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[BYOC] Fix DNNL Conv2D in JSON runtime #9043

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Sep 23, 2021
Merged

[BYOC] Fix DNNL Conv2D in JSON runtime #9043

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e7ff9dc
dnnl memory dim is wrong for conv2d
Sep 18, 2021
62340cc
change test case for dnnl conv2d
Sep 19, 2021
00a5e51
trigger CI
Sep 22, 2021
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12 changes: 6 additions & 6 deletions src/runtime/contrib/dnnl/dnnl_json_runtime.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -163,16 +163,16 @@ class DNNLJSONRuntime : public JSONRuntimeBase {
dnnl::memory::dim N = input_shape[0], // batch size
IC = input_shape[1], // input channels
IH = input_shape[2], // input height
IW = input_shape[2], // input width
IW = input_shape[3], // input width
OC = weight_shape[0], // output channels
KH = weight_shape[2], // weight height
KW = weight_shape[3], // weight width
PH_L = std::stoi(str_padding[1]), // height padding: left
PH_R = std::stoi(str_padding[3]), // height padding: right
PW_L = std::stoi(str_padding[0]), // width padding: left
PW_R = std::stoi(str_padding[2]), // width padding: right
PW_L = std::stoi(str_padding[1]), // width padding: left
PW_R = std::stoi(str_padding[3]), // width padding: right
PH_L = std::stoi(str_padding[0]), // height padding: top
PH_R = std::stoi(str_padding[2]), // height padding: bottom
SH = std::stoi(str_strides[0]), // height-wise stride
SW = std::stoi(str_strides[0]), // weight-wise stride
SW = std::stoi(str_strides[1]), // weight-wise stride
OH = (IH - KH + PH_L + PH_R) / SH + 1, // output height
OW = (IW - KW + PW_L + PW_R) / SW + 1; // output width

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14 changes: 9 additions & 5 deletions tests/python/relay/test_json_runtime.py
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Expand Up @@ -96,12 +96,14 @@ def test_conv2d():

def conv2d_direct():
dtype = "float32"
ishape = (1, 32, 14, 14)
w1shape = (32, 32, 3, 3)
ishape = (1, 1, 99, 12)
w1shape = (54, 1, 3, 3)

data0 = relay.var("data", shape=ishape, dtype=dtype)
weight0 = relay.var("weight", shape=w1shape, dtype=dtype)
out = relay.nn.conv2d(data0, weight0, kernel_size=(3, 3), padding=(1, 1))
out = relay.nn.conv2d(
data0, weight0, kernel_size=(3, 3), strides=(2, 2), padding=(1, 0, 1, 1)
)

func = relay.Function([data0, weight0], out)
func = set_func_attr(func, "dnnl", "tvmgen_default_dnnl_0")
Expand All @@ -118,7 +120,9 @@ def conv2d_direct():

data0 = relay.var("data", shape=ishape, dtype=dtype)
weight0 = relay.var("weight", shape=w1shape, dtype=dtype)
out = relay.nn.conv2d(data0, weight0, kernel_size=(3, 3), padding=(1, 1))
out = relay.nn.conv2d(
data0, weight0, kernel_size=(3, 3), strides=(2, 2), padding=(1, 0, 1, 1)
)
main_f = relay.Function([data0, weight0], out)
ref_mod = tvm.IRModule()
ref_mod["main"] = main_f
Expand All @@ -127,7 +131,7 @@ def conv2d_direct():
i_data = np.random.uniform(0, 1, ishape).astype(dtype)
w1_data = np.random.uniform(0, 1, w1shape).astype(dtype)

return mod, ref_mod, {"data": i_data, "weight": w1_data}, (1, 32, 14, 14)
return mod, ref_mod, {"data": i_data, "weight": w1_data}, (1, 54, 50, 6)

def group_conv2d():
dtype = "float32"
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