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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/06/24 22:58:11 UTC
[GitHub] [tvm] Lunderberg opened a new pull request #8336: [Topi][Unittests] Parametrized tests in `test_topi_dense.py`, split out gpu-independent implementations
Lunderberg opened a new pull request #8336:
URL: https://github.com/apache/tvm/pull/8336
[Topi][UnitTests] Parametrized tests in test_topi_dense.py
Now, tests run for multiple data types, can be extended with additional datatypes.
[Topi] Separated generic-gpu nn.dense implementations into topi.gpu.dense
As a follow-up to the renaming of "gpu" to "cuda", separating implementations that require CUDA (e.g. dense_cublas.cuda) from implementations that require any GPU, but not necessarily a CUDA GPU (e.g. dense_small_batch.gpu).
My intent is to pair this migration with the extension of unit tests to cover additional GPU runtimes, migrating only implementations that run correctly on non-CUDA GPU devices.
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[GitHub] [tvm] jcf94 merged pull request #8336: [Topi][Unittests] Parametrized tests in `test_topi_dense.py`, split out gpu-independent implementations
Posted by GitBox <gi...@apache.org>.
jcf94 merged pull request #8336:
URL: https://github.com/apache/tvm/pull/8336
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[GitHub] [tvm] jcf94 commented on a change in pull request #8336: [Topi][Unittests] Parametrized tests in `test_topi_dense.py`, split out gpu-independent implementations
Posted by GitBox <gi...@apache.org>.
jcf94 commented on a change in pull request #8336:
URL: https://github.com/apache/tvm/pull/8336#discussion_r659451517
##########
File path: python/tvm/topi/gpu/dense.py
##########
@@ -0,0 +1,218 @@
+# 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-argument
+"""Schedule for dense operator"""
+
+import logging
+
+from tvm import autotvm, te
+from tvm.autotvm.task.space import SplitEntity
+
+from .. import nn
+from ..utils import traverse_inline, get_const_tuple
+
+logger = logging.getLogger("topi")
+
+
+@autotvm.register_topi_compute("dense_small_batch.gpu")
+def dense_small_batch(cfg, data, weight, bias=None, out_dtype=None):
+ """Dense operator on CUDA"""
Review comment:
```suggestion
"""Dense operator on gpu."""
```
##########
File path: tests/python/topi/python/test_topi_dense.py
##########
@@ -43,108 +54,115 @@
}
-def verify_dense(batch, in_dim, out_dim, use_bias=True):
- A = te.placeholder((batch, in_dim), name="A")
- B = te.placeholder((out_dim, in_dim), name="B")
- C = te.placeholder((out_dim,), name="C")
- dtype = A.dtype
-
- # use memoize to pickle the test data for next time use
- @memoize("topi.tests.test_topi_dense")
- def get_ref_data():
- a_np = np.random.uniform(size=(batch, in_dim)).astype(dtype)
- b_np = np.random.uniform(size=(out_dim, in_dim)).astype(dtype)
- c_np = np.random.uniform(size=(out_dim,)).astype(dtype)
- if use_bias:
- d_np = np.maximum(np.dot(a_np, b_np.T) + c_np, 0.0)
- else:
- d_np = np.maximum(np.dot(a_np, b_np.T), 0.0)
- return (a_np, b_np, c_np, d_np)
-
- # get the test data
- a_np, b_np, c_np, d_np = get_ref_data()
-
- def check_device(device, dev):
- print("Running on target: %s" % device)
- for fcompute, fschedule in tvm.topi.testing.dispatch(device, _dense_implement):
- with tvm.target.Target(device):
- D = fcompute(A, B, C if use_bias else None)
- D = topi.nn.relu(D)
- s = fschedule([D])
- a = tvm.nd.array(a_np, dev)
- b = tvm.nd.array(b_np, dev)
- c = tvm.nd.array(c_np, dev)
- d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), dev)
- f = tvm.build(s, [A, B, C, D], device, name="dense")
- f(a, b, c, d)
