Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Added nearest interp v2 BF16 FWD kernel #39490

Merged
merged 15 commits into from
Feb 24, 2022
Merged

Added nearest interp v2 BF16 FWD kernel #39490

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
15 commits
Select commit Hold shift + click to select a range
988df42
added nearest interp v2 bf16
jakpiase Feb 11, 2022
c060b92
disabled bilinear interp nhwc test
jakpiase Feb 13, 2022
3806296
added skipping UT for gpu
jakpiase Feb 14, 2022
b493a7a
added NHWC support
jakpiase Feb 15, 2022
c684117
removed unnecessary statements
jakpiase Feb 15, 2022
343313e
minor change
jakpiase Feb 15, 2022
69f9bec
CI fix
jakpiase Feb 16, 2022
5e44f03
added appropriate changes to interpolate_v1
jakpiase Feb 16, 2022
39ff4d0
fix after review
jakpiase Feb 18, 2022
2b90948
minor change
jakpiase Feb 18, 2022
2c518b0
resolved conflicts
jakpiase Feb 19, 2022
202b5f2
minor change
jakpiase Feb 19, 2022
ee12877
revert unwanted deletions
jakpiase Feb 19, 2022
2949758
CI fix
jakpiase Feb 19, 2022
fe73d3b
Merge branch 'develop' into nearest_bf16
jakpiase Feb 21, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion paddle/fluid/operators/interpolate_op.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -328,7 +328,7 @@ class InterpolateOp : public framework::OperatorWithKernel {
auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");

#ifdef PADDLE_WITH_MKLDNN
auto interp_method = ctx.Attr<std::string>("interp_method");
const auto& interp_method = ctx.Attr<std::string>("interp_method");
// TODO(danqing): support other interp_method
if (this->CanMKLDNNBeUsed(ctx, data_type) &&
(interp_method == "nearest" || interp_method == "bilinear")) {
Expand Down
2 changes: 1 addition & 1 deletion paddle/fluid/operators/interpolate_v2_op.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -414,7 +414,7 @@ class InterpolateV2Op : public framework::OperatorWithKernel {
auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");

#ifdef PADDLE_WITH_MKLDNN
auto interp_method = ctx.Attr<std::string>("interp_method");
const auto& interp_method = ctx.Attr<std::string>("interp_method");
// TODO(danqing): support other interp_method
if (this->CanMKLDNNBeUsed(ctx, data_type) &&
(interp_method == "nearest" || interp_method == "bilinear")) {
Expand Down
34 changes: 14 additions & 20 deletions paddle/fluid/operators/mkldnn/interpolate_mkldnn_op.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -53,17 +53,13 @@ class InterpolateMKLDNNKernel : public framework::OpKernel<T> {
std::vector<int> ComputeOutputShape(
const framework::ExecutionContext& ctx) const {
const auto* x = ctx.Input<Tensor>("X");
auto in_dims = x->dims();
const bool is_channel_last = false; // In mkldnn kernel, always use NCHW

framework::DDim in_dhw_dims;
if (is_channel_last) { // NDHWC, NHWC, NWC
in_dhw_dims = phi::slice_ddim(in_dims, 1, in_dims.size() - 1);
} else { // NCDHW, NCHW, NCW
in_dhw_dims = phi::slice_ddim(in_dims, 2, in_dims.size());
}
const auto& in_dims = x->dims();

const framework::DDim in_dhw_dims =
phi::slice_ddim(in_dims, 2, in_dims.size());

std::vector<int> out_dims;
out_dims.reserve(5);
if (in_dhw_dims.size() == 1) {
out_dims.push_back(ctx.Attr<int>("out_w"));
} else if (in_dhw_dims.size() == 2) {
Expand Down Expand Up @@ -125,12 +121,8 @@ class InterpolateMKLDNNKernel : public framework::OpKernel<T> {
"out_d, out_h, out_w of Op(interpolate) "
"should be greater than 0."));

out_dims.insert(out_dims.begin(), in_dims[0]);
if (is_channel_last) {
out_dims.push_back(in_dims[in_dims.size() - 1]);
} else {
out_dims.insert(out_dims.begin() + 1, in_dims[1]);
}
const std::vector<int64_t> nc_dims = {in_dims[0], in_dims[1]};
out_dims.insert(out_dims.begin(), nc_dims.begin(), nc_dims.end());
return out_dims;
}

