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Unverified Commit faefeef9 authored by Charlie Lin's avatar Charlie Lin Committed by GitHub
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Merge branch 'develop' into dyn_shape_update

parents 97a40ac3 bf0a4713
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Showing with 685 additions and 583 deletions
src/reduce_dims.cpp
View file @ faefeef9
......@@ -16,10 +16,8 @@ bool reduce_dim(std::vector<shape>& shapes, std::size_t n)
auto bstride = s.strides()[n + 1];
auto blen = s.lens()[n + 1];
if(astride == bstride * blen)
{
if(astride == bstride * blen or alen == 1)
new_lens.push_back(alen * blen);
}
}
if(new_lens.size() != shapes.size())
return false;
......@@ -37,10 +35,25 @@ bool reduce_dim(std::vector<shape>& shapes, std::size_t n)
return true;
}
void reduce_dim1(std::vector<shape>& shapes)
{
if(std::any_of(shapes.begin(), shapes.end(), [&](const auto& s) {
return s.lens().size() < 2 or s.lens().back() != 1;
}))
return;
for(auto& s : shapes)
{
auto lens = s.lens();
auto strides = s.strides();
lens.pop_back();
strides.pop_back();
s = shape{s.type(), lens, strides};
}
}
std::size_t reduce_dim_all(std::vector<shape>& shapes, std::size_t n)
{
while(reduce_dim(shapes, n) and n < shapes.size()) {}
return n + 1;
}
void reduce_dim_all(std::vector<shape>& shapes)
......@@ -48,6 +61,7 @@ void reduce_dim_all(std::vector<shape>& shapes)
std::size_t n = 0;
while(n < shapes.front().lens().size() - 1)
n = reduce_dim_all(shapes, n);
reduce_dim1(shapes);
}
std::vector<std::size_t> base_lens(const std::vector<shape>& shapes)
......
src/rewrite_batchnorm.cpp
View file @ faefeef9
......@@ -14,9 +14,9 @@
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
void rewrite_batchnorm::apply(module& p) const
void rewrite_batchnorm::apply(module& m) const
{
for(auto ins : iterator_for(p))
for(auto ins : iterator_for(m))
{
if(ins->name() != "batch_norm_inference")
continue;
......@@ -46,13 +46,13 @@ void rewrite_batchnorm::apply(module& p) const
});
auto broadcast = op::broadcast{1, ins->get_shape().lens()};
auto a_ins = p.add_literal({a.get_shape(), a.data()});
auto a_broadcast = p.insert_instruction(ins, broadcast, a_ins);
auto mul = p.insert_instruction(ins, make_op("mul"), ins->inputs().front(), a_broadcast);
auto b_ins = p.add_literal({b.get_shape(), b.data()});
auto b_broadcast = p.insert_instruction(ins, broadcast, b_ins);
auto add = p.insert_instruction(ins, make_op("add"), mul, b_broadcast);
p.replace_instruction(ins, add);
auto a_ins = m.add_literal({a.get_shape(), a.data()});
auto a_broadcast = m.insert_instruction(ins, broadcast, a_ins);
auto mul = m.insert_instruction(ins, make_op("mul"), ins->inputs().front(), a_broadcast);
auto b_ins = m.add_literal({b.get_shape(), b.data()});
auto b_broadcast = m.insert_instruction(ins, broadcast, b_ins);
auto add = m.insert_instruction(ins, make_op("add"), mul, b_broadcast);
m.replace_instruction(ins, add);
}
}
......
src/rewrite_pooling.cpp
View file @ faefeef9
......@@ -12,9 +12,9 @@
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
void rewrite_pooling::apply(module& prog) const
void rewrite_pooling::apply(module& m) const
{
for(auto ins : iterator_for(prog))
for(auto ins : iterator_for(m))
{
if(ins->name() != "pooling")
continue;
......@@ -33,26 +33,25 @@ void rewrite_pooling::apply(module& prog) const
continue;
std::int64_t n = s.lens()[0];
std::int64_t c = s.lens()[1];
auto reshape = prog.insert_instruction(
auto reshape = m.insert_instruction(
ins, make_op("reshape", {{"dims", {n * c, -1}}}), ins->inputs().front());
instruction_ref pooling{};
// average pooling
if(op.mode == op::pooling_mode::average)
{
pooling =
prog.insert_instruction(ins, make_op("reduce_mean", {{"axes", {1}}}), reshape);
pooling = m.insert_instruction(ins, make_op("reduce_mean", {{"axes", {1}}}), reshape);
}
// max pooling
else
{
pooling = prog.insert_instruction(ins, make_op("reduce_max", {{"axes", {1}}}), reshape);
pooling = m.insert_instruction(ins, make_op("reduce_max", {{"axes", {1}}}), reshape);
}
std::vector<int64_t> rsp_lens(lens.size(), 1);
rsp_lens[0] = n;
rsp_lens[1] = c;
prog.replace_instruction(ins, make_op("reshape", {{"dims", rsp_lens}}), pooling);
m.replace_instruction(ins, make_op("reshape", {{"dims", rsp_lens}}), pooling);
}
}
......
src/rewrite_rnn.cpp
View file @ faefeef9
This diff is collapsed.