- tvm.testing.assert_allclose(d.numpy(), d_np, rtol=1e-5)
-
- for device, dev in tvm.testing.enabled_targets():
- check_device(device, dev)
-
-
-def verify_dense_int8(batch, in_dim, out_dim, use_bias=True):
- dtype = "int8"
- out_dtype = "int32"
- A = te.placeholder((batch, in_dim), name="A", dtype=dtype)
- B = te.placeholder((out_dim, in_dim), name="B", dtype=dtype)
+@tvm.testing.fixture(cache_return_value=True)
+def dense_ref_data(batch_size, in_dim, out_dim, use_bias, in_dtype, out_dtype):
+ if "float" in in_dtype:
+ a_np = np.random.uniform(size=(batch_size, in_dim)).astype(in_dtype)
+ b_np = np.random.uniform(size=(out_dim, in_dim)).astype(in_dtype)
+ c_np = np.random.uniform(size=(out_dim,)).astype(out_dtype)
+ elif in_dtype == "int8":
+ a_np = np.random.randint(low=-128, high=127, size=(batch_size, in_dim)).astype(in_dtype)
+ b_np = np.random.randint(low=-128, high=127, size=(out_dim, in_dim)).astype(in_dtype)
+ c_np = np.random.randint(low=-128, high=127, size=(out_dim,)).astype(out_dtype)
+ else:
+ raise ValueError("No method to generate test data for data type '{}'".format(in_dtype))
+
+ matmul = np.dot(a_np.astype(out_dtype), b_np.T.astype(out_dtype))
+
+ if use_bias:
+ matmul += c_np
+
+ d_np = np.maximum(matmul, 0)
+ return (a_np, b_np, c_np, d_np)
+
+
+def test_dense(
Review comment:
I'm thinking that wheather this can test different targets as you expected.
Shouldn't this function be decorated with "@tvm.testing.parametrize_targets"?
##########
File path: python/tvm/topi/gpu/dense.py
##########
@@ -0,0 +1,218 @@
+# 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-argument
+"""Schedule for dense operator"""
+
+import logging
+
+from tvm import autotvm, te
+from tvm.autotvm.task.space import SplitEntity
+
+from .. import nn
+from ..utils import traverse_inline, get_const_tuple
+
+logger = logging.getLogger("topi")
+
+
+@autotvm.register_topi_compute("dense_small_batch.gpu")
+def dense_small_batch(cfg, data, weight, bias=None, out_dtype=None):
+ """Dense operator on CUDA"""
+ return nn.dense(data, weight, bias, out_dtype)
+
+
+@autotvm.register_topi_schedule("dense_small_batch.gpu")
+def schedule_dense_small_batch(cfg, outs):
+ """Schedule float32/64 dense with small batch size"""
+ 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 == "dense":
+ _schedule_dense_small_batch(cfg, s, op.output(0))
+
+ traverse_inline(s, outs[0].op, _callback)
+ return s
+
+
+def _schedule_dense_small_batch(cfg, s, C):
+ A, weights = C.op.input_tensors
+ _, in_dim_weights = get_const_tuple(weights.shape)
+ _, in_dim_A = get_const_tuple(A.shape)
+
+ if isinstance(in_dim_A, int):
+ in_dim = in_dim_A
+ elif isinstance(in_dim_weights, int):
+ in_dim = in_dim_weights
+ else:
+ in_dim = None
+
+ if in_dim is not None:
+ cfg.define_split("tile_k", in_dim, num_outputs=2)
+ if cfg.is_fallback:
+ cfg["tile_k"] = SplitEntity([-1, 64] if in_dim > 64 else [1, 64])
+ _, kf = cfg["tile_k"].apply(s, C, C.op.reduce_axis[0])
+ else:
+ tile_k = 64
+ _, kf = s[C].split(C.op.reduce_axis[0], tile_k)
+
+ CF = s.rfactor(C, kf)
+
+ if C.op in s.outputs:
+ Out = C
+ else:
+ Out = s.outputs[0].output(0)
+ s[C].compute_at(s[Out], s[Out].op.axis[1])
+ s[Out].bind(s[Out].op.axis[0], te.thread_axis("blockIdx.y"))
+ s[Out].bind(s[Out].op.axis[1], te.thread_axis("blockIdx.x"))
+
+ tx = s[C].op.reduce_axis[0]
+ thread_x = te.thread_axis("threadIdx.x")
+ s[C].bind(tx, thread_x)
+ s[CF].compute_at(s[C], tx)
+ s[C].set_store_predicate(thread_x.var.equal(0))
+ s[Out].set_store_predicate(thread_x.var.equal(0))
+
+
+@autotvm.register_topi_compute("dense_large_batch.gpu")
+def dense_large_batch(cfg, data, weight, bias=None, out_dtype=None):
+ """Dense operator on CUDA"""
Review comment:
ditto.