Expand All @@ -143,12 +135,12 @@ class InterpolateMKLDNNKernel : public framework::OpKernel<T> {
const auto* x = ctx.Input<Tensor>("X");
auto* z = ctx.Output<Tensor>("Out");

auto interp_method = ctx.Attr<std::string>("interp_method");
dnnl::algorithm algo = (interp_method == "nearest")
? dnnl::algorithm::resampling_nearest
: dnnl::algorithm::resampling_linear;
const auto interp_method = ctx.Attr<std::string>("interp_method");
const dnnl::algorithm algo = (interp_method == "nearest")
? dnnl::algorithm::resampling_nearest
: dnnl::algorithm::resampling_linear;

auto out_dims_vec = ComputeOutputShape(ctx);
const auto out_dims_vec = ComputeOutputShape(ctx);
framework::DDim dim_out = phi::make_ddim(out_dims_vec);
z->Resize(dim_out);

Expand All @@ -162,6 +154,7 @@ class InterpolateMKLDNNKernel : public framework::OpKernel<T> {
const std::unordered_map<int, dnnl::memory> args = {
{DNNL_ARG_SRC, *src_memory_p}, {DNNL_ARG_DST, *dst_memory_p}};
auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();

resampling_prim->execute(astream, args);
astream.wait();

Expand All @@ -184,6 +177,7 @@ REGISTER_OP_KERNEL(bilinear_interp, MKLDNN, ::paddle::platform::CPUPlace,

REGISTER_OP_KERNEL(nearest_interp_v2, MKLDNN, ::paddle::platform::CPUPlace,
ops::InterpolateMKLDNNKernel<float>,
ops::InterpolateMKLDNNKernel<paddle::platform::bfloat16>,
ops::InterpolateMKLDNNKernel<int8_t>,
ops::InterpolateMKLDNNKernel<uint8_t>);
REGISTER_OP_KERNEL(bilinear_interp_v2, MKLDNN, ::paddle::platform::CPUPlace,
Expand Down
26 changes: 18 additions & 8 deletions python/paddle/fluid/tests/unittests/mkldnn/test_nearest_interp_v2_mkldnn_op.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@

import unittest
import numpy as np
from paddle.fluid.tests.unittests.op_test import OpTest
from paddle.fluid.tests.unittests.op_test import OpTest, OpTestTool, convert_float_to_uint16
from paddle.fluid.tests.unittests.op_test import skip_check_grad_ci


Expand Down Expand Up @@ -59,6 +59,7 @@ def nearest_neighbor_interp_mkldnn_np(X,


@skip_check_grad_ci(reason="Haven not implement interpolate grad kernel.")
@OpTestTool.skip_if_not_cpu_bf16()
class TestNearestInterpV2MKLDNNOp(OpTest):
def init_test_case(self):
pass
Expand All @@ -84,7 +85,7 @@ def setUp(self):
self.init_test_case()
self.init_data_type()

if self.dtype == np.float32:
if self.dtype == np.float32 or self.dtype == np.uint16:
input_np = np.random.random(self.input_shape).astype(self.dtype)
else:
init_low, init_high = (-5, 5) if self.dtype == np.int8 else (0, 10)
Expand Down Expand Up @@ -126,6 +127,9 @@ def setUp(self):
if isinstance(self.scale, float):
self.scale = [self.scale]

if self.dtype == np.uint16:
input_np = convert_float_to_uint16(input_np)

self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
Expand Down Expand Up @@ -191,6 +195,10 @@ class TestFp32Case(parent):
def init_data_type(self):
self.dtype = np.float32

class TestBf16Case(parent):
def init_data_type(self):
self.dtype = np.uint16

class TestInt8Case(parent):
def init_data_type(self):
self.dtype = np.int8
Expand All @@ -199,12 +207,14 @@ class TestUint8Case(parent):
def init_data_type(self):
self.dtype = np.uint8

TestFp32Case.__name__ = parent.__name__
TestInt8Case.__name__ = parent.__name__
TestUint8Case.__name__ = parent.__name__
globals()[parent.__name__] = TestFp32Case
globals()[parent.__name__] = TestInt8Case
globals()[parent.__name__] = TestUint8Case
TestFp32Case.__name__ = "{0}_{1}".format(parent.__name__, "FP32")
TestBf16Case.__name__ = "{0}_{1}".format(parent.__name__, "BF16")
TestInt8Case.__name__ = "{0}_{1}".format(parent.__name__, "INT8")
TestUint8Case.__name__ = "{0}_{1}".format(parent.__name__, "UINT8")
globals()[TestFp32Case.__name__] = TestFp32Case
globals()[TestBf16Case.__name__] = TestBf16Case
globals()[TestInt8Case.__name__] = TestInt8Case
globals()[TestUint8Case.__name__] = TestUint8Case


create_test_class(TestNearestInterpV2MKLDNNOp)
Expand Down