src/schedule.cpp
View file @ faefeef9
......@@ -42,7 +42,7 @@ struct stream_info
std::unordered_map<instruction_ref, std::size_t> iweights;
ins_dep_map mod_implicit_deps;
void calc_implicit_deps(const module& p) { mod_implicit_deps = p.calc_implicit_deps(); }
void calc_implicit_deps(const module& m) { mod_implicit_deps = m.calc_implicit_deps(); }
void accumulate_weights(instruction_ref last, const schedule_model& model)
{
......@@ -116,15 +116,15 @@ struct stream_info
}
};
std::size_t assign_streams(module& p, std::size_t n)
std::size_t assign_streams(module& m, std::size_t n)
{
assert(n > 0);
partition critical;
std::unordered_map<instruction_ref, std::deque<partition>> partitions;
partitions.reserve(weights.size());
fix([&](auto self, auto ins, auto& part) {
assert(not is_end(ins, p.end()));
if(not p.has_instruction(ins))
assert(not is_end(ins, m.end()));
if(not m.has_instruction(ins))
return;
if(contains(partitions, ins))
return;
......@@ -151,8 +151,8 @@ struct stream_info
}
}
// Sort instructions
p.move_instruction(ins, p.end());
})(std::prev(p.end()), critical);
m.move_instruction(ins, m.end());
})(std::prev(m.end()), critical);
// Set the critical partition to stream 0
set_stream(critical, 0);
......@@ -197,13 +197,13 @@ struct stream_info
}
};
void sort(module& p, std::size_t)
void sort(module& m, std::size_t)
{
std::set<weight_ins, compare_weight_ins> children;
std::unordered_map<instruction_ref, std::size_t> visited;
auto last = std::prev(p.end());
auto last = std::prev(m.end());
auto mw = this->weights.at(last);
auto nw = mw / (p.size() + 1);
auto nw = mw / (m.size() + 1);
auto add_child = [&](auto ins) {
auto x = 1 + (mw - this->weights.at(ins)) / (nw + 1);
auto w = x * this->iweights.at(ins);
......@@ -222,10 +222,10 @@ struct stream_info
// Pop the first element
auto top = children.begin()->second;
children.erase(children.begin());
p.move_instruction(top, p.begin());
m.move_instruction(top, m.begin());
for(auto ins : top->inputs())
{
if(not p.has_instruction(ins))
if(not m.has_instruction(ins))
continue;
add_child(ins);
}
......@@ -234,7 +234,7 @@ struct stream_info
{
for(auto ins : mod_implicit_deps.at(top))
{
assert(p.has_instruction(ins));
assert(m.has_instruction(ins));
add_child(ins);
}
}
......@@ -242,12 +242,12 @@ struct stream_info
// move dangling parameter to the front so as not be removed
auto ins = std::next(last);
while(ins != p.end())
while(ins != m.end())
{
auto next = std::next(ins);
if(ins->name() == "@param")
{
p.move_instruction(ins, p.begin());
m.move_instruction(ins, m.begin());
}
ins = next;
}
......@@ -364,18 +364,18 @@ struct stream_info
}
std::unordered_map<instruction_ref, std::vector<std::vector<instruction_ref>>>
find_concurrent_instructions(module& p) const
find_concurrent_instructions(module& m) const
{
std::unordered_map<instruction_ref, std::vector<std::vector<instruction_ref>>> result;
std::unordered_map<instruction_ref, std::unordered_set<instruction_ref>> merge_from;
dominator_info di = compute_dominator(p);
result.reserve(p.size());
merge_from.reserve(p.size());
for(auto ins : reverse_iterator_for(p))
dominator_info di = compute_dominator(m);
result.reserve(m.size());
merge_from.reserve(m.size());
for(auto ins : reverse_iterator_for(m))
{
for(auto&& arg : ins->outputs())
{
if(not p.has_instruction(arg))
if(not m.has_instruction(arg))
continue;
if(is_merge_point(arg))
merge_from[ins].insert(arg);
......@@ -415,18 +415,18 @@ struct stream_info
}
std::unordered_map<instruction_ref, std::unordered_set<instruction_ref>>
get_conflicts(module& p)
get_conflicts(module& m)
{
using conflict_table_type =
std::unordered_map<instruction_ref, std::unordered_set<instruction_ref>>;
conflict_table_type conflict_table;
auto concur_ins = this->find_concurrent_instructions(p);
auto concur_ins = this->find_concurrent_instructions(m);
// Compute an index for each instruction
std::unordered_map<instruction_ref, std::size_t> ins2index;
std::size_t index_total = 0;
for(auto ins : iterator_for(p))
for(auto ins : iterator_for(m))
ins2index[ins] = index_total++;
std::vector<conflict_table_type> thread_conflict_tables(
......@@ -507,21 +507,21 @@ struct stream_info
}
};
void schedule::apply(module& p) const
void schedule::apply(module& m) const
{
if(not enable)
return;
stream_info si;
si.calc_implicit_deps(p);
auto last = std::prev(p.end());
si.calc_implicit_deps(m);
auto last = std::prev(m.end());
si.accumulate_weights(last, model);
auto nstreams = si.assign_streams(p, model.concurrency());
si.sort(p, model.concurrency());
auto nstreams = si.assign_streams(m, model.concurrency());
si.sort(m, model.concurrency());
if(enabled(MIGRAPHX_TRACE_COMPILE{}) or enabled(MIGRAPHX_TRACE_SCHEDULE{}))
{
p.annotate(std::cout, [&](auto ins) {
m.annotate(std::cout, [&](auto ins) {
if(ins->name() == "@param" and not contains(si.weights, ins))
return;
......@@ -548,9 +548,9 @@ void schedule::apply(module& p) const
std::unordered_map<instruction_ref, std::size_t> ins2wait;
