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[GitHub] [tvm] crazydemo opened a new pull request, #12151: [BYOC-DNNL] add post_sum pattern
crazydemo opened a new pull request, #12151:
URL: https://github.com/apache/tvm/pull/12151
This PR add `conv2d-add-sum-relu` pattern, and the corresponding test case is added.
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[GitHub] [tvm] crazydemo commented on a diff in pull request #12151: [BYOC-DNNL] add post_sum pattern
Posted by GitBox <gi...@apache.org>.
crazydemo commented on code in PR #12151:
URL: https://github.com/apache/tvm/pull/12151#discussion_r932899094
##########
tests/python/contrib/test_dnnl.py:
##########
@@ -192,6 +192,7 @@ def run_and_verify(mod, input, params, target, run_module, subgraph_num=None, te
if use_dnnl:
processed_mod = partition_for_dnnl(processed_mod, params, alter_layout)
check_dnnl_used(processed_mod)
+ print(processed_mod)
Review Comment:
Fixed.
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[GitHub] [tvm] yangulei commented on a diff in pull request #12151: [BYOC-DNNL] add post_sum pattern
Posted by GitBox <gi...@apache.org>.
yangulei commented on code in PR #12151:
URL: https://github.com/apache/tvm/pull/12151#discussion_r931740353
##########
python/tvm/relay/op/contrib/dnnl.py:
##########
@@ -305,6 +395,12 @@ def pattern_table():
dnnl_patterns = list()
dnnl_patterns.append(make_qnn_conv2d_pattern())
dnnl_patterns.append(make_qnn_dense_pattern())
+ dnnl_patterns.append(
+ ("dnnl.conv2d_bias_sum_relu", make_conv_bias_sum_relu_pattern("nn.conv2d"), make_predicate(add_checker))
+ ),
+ dnnl_patterns.append(
+ ("dnnl.conv2d_bias_sum", make_conv_bias_sum_relu_pattern("nn.conv2d", False), make_predicate(add_checker))
+ ),
Review Comment:
```suggestion
dnnl_patterns.append(
(
"dnnl.conv2d_bias_sum_relu",
make_conv_bias_sum_relu_pattern("nn.conv2d"),
make_predicate(add_checker),
)
)
dnnl_patterns.append(
(
"dnnl.conv2d_bias_sum",
make_conv_bias_sum_relu_pattern("nn.conv2d", False),
make_predicate(add_checker),
)
)
```
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[GitHub] [tvm] yangulei commented on a diff in pull request #12151: [BYOC-DNNL] add post_sum pattern
Posted by GitBox <gi...@apache.org>.
yangulei commented on code in PR #12151:
URL: https://github.com/apache/tvm/pull/12151#discussion_r931740897
##########
tests/python/contrib/test_dnnl.py:
##########
@@ -788,6 +789,44 @@ def test_conv2d_pattern(run_module, dtype="float32"):
run_and_verify_func(config, run_module=run_module, dtype=dtype)
+def test_conv2d_bias_sum_relu(run_module, dtype="float32"):
+ x_shape = (1, 32, 8, 8)
+ k_shape = (16, 32, 3, 3)
+
+ def get_conv2d_bn_sum_relu(x_shape, k_shape, sum_shape, dtype="float32"):
+ out, dic, param_lst = get_conv2d_bias(x_shape=x_shape, k_shape=k_shape, dtype=dtype)
+ beta = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ gamma = relay.const(np.ones(k_shape[0]).astype(dtype))
+ moving_mean = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ moving_var = relay.const(np.ones(k_shape[0]).astype(dtype))
+ out, _, _ = relay.nn.batch_norm(
+ out,
+ gamma=gamma,
+ beta=beta,
+ moving_mean=moving_mean,
+ moving_var=moving_var,
+ axis=1,
+ center=True,
+ scale=True,
+ epsilon=1e-5,
+ )
+ sum_data = relay.var("data1", shape=sum_shape, dtype=dtype)
+ out = relay.add(out, sum_data)
+ dic["data1"] = sum_shape
+ param_lst += ["data1"]
+ return relay.nn.relu(out), dic, param_lst
+
+ conv2d_bn_sum_relu, dic, param_lst = get_conv2d_bn_sum_relu(x_shape, k_shape, sum_shape=(1, 16, 6, 6), dtype=dtype)
Review Comment:
```suggestion
conv2d_bn_sum_relu, dic, param_lst = get_conv2d_bn_sum_relu(
x_shape, k_shape, sum_shape=(1, 16, 6, 6), dtype=dtype
)
```
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[GitHub] [tvm] billishyahao commented on a diff in pull request #12151: [BYOC-DNNL] add post_sum pattern
Posted by GitBox <gi...@apache.org>.