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[GitHub] [tvm] Lunderberg commented on a change in pull request #8336: [Topi][Unittests] Parametrized tests in `test_topi_dense.py`, split out gpu-independent implementations
Posted by GitBox <gi...@apache.org>.
Lunderberg commented on a change in pull request #8336:
URL: https://github.com/apache/tvm/pull/8336#discussion_r660820023
##########
File path: tests/python/topi/python/test_topi_dense.py
##########
@@ -43,108 +54,115 @@
}
-def verify_dense(batch, in_dim, out_dim, use_bias=True):
- A = te.placeholder((batch, in_dim), name="A")
- B = te.placeholder((out_dim, in_dim), name="B")
- C = te.placeholder((out_dim,), name="C")
- dtype = A.dtype
-
- # use memoize to pickle the test data for next time use
- @memoize("topi.tests.test_topi_dense")
- def get_ref_data():
- a_np = np.random.uniform(size=(batch, in_dim)).astype(dtype)
- b_np = np.random.uniform(size=(out_dim, in_dim)).astype(dtype)
- c_np = np.random.uniform(size=(out_dim,)).astype(dtype)
- if use_bias:
- d_np = np.maximum(np.dot(a_np, b_np.T) + c_np, 0.0)
- else:
- d_np = np.maximum(np.dot(a_np, b_np.T), 0.0)
- return (a_np, b_np, c_np, d_np)
-
- # get the test data
- a_np, b_np, c_np, d_np = get_ref_data()
-
- def check_device(device, dev):
- print("Running on target: %s" % device)
- for fcompute, fschedule in tvm.topi.testing.dispatch(device, _dense_implement):
- with tvm.target.Target(device):
- D = fcompute(A, B, C if use_bias else None)
- D = topi.nn.relu(D)
- s = fschedule([D])
- a = tvm.nd.array(a_np, dev)
- b = tvm.nd.array(b_np, dev)
- c = tvm.nd.array(c_np, dev)
- d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), dev)
- f = tvm.build(s, [A, B, C, D], device, name="dense")
- f(a, b, c, d)
- tvm.testing.assert_allclose(d.numpy(), d_np, rtol=1e-5)
-
- for device, dev in tvm.testing.enabled_targets():
- check_device(device, dev)
-
-
-def verify_dense_int8(batch, in_dim, out_dim, use_bias=True):
- dtype = "int8"
- out_dtype = "int32"
- A = te.placeholder((batch, in_dim), name="A", dtype=dtype)
- B = te.placeholder((out_dim, in_dim), name="B", dtype=dtype)
+@tvm.testing.fixture(cache_return_value=True)
+def dense_ref_data(batch_size, in_dim, out_dim, use_bias, in_dtype, out_dtype):
+ if "float" in in_dtype:
+ a_np = np.random.uniform(size=(batch_size, in_dim)).astype(in_dtype)
+ b_np = np.random.uniform(size=(out_dim, in_dim)).astype(in_dtype)
+ c_np = np.random.uniform(size=(out_dim,)).astype(out_dtype)
+ elif in_dtype == "int8":
+ a_np = np.random.randint(low=-128, high=127, size=(batch_size, in_dim)).astype(in_dtype)
+ b_np = np.random.randint(low=-128, high=127, size=(out_dim, in_dim)).astype(in_dtype)
+ c_np = np.random.randint(low=-128, high=127, size=(out_dim,)).astype(out_dtype)
+ else:
+ raise ValueError("No method to generate test data for data type '{}'".format(in_dtype))
+
+ matmul = np.dot(a_np.astype(out_dtype), b_np.T.astype(out_dtype))
+
+ if use_bias:
+ matmul += c_np
+
+ d_np = np.maximum(matmul, 0)
+ return (a_np, b_np, c_np, d_np)
+
+
+def test_dense(
Review comment:
As of #8010 , the decorator for `@tvm.testing.parametrize_targets` is only needed if the test should override the default targets. Otherwise, having the `target` and/or `dev` fixture arguments is sufficient to have the test run on all enabled targets.
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[GitHub] [tvm] Lunderberg commented on a change in pull request #8336: [Topi][Unittests] Parametrized tests in `test_topi_dense.py`, split out gpu-independent implementations
Posted by GitBox <gi...@apache.org>.