std::unordered_map<std::size_t, std::unordered_set<std::size_t>> waited_for;
std::unordered_map<instruction_ref, std::unordered_set<std::size_t>> ins2waited;
ins2wait.reserve(p.size());
ins2waited.reserve(p.size());
for(auto ins : iterator_for(p))
ins2wait.reserve(m.size());
ins2waited.reserve(m.size());
for(auto ins : iterator_for(m))
{
// Only schedule instructions that have a stream
if(not si.has_stream(ins))
......@@ -559,7 +559,7 @@ void schedule::apply(module& p) const
// Schedule instruction on the stream
auto stream = si.get_stream(ins);
assert(stream < model.concurrency());
model.sched(p, ins, stream);
model.sched(m, ins, stream);
// Insert wait instructions
if(si.is_merge_point(ins, stream))
{
......@@ -572,14 +572,14 @@ void schedule::apply(module& p) const
if(not contains(ins2wait, i))
{
ins2wait[i] = wait_id;
model.record(p, i, wait_id);
model.record(m, i, wait_id);
wait_id++;
}
auto w = ins2wait.at(i);
// If we already waited for the event on this stream then dont
// insert another wait event
if(not contains(waited_for[stream], w))
model.wait(p, ins, w);
model.wait(m, ins, w);
// Store the event as waited
waited_for[stream].insert(w);
// Store all wait events that have been waited on prior to the recorded instruction
......@@ -594,7 +594,7 @@ void schedule::apply(module& p) const
}
// Add memory conflicts
auto conflict_table = si.get_conflicts(p);
auto conflict_table = si.get_conflicts(m);
for(auto&& ip : conflict_table)
{
if(ip.second.empty())
......@@ -602,7 +602,7 @@ void schedule::apply(module& p) const
std::vector<instruction_ref> args;
args.push_back(ip.first);
args.insert(args.end(), ip.second.begin(), ip.second.end());
p.insert_instruction(std::next(ip.first), make_op("identity"), args);
m.insert_instruction(std::next(ip.first), make_op("identity"), args);
}
}
......
src/simplify_algebra.cpp
View file @ faefeef9
This diff is collapsed.
src/simplify_qdq.cpp
View file @ faefeef9
......@@ -53,7 +53,7 @@ struct match_find_quantizable_ops
match::arg(1)(dequantizelinear_op("x2", "scale2")));
}
void apply(module& m, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto qop = r.result;
auto q1 = r.instructions["x1"];
......
src/simplify_reshapes.cpp
View file @ faefeef9
......@@ -70,19 +70,19 @@ struct find_reshaper
match::any_of[match::outputs()](match::name(reshaper_names())));
}
void apply(module& p, const match::matcher_result& mr) const
void apply(module& m, const match::matcher_result& mr) const
{
auto ins = mr.result;
std::vector<instruction_ref> reshapes{ins};
while(is_reshaper(reshapes.back()))
{
assert(!reshapes.back()->inputs().empty());
assert(p.has_instruction(reshapes.back()->inputs().front()));
assert(m.has_instruction(reshapes.back()->inputs().front()));
auto input = reshapes.back()->inputs().front();
reshapes.push_back(input);
}
std::pair<instruction_ref, instruction_ref> r{p.end(), p.end()};
std::pair<instruction_ref, instruction_ref> r{m.end(), m.end()};
for(auto start : iterator_for(reshapes))
{
auto last = std::find_if(reshapes.rbegin(), reshapes.rend(), [&](auto&& i) {
......@@ -96,7 +96,7 @@ struct find_reshaper
}
if(r.first != r.second)
{
p.replace_instruction(r.first, r.second);
m.replace_instruction(r.first, r.second);
}
}
};
......@@ -117,10 +117,10 @@ struct find_nop_reshapes
return match::name(reshapes)(match::same_shape(match::arg(0)));
}
void apply(module& p, const match::matcher_result& mr) const
void apply(module& m, const match::matcher_result& mr) const
{
auto ins = mr.result;
p.replace_instruction(ins, ins->inputs().front());
m.replace_instruction(ins, ins->inputs().front());
}
};
......@@ -132,7 +132,7 @@ struct find_transpose
match::skip_output(match::name("contiguous"))(match::name("transpose"))));
}
void apply(module& p, const match::matcher_result& mr) const
void apply(module& m, const match::matcher_result& mr) const
{
auto ins = mr.result;
auto x = ins;
......@@ -149,11 +149,11 @@ struct find_transpose
return;
if(is_no_transpose(dims))
{
p.replace_instruction(ins, t->inputs().front());
m.replace_instruction(ins, t->inputs().front());
}
else
{
p.replace_instruction(
m.replace_instruction(
ins, make_op("transpose", {{"permutation", dims}}), t->inputs().front());
}
}
......@@ -223,7 +223,7 @@ struct find_nested_slice
return result;
}
void apply(module& p, const match::matcher_result& mr) const
void apply(module& m, const match::matcher_result& mr) const
{
auto ins = mr.result;
auto slice = ins->inputs().front();
......@@ -241,7 +241,7 @@ struct find_nested_slice
op.starts.push_back(pp.second.first);
op.ends.push_back(pp.second.second);
}
p.replace_instruction(ins, op, input);
m.replace_instruction(ins, op, input);
}
};
......@@ -252,7 +252,7 @@ struct find_concat_transpose
return match::name("concat")(match::all_of[match::inputs()](match::transpose_shape()));
}
void apply(module& p, const match::matcher_result& mr) const
void apply(module& m, const match::matcher_result& mr) const
{
auto ins = mr.result;
auto trans_inputs = ins->inputs();
......@@ -279,14 +279,14 @@ struct find_concat_transpose
