diff --git a/python/tvm/contrib/nnpack.py b/python/tvm/contrib/nnpack.py
index 3fb00a3f85e5b034f72888eb5d4707a3f514c415..98367b4ef04ee579e6ad50022e84af67427c5306 100644
--- a/python/tvm/contrib/nnpack.py
+++ b/python/tvm/contrib/nnpack.py
@@ -34,30 +34,6 @@ def fully_connected_inference(lhs, rhs, nthreads=1):
"tvm.contrib.nnpack.fully_connected_inference",
ins[0], ins[1], outs[0], nthreads), name="C")
-def fully_connected_output(lhs, rhs, nthreads=1):
- """Create an extern op that compute fully connected of 2D tensor lhs and
- 2D tensor rhs with nnpack.
-
- Parameters
- ----------
- lhs : Tensor
- lhs 2D matrix input[batch_size][input_channels] of FP32 elements
- rhs : Tensor
- lhs 2D matrix kernel[output_channels][input_channels] of FP32 elements
-
- Returns
- -------
- C : Tensor
- lhs 2D array out[batch_size][output_channels] of FP32 elements.
- """
- n = lhs.shape[0]
- m = rhs.shape[0]
- return _api.extern(
- (n, m), [lhs, rhs],
- lambda ins, outs: _intrin.call_packed(
- "tvm.contrib.nnpack.fully_connected_output",
- ins[0], ins[1], outs[0], nthreads), name="C")
-
class ConvolutionAlgorithm:
AUTO = 0
@@ -204,43 +180,4 @@ def convolution_inference_weight_transform(
"tvm.contrib.nnpack.convolution_inference_weight_transform",
ins[0], outs[0], nthreads, algorithm), name="transform_kernel")
-def convolution_output(data, kernel, bias, padding, nthreads=1):
- """Create an extern op to compute convolution of 4D tensor data and
- 4D tensor kernel and 1D tensor bias with nnpack.
-
- Parameters
- ----------
- data : Tensor
- data 4D tensor input[batch_size][input_channels][input_height]
- [input_width] of FP32 elements.
- kernel : Tensor
- kernel 4D tensor kernel[output_channels][input_channels][kernel_height]
- [kernel_width] of FP32 elements.
- bias : Tensor
- bias 1D array bias[output_channels][input_channels][kernel_height]
- [kernel_width] of FP32 elements.
- padding : list
- padding A 4-dim list of [pad_top, pad_bottom, pad_left, pad_right],
- which indicates the padding around the feature map.
-
- Returns
- -------
- output : Tensor
- output 4D tensor output[batch_size][output_channels][output_height]
- [output_width] of FP32 elements.
- """
-
- assert isinstance(padding, list) and len(padding) == 4
- batch, _, input_height, input_width = data.shape
- output_channels, _, kernel_height, kernel_width = kernel.shape
- output_height = (input_height + padding[0] + padding[1] - kernel_height) + 1
- output_width = (input_width + padding[0] + padding[1] - kernel_width) + 1
-
- return _api.extern(
- (batch, output_channels, output_height, output_width), [data, kernel, bias],
- lambda ins, outs: _intrin.call_packed(
- "tvm.contrib.nnpack.convolution_output", ins[0], ins[1], ins[2],
- outs[0], padding[0], padding[1], padding[2], padding[3], nthreads), name="C")
-
-
_init_api("tvm.contrib.nnpack")
diff --git a/src/contrib/nnpack/convolution.cc b/src/contrib/nnpack/convolution.cc
index 8bcdd64281cce31bde3aa3f79c93a3dfbb43cab0..e600360c67f1b4cc36195c1f9dbde5b468131dfe 100644
--- a/src/contrib/nnpack/convolution.cc
+++ b/src/contrib/nnpack/convolution.cc
@@ -215,64 +215,5 @@ TVM_REGISTER_GLOBAL(
entry->threadpool, nullptr);
CHECK_EQ(status, nnp_status_success);
});
-
-
-TVM_REGISTER_GLOBAL("tvm.contrib.nnpack.convolution_output")
-.set_body([](TVMArgs args, TVMRetValue *ret) {
- NNPackThreadLocalEntry *entry = NNPackThreadLocalEntry::ThreadLocal();
- nnp_initialize();
- DLTensor* input = args[0];
- DLTensor* kernel = args[1];