Lunderberg commented on a change in pull request #8336:
URL: https://github.com/apache/tvm/pull/8336#discussion_r660817079
##########
File path: python/tvm/topi/gpu/dense.py
##########
@@ -0,0 +1,218 @@
+# 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-argument
+"""Schedule for dense operator"""
+
+import logging
+
+from tvm import autotvm, te
+from tvm.autotvm.task.space import SplitEntity
+
+from .. import nn
+from ..utils import traverse_inline, get_const_tuple
+
+logger = logging.getLogger("topi")
+
+
+@autotvm.register_topi_compute("dense_small_batch.gpu")
+def dense_small_batch(cfg, data, weight, bias=None, out_dtype=None):
+ """Dense operator on CUDA"""
Review comment:
Thank you, and corrected.
##########
File path: python/tvm/topi/gpu/dense.py
##########
@@ -0,0 +1,218 @@
+# 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-argument
+"""Schedule for dense operator"""
+
+import logging
+
+from tvm import autotvm, te
+from tvm.autotvm.task.space import SplitEntity
+
+from .. import nn
+from ..utils import traverse_inline, get_const_tuple
+
+logger = logging.getLogger("topi")
+
+
+@autotvm.register_topi_compute("dense_small_batch.gpu")
+def dense_small_batch(cfg, data, weight, bias=None, out_dtype=None):
+ """Dense operator on CUDA"""
+ return nn.dense(data, weight, bias, out_dtype)
+
+
+@autotvm.register_topi_schedule("dense_small_batch.gpu")
+def schedule_dense_small_batch(cfg, outs):
+ """Schedule float32/64 dense with small batch size"""
+ 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 == "dense":
+ _schedule_dense_small_batch(cfg, s, op.output(0))
+
+ traverse_inline(s, outs[0].op, _callback)
+ return s
+
+
+def _schedule_dense_small_batch(cfg, s, C):
+ A, weights = C.op.input_tensors
+ _, in_dim_weights = get_const_tuple(weights.shape)
+ _, in_dim_A = get_const_tuple(A.shape)
+
+ if isinstance(in_dim_A, int):
+ in_dim = in_dim_A
+ elif isinstance(in_dim_weights, int):
+ in_dim = in_dim_weights
+ else:
+ in_dim = None
+
+ if in_dim is not None:
+ cfg.define_split("tile_k", in_dim, num_outputs=2)
+ if cfg.is_fallback:
+ cfg["tile_k"] = SplitEntity([-1, 64] if in_dim > 64 else [1, 64])
+ _, kf = cfg["tile_k"].apply(s, C, C.op.reduce_axis[0])
+ else:
+ tile_k = 64
+ _, kf = s[C].split(C.op.reduce_axis[0], tile_k)
+
+ CF = s.rfactor(C, kf)
+
+ if C.op in s.outputs:
+ Out = C
+ else:
+ Out = s.outputs[0].output(0)
+ s[C].compute_at(s[Out], s[Out].op.axis[1])
+ s[Out].bind(s[Out].op.axis[0], te.thread_axis("blockIdx.y"))
+ s[Out].bind(s[Out].op.axis[1], te.thread_axis("blockIdx.x"))
+
+ tx = s[C].op.reduce_axis[0]
+ thread_x = te.thread_axis("threadIdx.x")
+ s[C].bind(tx, thread_x)
+ s[CF].compute_at(s[C], tx)
+ s[C].set_store_predicate(thread_x.var.equal(0))
+ s[Out].set_store_predicate(thread_x.var.equal(0))
+
+
+@autotvm.register_topi_compute("dense_large_batch.gpu")
+def dense_large_batch(cfg, data, weight, bias=None, out_dtype=None):
+ """Dense operator on CUDA"""
Review comment:
Thank you, and corrected.
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[GitHub] [tvm] Lunderberg edited a comment on pull request #8336: [Topi][Unittests] Parametrized tests in `test_topi_dense.py`, split out gpu-independent implementations
Posted by GitBox <gi...@apache.org>.