std::vector<instruction_ref> inputs;
std::transform(
ins->inputs().begin(), ins->inputs().end(), std::back_inserter(inputs), [&](auto i) {
return p.insert_instruction(
return m.insert_instruction(
ins, make_op("transpose", {{"permutation", permutation}}), i);
});
auto concat = p.insert_instruction(ins, op, inputs);
auto t = p.insert_instruction(
auto concat = m.insert_instruction(ins, op, inputs);
auto t = m.insert_instruction(
ins, make_op("transpose", {{"permutation", ipermutation}}), concat);
assert(ins->get_shape().lens() == t->get_shape().lens());
p.replace_instruction(ins, t);
m.replace_instruction(ins, t);
}
};
......@@ -303,7 +303,7 @@ struct find_nested_concat
return op.axis;
}
void apply(module& p, const match::matcher_result& mr) const
void apply(module& m, const match::matcher_result& mr) const
{
auto ins = mr.result;
auto axis = get_axis(ins);
......@@ -317,7 +317,7 @@ struct find_nested_concat
args.push_back(i);
}
})(ins->inputs());
p.replace_instruction(ins, ins->get_operator(), args);
m.replace_instruction(ins, ins->get_operator(), args);
}
};
......@@ -329,7 +329,7 @@ struct find_resize
match::args(match::name("reshape").bind("data"), match::is_constant().bind("ind")));
}
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto ins_rsp = r.instructions["data"];
......@@ -417,13 +417,13 @@ struct find_resize
}
auto in_rsp = ins_rsp->inputs().front();
auto rsp_data = p.insert_instruction(
auto rsp_data = m.insert_instruction(
ins_rsp, migraphx::make_op("reshape", {{"dims", in_dims}}), in_rsp);
auto mb_rsp = p.insert_instruction(
auto mb_rsp = m.insert_instruction(
ins_rsp, migraphx::make_op("multibroadcast", {{"out_lens", out_dims}}), rsp_data);
auto std_mb = p.insert_instruction(ins, migraphx::make_op("contiguous"), mb_rsp);
auto std_mb = m.insert_instruction(ins, migraphx::make_op("contiguous"), mb_rsp);
std::vector<int64_t> rsp_dims(out_lens.begin(), out_lens.end());
p.replace_instruction(ins, migraphx::make_op("reshape", {{"dims", rsp_dims}}), std_mb);
m.replace_instruction(ins, migraphx::make_op("reshape", {{"dims", rsp_dims}}), std_mb);
}
};
......@@ -436,7 +436,7 @@ struct find_where_op
match::is_constant().bind("ind")));
}
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto concat = r.instructions["data"];
......@@ -475,11 +475,11 @@ struct find_where_op
if(val)
{
p.replace_instruction(ins, inputs.at(0));
m.replace_instruction(ins, inputs.at(0));
}
else
{
p.replace_instruction(ins, inputs.at(1));
m.replace_instruction(ins, inputs.at(1));
}
}
};
......@@ -496,7 +496,7 @@ struct find_reshape_cont
match::any()));
}
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto ins_cont = r.instructions["cont"];
......@@ -530,11 +530,11 @@ struct find_reshape_cont
else
{
inputs.push_back(
p.insert_instruction(ins, make_op("reshape", {{"dims", dims}}), in));
m.insert_instruction(ins, make_op("reshape", {{"dims", dims}}), in));
}
}
auto out = p.insert_instruction(ins, ins->get_operator(), inputs);
p.replace_instruction(ins, make_op("reshape", {{"dims", out_dims}}), out);
auto out = m.insert_instruction(ins, ins->get_operator(), inputs);
m.replace_instruction(ins, make_op("reshape", {{"dims", out_dims}}), out);
}
};
......@@ -564,25 +564,25 @@ struct find_transpose_contiguous_reshaper_unary
match::args(match_transpose_contiguous_reshaper()));
}
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto reshaper_ins = r.instructions["reshaper_ins"];
auto trans_ins = r.instructions["trans_ins"];
auto cont_ins = r.instructions["cont_ins"];
auto unary_op_name = ins->get_operator().name();
auto unary_ins = p.insert_instruction(cont_ins, make_op(unary_op_name), trans_ins);
auto new_cont_ins = p.insert_instruction(cont_ins, make_op("contiguous"), unary_ins);
auto unary_ins = m.insert_instruction(cont_ins, make_op(unary_op_name), trans_ins);
auto new_cont_ins = m.insert_instruction(cont_ins, make_op("contiguous"), unary_ins);
// older cont and reshape are removed by deadcode elimination
p.replace_instruction(ins, reshaper_ins->get_operator(), new_cont_ins);
m.replace_instruction(ins, reshaper_ins->get_operator(), new_cont_ins);
}
};
void simplify_reshapes::apply(module& p) const
void simplify_reshapes::apply(module& m) const
{
for(int i = 0; i < 2; i++)
{
match::find_matches(p,
match::find_matches(m,
find_where_op{},
find_resize{},
find_reshape_cont{},
......@@ -594,7 +594,7 @@ void simplify_reshapes::apply(module& p) const
find_nested_slice{},
find_nested_concat{},
find_transpose_contiguous_reshaper_unary{});
dead_code_elimination{}.apply(p);
dead_code_elimination{}.apply(m);
}
}
......
src/targets/cpu/lowering.cpp
View file @ faefeef9
......@@ -352,7 +352,7 @@ struct cpu_apply
std::transform(bind_inputs.begin(),
bind_inputs.end(),
std::back_inserter(inputs),
[&](const auto& s) { return r.instructions.at(s); });
[&](const auto& s) { return r.instructions[s]; });
inputs.push_back(this->insert_allocation(ins, ins->get_shape()));
modl->replace_instruction(ins, op, inputs);
});
......