- DLTensor* bias = args[2];
- DLTensor* output = args[3];
- uint64_t pad_top = args[4], pad_right = args[5], pad_bottom = args[6], pad_left = args[7];
- nnp_padding input_padding{pad_top, pad_right, pad_bottom, pad_left};
- NNPackConfig(args[8]);
-
- CHECK_EQ(input->ndim, 4);
- CHECK_EQ(kernel->ndim, 4);
- CHECK_EQ(bias->ndim, 1);
- CHECK_EQ(output->ndim, 4);
-
- CHECK_EQ(input->shape[0], output->shape[0]);
- size_t batch_size = input->shape[0];
- CHECK_EQ(input->shape[1], kernel->shape[1]);
- size_t input_channels = input->shape[1];
- CHECK_EQ(output->shape[1], bias->shape[0]);
- CHECK_EQ(output->shape[1], kernel->shape[0]);
- size_t output_channels = output->shape[1];
- nnp_size input_size{static_cast<size_t>(input->shape[2]),
- static_cast<size_t>(input->shape[3])};
- nnp_size kernel_size{static_cast<size_t>(kernel->shape[2]),
- static_cast<size_t>(kernel->shape[3])};
-
- CHECK(input->strides == nullptr);
- CHECK(kernel->strides == nullptr);
- CHECK(bias->strides == nullptr);
-
- CHECK(TypeMatch(input->dtype, kDLFloat, 32));
- CHECK(TypeMatch(kernel->dtype, kDLFloat, 32));
- CHECK(TypeMatch(bias->dtype, kDLFloat, 32));
- CHECK(TypeMatch(output->dtype, kDLFloat, 32));
-
- nnp_status status = nnp_convolution_output(nnp_convolution_algorithm_auto,
- batch_size,
- input_channels,
- output_channels,
- input_size,
- input_padding,
- kernel_size,
- static_cast<float*>(input->data),
- static_cast<float*>(kernel->data),
- static_cast<float*>(bias->data),
- static_cast<float*>(output->data),
- NULL,
- NULL,
- nnp_activation_identity,
- NULL,
- entry->threadpool,
- NULL);
- CHECK_EQ(status, nnp_status_success);
- });
} // namespace contrib
} // namespace tvm
diff --git a/src/contrib/nnpack/fully_connected.cc b/src/contrib/nnpack/fully_connected.cc
index ad2569e1f2e1fe31dbe5c1252169efa522125fa1..80f981b29cf6a28dea97eea83cf512ce862adb1b 100644
--- a/src/contrib/nnpack/fully_connected.cc
+++ b/src/contrib/nnpack/fully_connected.cc
@@ -43,38 +43,5 @@ TVM_REGISTER_GLOBAL("tvm.contrib.nnpack.fully_connected_inference")
entry->threadpool);
});
-
-TVM_REGISTER_GLOBAL("tvm.contrib.nnpack.fully_connected_output")
-.set_body([](TVMArgs args, TVMRetValue *ret) {
- NNPackThreadLocalEntry *entry = NNPackThreadLocalEntry::ThreadLocal();
- nnp_initialize();
- DLTensor* A = args[0];
- DLTensor* B = args[1];
- DLTensor* C = args[2];
- NNPackConfig(args[3]);
-
- CHECK_EQ(A->ndim, 2);
- CHECK_EQ(B->ndim, 2);
- CHECK_EQ(C->ndim, 2);
- CHECK_EQ(B->shape[0], C->shape[1]);
- CHECK_EQ(B->shape[1], A->shape[1]);
- CHECK_EQ(A->shape[0], C->shape[0]);
- CHECK(C->strides == nullptr);
- CHECK(B->strides == nullptr);
- CHECK(A->strides == nullptr);
- CHECK(TypeMatch(A->dtype, kDLFloat, 32));
- CHECK(TypeMatch(B->dtype, kDLFloat, 32));
- CHECK(TypeMatch(C->dtype, kDLFloat, 32));
-
- nnp_fully_connected_output(A->shape[0],
- B->shape[1],
- B->shape[0],
- static_cast<float*>(A->data),
- static_cast<float*>(B->data),
- static_cast<float*>(C->data),
- entry->threadpool,
- NULL);
- });
-
} // namespace contrib
} // namespace tvm
diff --git a/tests/python/contrib/test_nnpack.py b/tests/python/contrib/test_nnpack.py
index a4b77a39af63da817b7eaa64acc6e62f558ce075..3ebea0e62ce3c88bb68419976cfab792a7048069 100644
--- a/tests/python/contrib/test_nnpack.py
+++ b/tests/python/contrib/test_nnpack.py
@@ -3,38 +3,6 @@ import numpy as np
import scipy.signal
from tvm.contrib import nnpack
-def test_fully_connected_output():
- n = 1024
- l = 128