Lunderberg edited a comment on pull request #8336:
URL: https://github.com/apache/tvm/pull/8336#issuecomment-870776420
Added a few fixes in the codegen needed for the vulkan tests to run correctly on newer nvidia GPUs. (@masahi )
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[GitHub] [tvm] jcf94 commented on a change in pull request #8336: [Topi][Unittests] Parametrized tests in `test_topi_dense.py`, split out gpu-independent implementations
Posted by GitBox <gi...@apache.org>.
jcf94 commented on a change in pull request #8336:
URL: https://github.com/apache/tvm/pull/8336#discussion_r661092648
##########
File path: tests/python/topi/python/test_topi_dense.py
##########
@@ -43,108 +54,115 @@
}
-def verify_dense(batch, in_dim, out_dim, use_bias=True):
- A = te.placeholder((batch, in_dim), name="A")
- B = te.placeholder((out_dim, in_dim), name="B")
- C = te.placeholder((out_dim,), name="C")
- dtype = A.dtype
-
- # use memoize to pickle the test data for next time use
- @memoize("topi.tests.test_topi_dense")
- def get_ref_data():
- a_np = np.random.uniform(size=(batch, in_dim)).astype(dtype)
- b_np = np.random.uniform(size=(out_dim, in_dim)).astype(dtype)
- c_np = np.random.uniform(size=(out_dim,)).astype(dtype)
- if use_bias:
- d_np = np.maximum(np.dot(a_np, b_np.T) + c_np, 0.0)
- else:
- d_np = np.maximum(np.dot(a_np, b_np.T), 0.0)
- return (a_np, b_np, c_np, d_np)
-
- # get the test data
- a_np, b_np, c_np, d_np = get_ref_data()
-
- def check_device(device, dev):
- print("Running on target: %s" % device)
- for fcompute, fschedule in tvm.topi.testing.dispatch(device, _dense_implement):
- with tvm.target.Target(device):
- D = fcompute(A, B, C if use_bias else None)
- D = topi.nn.relu(D)
- s = fschedule([D])
- a = tvm.nd.array(a_np, dev)
- b = tvm.nd.array(b_np, dev)
- c = tvm.nd.array(c_np, dev)
- d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), dev)
- f = tvm.build(s, [A, B, C, D], device, name="dense")
- f(a, b, c, d)
- tvm.testing.assert_allclose(d.numpy(), d_np, rtol=1e-5)
-
- for device, dev in tvm.testing.enabled_targets():
- check_device(device, dev)
-
-
-def verify_dense_int8(batch, in_dim, out_dim, use_bias=True):
- dtype = "int8"
- out_dtype = "int32"
- A = te.placeholder((batch, in_dim), name="A", dtype=dtype)
- B = te.placeholder((out_dim, in_dim), name="B", dtype=dtype)
+@tvm.testing.fixture(cache_return_value=True)
+def dense_ref_data(batch_size, in_dim, out_dim, use_bias, in_dtype, out_dtype):
+ if "float" in in_dtype:
+ a_np = np.random.uniform(size=(batch_size, in_dim)).astype(in_dtype)
+ b_np = np.random.uniform(size=(out_dim, in_dim)).astype(in_dtype)
+ c_np = np.random.uniform(size=(out_dim,)).astype(out_dtype)
+ elif in_dtype == "int8":
+ a_np = np.random.randint(low=-128, high=127, size=(batch_size, in_dim)).astype(in_dtype)
+ b_np = np.random.randint(low=-128, high=127, size=(out_dim, in_dim)).astype(in_dtype)
+ c_np = np.random.randint(low=-128, high=127, size=(out_dim,)).astype(out_dtype)
+ else:
+ raise ValueError("No method to generate test data for data type '{}'".format(in_dtype))
+
+ matmul = np.dot(a_np.astype(out_dtype), b_np.T.astype(out_dtype))
+
+ if use_bias:
+ matmul += c_np
+
+ d_np = np.maximum(matmul, 0)
+ return (a_np, b_np, c_np, d_np)
+
+
+def test_dense(
Review comment:
Oh, my bad. That really a helpful feature.
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[GitHub] [tvm] Lunderberg commented on pull request #8336: [Topi][Unittests] Parametrized tests in `test_topi_dense.py`, split out gpu-independent implementations
Posted by GitBox <gi...@apache.org>.
Lunderberg commented on pull request #8336:
URL: https://github.com/apache/tvm/pull/8336#issuecomment-870776420
Added a few fixes in the codegen needed for the vulkan tests to run correctly on newer nvidia GPUs.
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