src/targets/gpu/CMakeLists.txt
View file @ faefeef9
......@@ -158,6 +158,7 @@ add_library(migraphx_gpu
nonzero.cpp
pack_args.cpp
pack_int8_args.cpp
prefuse_ops.cpp
pad.cpp
pooling.cpp
quant_convolution.cpp
......
src/targets/gpu/analyze_streams.cpp
View file @ faefeef9
......@@ -28,30 +28,30 @@ struct hip_stream_model
bool is_wait(migraphx::instruction_ref ins) const { return ins->name() == "gpu::wait_event"; }
};
stream_model make_stream_model(const module& p)
stream_model make_stream_model(const module& m)
{
hip_stream_model m;
hip_stream_model hsm;
std::size_t stream = 0;
for(auto ins : iterator_for(p))
for(auto ins : iterator_for(m))
{
if(ins->name() == "gpu::set_stream")
{
auto v = ins->get_operator().to_value();
stream = v["stream"].to<std::size_t>();
m.max_stream = std::max(stream, m.max_stream);
auto v = ins->get_operator().to_value();
stream = v["stream"].to<std::size_t>();
hsm.max_stream = std::max(stream, hsm.max_stream);
}
if(ins->get_operator().is_context_free())
continue;
if(contains({"hip::hip_allocate_memory", "hip::hip_copy_literal", "@param"}, ins->name()))
continue;
m.ins2stream[ins] = stream;
hsm.ins2stream[ins] = stream;
}
return m;
return hsm;
}
std::vector<stream_race> analyze_streams(const module& p)
std::vector<stream_race> analyze_streams(const module& m)
{
return migraphx::analyze_streams(p, make_stream_model(p));
return migraphx::analyze_streams(m, make_stream_model(m));
}
} // namespace gpu
......
src/targets/gpu/compile_hip.cpp
View file @ faefeef9
......@@ -22,6 +22,7 @@ namespace gpu {
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_GPU_DEBUG);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_GPU_OPTIMIZE);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_GPU_DUMP_ASM);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_GPU_DUMP_SRC);
#if MIGRAPHX_USE_HIPRTC
......@@ -247,6 +248,16 @@ compile_hip_src(const std::vector<src_file>& srcs, std::string params, const std
MIGRAPHX_THROW("Missing hsaco");
};
if(enabled(MIGRAPHX_GPU_DUMP_SRC{}))
{
for(const auto& src : srcs)
{
if(src.path.extension() != ".cpp")
continue;
std::cout << std::string(src.content.first, src.len()) << std::endl;
}
}
if(enabled(MIGRAPHX_GPU_DUMP_ASM{}))
{
......
src/targets/gpu/eliminate_workspace.cpp
View file @ faefeef9
......@@ -11,11 +11,11 @@ namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
void eliminate_workspace::apply(module& p) const
void eliminate_workspace::apply(module& m) const
{
std::size_t n = 0;
std::vector<instruction_ref> allocs;
for(auto ins : iterator_for(p))
for(auto ins : iterator_for(m))
{
if(ins->outputs().size() != 1)
continue;
......@@ -30,11 +30,11 @@ void eliminate_workspace::apply(module& p) const
}
if(n > 0)
{
auto ws = p.add_parameter("workspace", shape{shape::int8_type, {n}});
auto ws = m.add_parameter("workspace", shape{shape::int8_type, {n}});
for(auto&& a : allocs)
{
p.replace_instruction(a, ws);
p.remove_instruction(a);
m.replace_instruction(a, ws);
m.remove_instruction(a);
}
}
}
......
src/targets/gpu/fuse_ops.cpp
View file @ faefeef9
......@@ -316,7 +316,7 @@ struct find_layernorm
{
auto matcher() const { return match::layernorm(&gpu_name); }
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto x_ins = r.instructions["x"];
......@@ -331,7 +331,7 @@ struct find_layernorm
if(relements > 1024 or (relements % 4 != 0 and relements > 256))
return;
p.replace_instruction(ins, hip_layernorm{}, x_ins, args.back());
m.replace_instruction(ins, hip_layernorm{}, x_ins, args.back());
}
};
......@@ -343,11 +343,11 @@ struct find_triadd_layernorm
match::used_once(), match::all_of[match::inputs()](match::standard_shape()))));
}
void apply(module& p, const match::matcher_result& r) const
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto triadd = ins->inputs().front();
p.replace_instruction(ins, hip_triadd_layernorm{}, triadd->inputs());
m.replace_instruction(ins, hip_triadd_layernorm{}, triadd->inputs());
}
};
......@@ -355,13 +355,13 @@ struct find_gelu
{
auto matcher() const { return match::gelu_erf(&gpu_name); }
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto x_ins = r.instructions["x"];
auto args = ins->inputs();
p.replace_instruction(ins, hip_gelu{}, x_ins, args.back());
m.replace_instruction(ins, hip_gelu{}, x_ins, args.back());
}
};
......@@ -372,7 +372,7 @@ struct find_add_gelu
return match::name("gpu::gelu")(match::arg(0)(match::name("gpu::add").bind("add")));
}
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto add_ins = r.instructions["add"];