- m = 235
- bias = tvm.var('bias', dtype=tvm.float32)
- A = tvm.placeholder((n, l), name='A')
- B = tvm.placeholder((m, l), name='B')
- C = nnpack.fully_connected_output(A, B)
- D = tvm.compute(C.shape, lambda i, j: C[i,j] + bias, name="D")
- s = tvm.create_schedule(D.op)
-
- def verify(target="llvm"):
- if not tvm.module.enabled(target):
- print("skip because %s is not enabled..." % target)
- return
- if not tvm.get_global_func("tvm.contrib.nnpack.fully_connected_output", True):
- print("skip because extern function is not available")
- return
- if not nnpack.is_available():
- return
-
- ctx = tvm.cpu(0)
- f = tvm.build(s, [A, B, D, bias], target)
- a = tvm.nd.array(np.random.uniform(size=(n, l)).astype(A.dtype), ctx)
- b = tvm.nd.array(np.random.uniform(size=(m, l)).astype(B.dtype), ctx)
- d = tvm.nd.array(np.zeros((n, m), dtype=D.dtype), ctx)
- bb = 10.0
- f(a, b, d, bb)
- tvm.testing.assert_allclose(
- d.asnumpy(), np.dot(a.asnumpy(), b.asnumpy().T) + bb, rtol=1e-5)
- verify()
-
def test_fully_connected_inference():
n = 1024
@@ -131,7 +99,7 @@ def test_convolution_inference():
if not tvm.module.enabled(target):
print("skip because %s is not enabled..." % target)
return
- if not tvm.get_global_func("tvm.contrib.nnpack.fully_connected_inference", True):
+ if not tvm.get_global_func("tvm.contrib.nnpack.convolution_inference", True):
print("skip because extern function is not available")
return
if not nnpack.is_available():
@@ -195,7 +163,7 @@ def test_convolution_inference_without_weight_transform():
if not tvm.module.enabled(target):
print("skip because %s is not enabled..." % target)
return
- if not tvm.get_global_func("tvm.contrib.nnpack.fully_connected_inference", True):
+ if not tvm.get_global_func("tvm.contrib.nnpack.convolution_inference_without_weight_transform", True):
print("skip because extern function is not available")
return
if not nnpack.is_available():
@@ -228,53 +196,6 @@ def test_convolution_inference_without_weight_transform():
for with_bias in [True, False]:
verify(algorithm=algorithm, with_bias=with_bias)
-def test_convolution_output():
- BATCH = 32
- IH = 48
- IW = 48
- IC = 16
- OC = 16
- K = 3
- PAD = 1
-
- OH = (IH + 2*PAD - K) + 1
- OW = (IW + 2*PAD - K) + 1
- dshape = (BATCH, IC, IH, IW)
- kshape = (OC, IC, K, K)
- bshape = (OC, )
- oshape = (BATCH, OC, OH, OW)
-
- data = tvm.placeholder(dshape, name='data')
- kernel = tvm.placeholder(kshape, name='kernel')
- bias = tvm.placeholder(bshape, name='bias')
- output = nnpack.convolution_output(data, kernel, bias, [PAD, PAD, PAD, PAD])
- s = tvm.create_schedule(output.op)
-
- def verify(target="llvm"):
- if not tvm.module.enabled(target):
- print("skip because %s is not enabled..." % target)
- return
- if not tvm.get_global_func("tvm.contrib.nnpack.fully_connected_inference", True):
- print("skip because extern function is not available")
- return
- if not nnpack.is_available():
- return
-
- ctx = tvm.cpu(0)
- f = tvm.build(s, [data, kernel, bias, output], target)
-
- na = np.random.uniform(size=dshape).astype(data.dtype)
- nb = np.random.uniform(size=kshape).astype(kernel.dtype)
- nc = np.zeros(bshape, dtype=bias.dtype)
- ta = tvm.nd.array(na, ctx)
- tb = tvm.nd.array(nb, ctx)
- tc = tvm.nd.array(nc, ctx)
- td = tvm.nd.array(np.zeros(oshape, dtype=output.dtype), ctx)
- f(ta, tb, tc, td)
- nd = np_conv(na, nb, PAD)
- tvm.testing.assert_allclose(
- td.asnumpy(), nd, rtol=1e-5)
- verify()
if __name__ == "__main__":
import nose