]
- data_type = gemm_profile_result["data_type"]
+ for op in ops:
+ op["runtime"] = -1
- return create_conv2d_operator([tile_description], data_type,
[alignment])[0]
+ if profile_all:
+ self.engine.compile_all(ops, use_multiprocessing)
+
+ args = (
+ "--n=%d --h=%d --w=%d --c=%d --k=%d --r=%d --s=%d --pad_h=%d
--pad_w=%d "
+ "--stride_h=%d --stride_w=%d --dilation_h=%d --dilation_w=%d"
+ ) % workload
+
+ for op in ops:
+ out = self.engine.evaluate(op, args.split(" "))
+ op["runtime"] = out
+ if out > 0 and profile_all is False:
Review comment:
nit
```suggestion
if out > 0 and not profile_all:
```
##########
File path: python/tvm/contrib/cutlass/conv2d_profiler.py
##########
@@ -0,0 +1,163 @@
+# 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=import-outside-toplevel, invalid-name
+"""Instantiate a C++ source for profiling CUTLASS kernels."""
+
+
+class Conv2dProfilerEmitter(object):
Review comment:
I raised this topic before in the GEMM profiler PR, but I agreed with
@masahi that it seems not much to share and CUTLASS basically only supports
GEMM and Conv2D. Accordingly, it might be a bit overkill to have a common base
class at least for now.
##########
File path: python/tvm/contrib/cutlass/gen_conv2d.py
##########
@@ -121,27 +131,70 @@ def get_default(self, out_dtype):
data_type = gemm_profile_result["data_type"]
return create_conv2d_operator([tile_description], data_type,
[alignment])[0]
+ def check_align(self, op_name, C, K):
+ """Filter out kernels that cannot be supported."""
+ aligns = re.findall(r"align[1|2|4|8]", op_name)
+ assert len(aligns) == 1
+ align = int(aligns[0][-1])
+ return all([dim % align == 0 for dim in [C, K]])
+
def profile(
- self, d_shape, w_shape, out_shape, out_dtype, profile_all=True,
use_multiprocessing=False
+ self,
+ d_shape,
+ w_shape,
+ padding,
+ stride,
+ dilation,
+ out_dtype,
+ profile_all=True,
+ use_multiprocessing=False,
):
"""Profile and select the best kernel from candidate kernels.
If profile_all is False, return immediately after the first applicable
kernel is found.
If use_multiprocessing is True, compile all profiler executables in
parallel.
"""
- B, _, _, IC = d_shape
+ N, H, W, IC = d_shape
OC, R, S, _ = w_shape
- _, P, Q, _ = out_shape
+ workload = (
+ N,
+ H,
+ W,
+ IC,
+ OC,
+ R,
+ S,
+ padding[0],
+ padding[1],
+ stride[0],
+ stride[1],
+ dilation[0],
+ dilation[1],
+ )
- M = B * P * Q
- N = OC
- K = R * S * IC
+ if workload in self.cache:
+ return self.cache[workload]
- gemm_profile_result = self.gemm_profiler.profile(
- M, N, K, out_dtype, profile_all=profile_all,
use_multiprocessing=use_multiprocessing
- )
+ ops = GENERATOR_FUNC_TABLE[self.sm](out_dtype,
op_creator=create_conv2d_operator)
+ ops = list(filter(lambda op: self.check_align(op["name"], IC, OC),
ops))
- tile_description = gemm_profile_result["tile_description"]
- alignment = gemm_profile_result["alignment"]
- data_type = gemm_profile_result["data_type"]
+ for op in ops:
+ op["runtime"] = -1
- return create_conv2d_operator([tile_description], data_type,
[alignment])[0]
+ if profile_all:
+ self.engine.compile_all(ops, use_multiprocessing)
+
+ args = (
+ "--n=%d --h=%d --w=%d --c=%d --k=%d --r=%d --s=%d --pad_h=%d
--pad_w=%d "
+ "--stride_h=%d --stride_w=%d --dilation_h=%d --dilation_w=%d"
+ ) % workload
+
+ for op in ops:
+ out = self.engine.evaluate(op, args.split(" "))
+ op["runtime"] = out
+ if out > 0 and profile_all is False:
+ break
+
+ valid_ops = filter(lambda op: op["runtime"] > 0, ops)
+ output = sorted(valid_ops, key=lambda i: i["runtime"])
Review comment:
Looks like you could just `output = min(valid_ops, key=lambda i:
i["runtime"])`. Moreover, if you directly set the invalid runtime to
`float("inf")` after `self.engine.evaluate`, you could also get rid of the
filter.
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