auto ins = r.result;
......@@ -381,7 +381,7 @@ struct find_add_gelu
move_broadcasted_back(args);
args.back() = ins->inputs().back();
p.replace_instruction(ins, hip_add_gelu{}, args);
m.replace_instruction(ins, hip_add_gelu{}, args);
}
};
......@@ -391,16 +391,16 @@ struct find_gelu_new
auto matcher() const { return match::gelu_tanh(&gpu_name); }
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto x_ins = r.instructions["x"];
auto args = ins->inputs();
if(fast_math)
p.replace_instruction(ins, hip_gelu{}, x_ins, args.back());
m.replace_instruction(ins, hip_gelu{}, x_ins, args.back());
else
p.replace_instruction(ins, hip_gelu_new{}, x_ins, args.back());
m.replace_instruction(ins, hip_gelu_new{}, x_ins, args.back());
}
};
......@@ -411,7 +411,7 @@ struct find_add_gelu_new
return match::name("gpu::gelu_new")(match::arg(0)(match::name("gpu::add").bind("add")));
}
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto add_ins = r.instructions["add"];
auto ins = r.result;
......@@ -420,7 +420,7 @@ struct find_add_gelu_new
move_broadcasted_back(args);
args.back() = ins->inputs().back();
p.replace_instruction(ins, hip_add_gelu_new{}, args);
m.replace_instruction(ins, hip_add_gelu_new{}, args);
}
};
......@@ -435,7 +435,7 @@ struct find_add_clip
.bind("add")));
}
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto add_ins = r.instructions["add"];
auto ins = r.result;
......@@ -448,9 +448,9 @@ struct find_add_clip
add_args.pop_back();
add_args.insert(add_args.end(), std::next(ins_args.begin()), ins_args.end());
if(add_ins->name() == "gpu::add")
p.replace_instruction(ins, hip_add_clip{}, add_args);
m.replace_instruction(ins, hip_add_clip{}, add_args);
else if(add_ins->name() == "gpu::triadd")
p.replace_instruction(ins, hip_triadd_clip{}, add_args);
m.replace_instruction(ins, hip_triadd_clip{}, add_args);
}
};
......@@ -470,7 +470,7 @@ struct find_add_unary
.bind("add")));
}
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto add_ins = r.instructions["add"];
auto ins = r.result;
......@@ -481,9 +481,9 @@ struct find_add_unary
// Use the allocation from the relu operator
args.back() = ins->inputs().back();
if(add_ins->name() == "gpu::add")
p.replace_instruction(ins, binary_add_op, args);
m.replace_instruction(ins, binary_add_op, args);
else if(add_ins->name() == "gpu::triadd")
p.replace_instruction(ins, ternary_add_op, args);
m.replace_instruction(ins, ternary_add_op, args);
}
};
......@@ -498,7 +498,7 @@ struct find_triadd
.bind("input")));
}
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto add_ins = r.instructions["add"];
auto input_ins = r.instructions["input"];
......@@ -513,7 +513,7 @@ struct find_triadd
move_broadcasted_back(args);
args.back() = ins->inputs().back();
p.replace_instruction(ins, hip_triadd{}, args);
m.replace_instruction(ins, hip_triadd{}, args);
}
};
......@@ -525,7 +525,7 @@ struct find_mul_add
match::name("gpu::mul")(match::used_once()).bind("mul"), match::any().bind("b")));
}
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto mul_ins = r.instructions["mul"];
auto b_ins = r.instructions["b"];
......@@ -538,7 +538,7 @@ struct find_mul_add
args.insert(std::prev(args.end()), b_ins);
args.back() = ins->inputs().back();
p.replace_instruction(ins, hip_mul_add{}, args);
m.replace_instruction(ins, hip_mul_add{}, args);
}
};
......@@ -550,7 +550,7 @@ struct find_mul_add_relu
match::arg(0)(match::name("gpu::mul_add")(match::used_once()).bind("mul_add")));
}
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto mul_add_ins = r.instructions["mul_add"];
auto ins = r.result;
......@@ -558,7 +558,7 @@ struct find_mul_add_relu
// Use the allocation from the relu operator
args.back() = ins->inputs().back();
p.replace_instruction(ins, hip_mul_add_relu{}, args);
m.replace_instruction(ins, hip_mul_add_relu{}, args);
}
};
......@@ -783,7 +783,7 @@ auto conv_bias(Ms... ms)
}
template <class Op>
void apply_conv_bias(context& ctx, module& p, match::matcher_result r)
void apply_conv_bias(context& ctx, module& m, const match::matcher_result& r)
{
auto conv_ins = r.instructions["conv"];
auto bias_ins = r.instructions["bias"];
......@@ -798,7 +798,7 @@ void apply_conv_bias(context& ctx, module& p, match::matcher_result r)
// TODO: Insert ws allocation
auto ws = cb.get_workspace(ctx);
(void)ws;
p.replace_instruction(ins, cb, input_ins, weights_ins, old_ws_ins, bias_ins, alloc_ins);
m.replace_instruction(ins, cb, input_ins, weights_ins, old_ws_ins, bias_ins, alloc_ins);
}
inline auto precompile_name(std::string s) // NOLINT