billishyahao commented on code in PR #12151:
URL: https://github.com/apache/tvm/pull/12151#discussion_r929489000
##########
tests/python/contrib/test_dnnl.py:
##########
@@ -192,6 +192,7 @@ def run_and_verify(mod, input, params, target, run_module, subgraph_num=None, te
if use_dnnl:
processed_mod = partition_for_dnnl(processed_mod, params, alter_layout)
check_dnnl_used(processed_mod)
+ print(processed_mod)
Review Comment:
remove it
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[GitHub] [tvm] crazydemo commented on pull request #12151: [BYOC-DNNL] add post_sum pattern
Posted by GitBox <gi...@apache.org>.
crazydemo commented on PR #12151:
URL: https://github.com/apache/tvm/pull/12151#issuecomment-1200575009
@masahi Could you please review this PR? This PR adds `conv2d-add-sum-relu pattern` with required checks, and the corresponding test case is added.
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[GitHub] [tvm] yangulei commented on a diff in pull request #12151: [BYOC-DNNL] add post_sum pattern
Posted by GitBox <gi...@apache.org>.
yangulei commented on code in PR #12151:
URL: https://github.com/apache/tvm/pull/12151#discussion_r931741021
##########
tests/python/contrib/test_dnnl.py:
##########
@@ -788,6 +789,44 @@ def test_conv2d_pattern(run_module, dtype="float32"):
run_and_verify_func(config, run_module=run_module, dtype=dtype)
+def test_conv2d_bias_sum_relu(run_module, dtype="float32"):
+ x_shape = (1, 32, 8, 8)
+ k_shape = (16, 32, 3, 3)
+
+ def get_conv2d_bn_sum_relu(x_shape, k_shape, sum_shape, dtype="float32"):
+ out, dic, param_lst = get_conv2d_bias(x_shape=x_shape, k_shape=k_shape, dtype=dtype)
+ beta = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ gamma = relay.const(np.ones(k_shape[0]).astype(dtype))
+ moving_mean = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ moving_var = relay.const(np.ones(k_shape[0]).astype(dtype))
+ out, _, _ = relay.nn.batch_norm(
+ out,
+ gamma=gamma,
+ beta=beta,
+ moving_mean=moving_mean,
+ moving_var=moving_var,
+ axis=1,
+ center=True,
+ scale=True,
+ epsilon=1e-5,
+ )
+ sum_data = relay.var("data1", shape=sum_shape, dtype=dtype)
+ out = relay.add(out, sum_data)
+ dic["data1"] = sum_shape
+ param_lst += ["data1"]
+ return relay.nn.relu(out), dic, param_lst
+
+ conv2d_bn_sum_relu, dic, param_lst = get_conv2d_bn_sum_relu(x_shape, k_shape, sum_shape=(1, 16, 6, 6), dtype=dtype)
+ conv2d_bn_sum_relu = tvm.IRModule.from_expr(conv2d_bn_sum_relu)
+ config = conv2d_bn_sum_relu, dic, param_lst
+ run_and_verify_func(config, run_module=run_module, dtype=dtype)
+
+ conv2d_bn_sum_relu, dic, param_lst = get_conv2d_bn_sum_relu(x_shape, k_shape, sum_shape=(1, 16, 1, 1), dtype=dtype)
Review Comment:
```suggestion
conv2d_bn_sum_relu, dic, param_lst = get_conv2d_bn_sum_relu(
x_shape, k_shape, sum_shape=(1, 16, 1, 1), dtype=dtype
)
```
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[GitHub] [tvm] crazydemo commented on a diff in pull request #12151: [BYOC-DNNL] add post_sum pattern
Posted by GitBox <gi...@apache.org>.