......@@ -829,9 +829,9 @@ struct find_conv_bias
match::output(match::name(std::unordered_set<std::string>{"gpu::relu"}))));
}
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
apply_conv_bias<miopen_conv_bias>(*ctx, p, std::move(r));
apply_conv_bias<miopen_conv_bias>(*ctx, m, r);
}
};
......@@ -840,9 +840,9 @@ struct find_conv_bias_relu
context* ctx = nullptr;
auto matcher() const { return match::name("gpu::relu")(match::arg(0)(conv_bias())); }
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
apply_conv_bias<miopen_conv_bias_relu>(*ctx, p, std::move(r));
apply_conv_bias<miopen_conv_bias_relu>(*ctx, m, r);
}
};
......@@ -857,7 +857,7 @@ struct find_conv_pointwise
fusable_conv(match::used_once()).bind("conv")));
}
void apply(module& m, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto conv_ins = r.instructions["conv"];
auto bias_ins = r.instructions["bias"];
......@@ -896,7 +896,7 @@ struct find_gemm_add
match::name("gpu::gemm")(match::nargs(3)).bind("gemm")));
}
void apply(module& p, match::matcher_result r) const
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto gemm_ins = r.instructions["gemm"];
......@@ -908,26 +908,68 @@ struct find_gemm_add
if(not float_equal(gemm.beta, 0))
return;
if(std::any_of(ins->inputs().begin(), ins->inputs().end(), [](auto i) {
return not i->get_shape().standard();
}))
return;
auto inputs = gemm_ins->inputs();
inputs.pop_back();
auto copy_ins = c_ins;
// Insert copy
if(ins == p.end() or c_ins->outputs().size() > 1 or c_ins->inputs().empty())
if(ins == m.end() or c_ins->outputs().size() > 1 or c_ins->inputs().empty())
{
copy_ins = p.insert_instruction(ins, hip_copy{}, c_ins, ins->inputs().back());
copy_ins = m.insert_instruction(ins, hip_copy{}, c_ins, ins->inputs().back());
}
inputs.push_back(copy_ins);
inputs.push_back(copy_ins);
gemm.beta = 1;
p.replace_instruction(ins, gemm, inputs);
m.replace_instruction(ins, gemm, inputs);
}
};
auto pointwise_name(const std::string& s)
{
return precompile_name("pointwise")(match::make_basic_pred_matcher([=](auto ins) {
module_ref pm = ins->module_inputs().front();
auto n = std::count_if(pm->begin(), pm->end(), [&](auto& i) { return i.name() == s; });
if(n != 1)
return false;
return std::all_of(pm->begin(), pm->end(), [&](auto& i) {
return starts_with(i.name(), "@") or i.name() == s;
});
}));
}
struct find_gemm_pointwise
{
auto matcher() const
{
return pointwise_name("add")(
match::nargs(3),
match::all_of[match::inputs()](match::standard_shape()),
match::either_arg(0, 1)(match::used_once().bind("c"),
match::name("gpu::gemm")(match::nargs(3)).bind("gemm")));
}
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto gemm_ins = r.instructions["gemm"];
auto c_ins = r.instructions["c"];
auto gemm = any_cast<rocblas_gemm<op::dot>>(gemm_ins->get_operator());
// Already fused gemm
if(not float_equal(gemm.beta, 0))
return;
auto inputs = gemm_ins->inputs();
inputs.pop_back();
inputs.push_back(c_ins);
inputs.push_back(gemm_ins->inputs().back());
gemm.beta = 1;
m.replace_instruction(ins, gemm, inputs);
}
};
......@@ -938,22 +980,22 @@ struct find_commutative_broadcast
return match::name("gpu::add", "gpu::mul")(match::arg(1)(match::broadcast_shape()));
}
void apply(module& p, const match::matcher_result& r) const
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto args = ins->inputs();
move_broadcasted_back(args);
p.replace_instruction(ins, ins->get_operator(), args);
m.replace_instruction(ins, ins->get_operator(), args);
}
};
void fuse_ops::apply(module& p) const
void fuse_ops::apply(module& m) const
{
match::find_matches(p, find_gelu{}, find_gelu_new{fast_math});
run_passes(p, {dead_code_elimination{}});
match::find_matches(p, find_triadd{});
match::find_matches(p,
match::find_matches(m, find_gelu{}, find_gelu_new{fast_math});
run_passes(m, {dead_code_elimination{}});
match::find_matches(m, find_triadd{});
match::find_matches(m,
find_layernorm{},
find_conv_pointwise{ctx},
find_conv_bias_relu{ctx},
......@@ -966,8 +1008,12 @@ void fuse_ops::apply(module& p) const
find_add_unary{"gpu::sigmoid", hip_add_sigmoid{}, hip_triadd_sigmoid{}},
find_add_unary{"gpu::tanh", hip_add_tanh{}, hip_triadd_tanh{}},
find_add_clip{});
run_passes(p, {dead_code_elimination{}});
match::find_matches(p, find_triadd_layernorm{}, find_gemm_add{}, find_commutative_broadcast{});
run_passes(m, {dead_code_elimination{}});
match::find_matches(m,
find_triadd_layernorm{},
find_gemm_add{},
find_gemm_pointwise{},
find_commutative_broadcast{});
}
} // namespace gpu
......