crazydemo commented on code in PR #12151:
URL: https://github.com/apache/tvm/pull/12151#discussion_r932899255
##########
python/tvm/relay/op/contrib/dnnl.py:
##########
@@ -305,6 +395,12 @@ def pattern_table():
dnnl_patterns = list()
dnnl_patterns.append(make_qnn_conv2d_pattern())
dnnl_patterns.append(make_qnn_dense_pattern())
+ dnnl_patterns.append(
+ ("dnnl.conv2d_bias_sum_relu", make_conv_bias_sum_relu_pattern("nn.conv2d"), make_predicate(add_checker))
+ ),
+ dnnl_patterns.append(
+ ("dnnl.conv2d_bias_sum", make_conv_bias_sum_relu_pattern("nn.conv2d", False), make_predicate(add_checker))
+ ),
Review Comment:
Fixed.
##########
src/runtime/contrib/dnnl/dnnl_json_runtime.cc:
##########
@@ -361,6 +367,10 @@ class DNNLJSONRuntime : public JSONRuntimeBase {
// TODO(@apeskov): Simulation of inplace primitive. just as PoC.
auto sum_in_tr = GetInputByName(nid, "sum_idx").TreatAs(dst_layout);
+ if (op_name.find("_sum") != std::string::npos) {
+ sum_in_tr = GetInput(nid, node.GetInputs().size()-1);
Review Comment:
Fixed.
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[GitHub] [tvm] yangulei commented on a diff in pull request #12151: [BYOC-DNNL] add post_sum pattern
Posted by GitBox <gi...@apache.org>.
yangulei commented on code in PR #12151:
URL: https://github.com/apache/tvm/pull/12151#discussion_r931740601
##########
src/runtime/contrib/dnnl/dnnl_json_runtime.cc:
##########
@@ -361,6 +367,10 @@ class DNNLJSONRuntime : public JSONRuntimeBase {
// TODO(@apeskov): Simulation of inplace primitive. just as PoC.
auto sum_in_tr = GetInputByName(nid, "sum_idx").TreatAs(dst_layout);
+ if (op_name.find("_sum") != std::string::npos) {
+ sum_in_tr = GetInput(nid, node.GetInputs().size()-1);
Review Comment:
```suggestion
sum_in_tr = GetInput(nid, node.GetInputs().size() - 1);
```
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[GitHub] [tvm] yangulei commented on a diff in pull request #12151: [BYOC-DNNL] add post_sum pattern
Posted by GitBox <gi...@apache.org>.
yangulei commented on code in PR #12151:
URL: https://github.com/apache/tvm/pull/12151#discussion_r931741349
##########
tests/python/contrib/test_dnnl.py:
##########
@@ -1732,4 +1771,5 @@ def generate_model(p, c):
if __name__ == "__main__":
- tvm.testing.main()
+ # tvm.testing.main()
+ test_conv2d_bias_sum_relu(True)
Review Comment:
```suggestion
tvm.testing.main()
```
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[GitHub] [tvm] masahi merged pull request #12151: [BYOC-DNNL] add post_sum pattern
Posted by GitBox <gi...@apache.org>.
masahi merged PR #12151:
URL: https://github.com/apache/tvm/pull/12151
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[GitHub] [tvm] crazydemo commented on a diff in pull request #12151: [BYOC-DNNL] add post_sum pattern
Posted by GitBox <gi...@apache.org>.