src/targets/gpu/gemm_impl.cpp
View file @ faefeef9
#include <rocblas.h>
#include <migraphx/gpu/gemm_impl.hpp>
#include <migraphx/reduce_dims.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -27,6 +28,22 @@ rocblas_datatype get_type(shape::type_t type)
MIGRAPHX_THROW("ROCBLAS_GEMM: data type not supported!");
}
void blas_shape(const shape& s)
{
if(s.lens().size() < 2)
return;
if(std::none_of(s.strides().end() - 2, s.strides().end(), [&](auto i) { return i == 1; }))
MIGRAPHX_THROW("GPU_GEMM: needs to have one matrix stride as 1");
if(s.lens().size() < 3)
return;
shape batch_shape{s.type(),
{s.lens().begin(), s.lens().end() - 2},
{s.strides().begin(), s.strides().end() - 2}};
auto batch_shapes = reduce_dims({batch_shape});
if(batch_shapes.front().lens().size() != 1)
MIGRAPHX_THROW("GPU_GEMM: Batch dimension is not collapsible");
}
template <class R, class... Ts, class... Us>
R rocblas_invoke(R (*f)(Ts...), Us... xs)
{
......@@ -36,6 +53,18 @@ R rocblas_invoke(R (*f)(Ts...), Us... xs)
return f(xs..., nullptr, nullptr);
}
static bool is_transposed(const shape& s)
{
if(not s.transposed())
return false;
return s.strides().back() != 1;
}
static rocblas_int get_batch_stride(const argument& a)
{
return a.get_shape().strides()[a.get_shape().strides().size() - 3];
}
template <class T>
void gemm_impl(context& ctx,
const shape& output_shape,
......@@ -45,8 +74,8 @@ void gemm_impl(context& ctx,
bool int8_x4_format,
bool compute_fp32)
{
bool transa = args[0].get_shape().transposed();
bool transb = args[1].get_shape().transposed();
bool transa = is_transposed(args[0].get_shape());
bool transb = is_transposed(args[1].get_shape());
auto n_dim = output_shape.lens().size();
auto dim_1 = n_dim - 1;
auto dim_0 = n_dim - 2;
......@@ -142,6 +171,9 @@ void gemm_impl(context& ctx,
}
else
{
auto a_stride = get_batch_stride(args[0]);
auto b_stride = get_batch_stride(args[1]);
auto c_stride = get_batch_stride(args[2]);
rocblas_invoke(&rocblas_gemm_strided_batched_ex,
ctx.get_stream().get_rocblas(),
transb ? rocblas_operation_transpose : rocblas_operation_none,
......@@ -153,20 +185,20 @@ void gemm_impl(context& ctx,
to_pointer(args.at(1)),
arg_type,
ldb,
k * n,
b_stride,
to_pointer(args.at(0)),
arg_type,
lda,
m * k,
a_stride,
beta_v,
to_pointer(args[2]),
output_type,
ldc,
m * n,
c_stride,
is_3inputs ? to_pointer(args[3]) : to_pointer(args[2]),
output_type,
ldc,
m * n,
c_stride,
num_matrices,
compute_type,
rocblas_gemm_algo_standard,
......
src/targets/gpu/include/migraphx/gpu/analyze_streams.hpp
View file @ faefeef9
......@@ -11,7 +11,7 @@ struct module;
namespace gpu {
std::vector<stream_race> analyze_streams(const module& p);
std::vector<stream_race> analyze_streams(const module& m);
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
......
src/targets/gpu/include/migraphx/gpu/eliminate_workspace.hpp
View file @ faefeef9
......@@ -14,7 +14,7 @@ namespace gpu {
struct eliminate_workspace
{
std::string name() const { return "eliminate_workspace"; }
void apply(module& p) const;
void apply(module& m) const;
};
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
......
src/targets/gpu/include/migraphx/gpu/fuse_ops.hpp
View file @ faefeef9
......@@ -16,7 +16,7 @@ struct fuse_ops
context* ctx = nullptr;
bool fast_math = true;
std::string name() const { return "gpu::fuse_ops"; }
void apply(module& p) const;
void apply(module& m) const;
};
} // namespace gpu
......
src/targets/gpu/include/migraphx/gpu/gemm.hpp
View file @ faefeef9
......@@ -18,6 +18,8 @@ namespace gpu {
struct context;
void blas_shape(const shape& s);
template <class Op>
struct rocblas_gemm
{
......@@ -50,13 +52,14 @@ struct rocblas_gemm
std::vector<shape> in_shapes(inputs);
in_shapes.pop_back();
check_shapes{in_shapes, *this}.not_broadcasted();
batch_not_transposed(inputs[0].strides());
batch_not_transposed(inputs[1].strides());
blas_shape(inputs[0]);
blas_shape(inputs[1]);
// if gemm and add are fused
if(not float_equal(beta, 0))
if(in_shapes.size() > 2)
{
auto cmat_shape = in_shapes.back();
in_shapes.pop_back();
blas_shape(cmat_shape);
auto op_out_shape = op.compute_shape(in_shapes);
if(cmat_shape.lens() != op_out_shape.lens())
{
......@@ -71,6 +74,7 @@ struct rocblas_gemm
to_string(cmat_shape.type()) +
", it must be: " + to_string(op_out_shape.type()));
}
return op_out_shape;
}
return op.compute_shape(in_shapes);
......@@ -96,28 +100,6 @@ struct rocblas_gemm
return args.back();
}
void batch_not_transposed(const std::vector<std::size_t>& strides) const
{
if(strides.size() <= 2)
return;
auto dim_0 = strides.size() - 2;
auto matrix_size = std::max(strides[dim_0], strides[dim_0 + 1]);
std::vector<std::size_t> batch(strides.begin(), strides.begin() + dim_0);
if(std::all_of(batch.begin(), batch.end(), [&](auto i) { return (i < matrix_size); }))
{
MIGRAPHX_THROW("GPU_GEMM: matrix size and batch size {" + to_string_range(strides) +
"} are transposed!");
}
if(std::adjacent_find(batch.begin(), batch.end(), [&](auto i, auto j) {
return (i < j or i < matrix_size or j < matrix_size);
}) != batch.end())
{
MIGRAPHX_THROW("GPU_GEMM: batch size {" + to_string_range(strides) +
"} is transposed!");
}
}
std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
{
return shapes.size() - 1;
......
src/targets/gpu/include/migraphx/gpu/prefuse_ops.hpp 0 → 100644
View file @ faefeef9
#ifndef MIGRAPHX_GUARD_GPU_PREFUSE_OPS_HPP
#define MIGRAPHX_GUARD_GPU_PREFUSE_OPS_HPP
#include <migraphx/config.hpp>
#include <migraphx/gpu/context.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
struct module;
namespace gpu {
struct prefuse_ops
{
std::string name() const { return "gpu::prefuse_ops"; }
void apply(module& m) const;
};
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif // MIGRAPHX_GUARD_GPU_PREFUSE_OPS_HPP
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