crazydemo commented on code in PR #12151:
URL: https://github.com/apache/tvm/pull/12151#discussion_r932899385
##########
tests/python/contrib/test_dnnl.py:
##########
@@ -788,6 +789,44 @@ def test_conv2d_pattern(run_module, dtype="float32"):
run_and_verify_func(config, run_module=run_module, dtype=dtype)
+def test_conv2d_bias_sum_relu(run_module, dtype="float32"):
+ x_shape = (1, 32, 8, 8)
+ k_shape = (16, 32, 3, 3)
+
+ def get_conv2d_bn_sum_relu(x_shape, k_shape, sum_shape, dtype="float32"):
+ out, dic, param_lst = get_conv2d_bias(x_shape=x_shape, k_shape=k_shape, dtype=dtype)
+ beta = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ gamma = relay.const(np.ones(k_shape[0]).astype(dtype))
+ moving_mean = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ moving_var = relay.const(np.ones(k_shape[0]).astype(dtype))
+ out, _, _ = relay.nn.batch_norm(
+ out,
+ gamma=gamma,
+ beta=beta,
+ moving_mean=moving_mean,
+ moving_var=moving_var,
+ axis=1,
+ center=True,
+ scale=True,
+ epsilon=1e-5,
+ )
+ sum_data = relay.var("data1", shape=sum_shape, dtype=dtype)
+ out = relay.add(out, sum_data)
+ dic["data1"] = sum_shape
+ param_lst += ["data1"]
+ return relay.nn.relu(out), dic, param_lst
+
+ conv2d_bn_sum_relu, dic, param_lst = get_conv2d_bn_sum_relu(x_shape, k_shape, sum_shape=(1, 16, 6, 6), dtype=dtype)
Review Comment:
Fixed.
##########
tests/python/contrib/test_dnnl.py:
##########
@@ -788,6 +789,44 @@ def test_conv2d_pattern(run_module, dtype="float32"):
run_and_verify_func(config, run_module=run_module, dtype=dtype)
+def test_conv2d_bias_sum_relu(run_module, dtype="float32"):
+ x_shape = (1, 32, 8, 8)
+ k_shape = (16, 32, 3, 3)
+
+ def get_conv2d_bn_sum_relu(x_shape, k_shape, sum_shape, dtype="float32"):
+ out, dic, param_lst = get_conv2d_bias(x_shape=x_shape, k_shape=k_shape, dtype=dtype)
+ beta = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ gamma = relay.const(np.ones(k_shape[0]).astype(dtype))
+ moving_mean = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ moving_var = relay.const(np.ones(k_shape[0]).astype(dtype))
+ out, _, _ = relay.nn.batch_norm(
+ out,
+ gamma=gamma,
+ beta=beta,
+ moving_mean=moving_mean,
+ moving_var=moving_var,
+ axis=1,
+ center=True,
+ scale=True,
+ epsilon=1e-5,
+ )
+ sum_data = relay.var("data1", shape=sum_shape, dtype=dtype)
+ out = relay.add(out, sum_data)
+ dic["data1"] = sum_shape
+ param_lst += ["data1"]
+ return relay.nn.relu(out), dic, param_lst
+
+ conv2d_bn_sum_relu, dic, param_lst = get_conv2d_bn_sum_relu(x_shape, k_shape, sum_shape=(1, 16, 6, 6), dtype=dtype)
+ conv2d_bn_sum_relu = tvm.IRModule.from_expr(conv2d_bn_sum_relu)
+ config = conv2d_bn_sum_relu, dic, param_lst
+ run_and_verify_func(config, run_module=run_module, dtype=dtype)
+
+ conv2d_bn_sum_relu, dic, param_lst = get_conv2d_bn_sum_relu(x_shape, k_shape, sum_shape=(1, 16, 1, 1), dtype=dtype)
Review Comment:
Fixed.
##########
tests/python/contrib/test_dnnl.py:
##########
@@ -1732,4 +1771,5 @@ def generate_model(p, c):
if __name__ == "__main__":
- tvm.testing.main()
+ # tvm.testing.main()
+ test_conv2d_bias_sum_relu(True)
Review Comment:
Fixed.
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