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[VM][Adreno] Fix using buffers for weights in VM #15671

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Sep 6, 2023
Merged

[VM][Adreno] Fix using buffers for weights in VM #15671

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58244be
[VM][Adreno] Fix using buffers for weights in VM
echuraev Sep 5, 2023
a9b41da
Fix ci
echuraev Sep 6, 2023
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8 changes: 5 additions & 3 deletions src/relay/transforms/annotate_texture_storage.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -174,8 +174,11 @@ class StorageInfo : private transform::DeviceAwareExprVisitor {
for (const auto& ttype : FlattenTupleType(fn->params[i]->checked_type())) {
std::string scope = Scope(ttype->shape, GetVirtualDevice(GetRef<Expr>(call)));
if (expr_attrib.as<Conv2DAttrs>() || expr_attrib.as<Conv2DWinogradAttrs>()) {
String kernel_layout = expr_attrib.as<Conv2DAttrs>()
? expr_attrib.as<Conv2DAttrs>()->kernel_layout
: expr_attrib.as<Conv2DWinogradAttrs>()->kernel_layout;
if ((i == weights_pos) && !ttype->dtype.is_float16() &&
CanUseBuffers(call->args[i], ttype->shape, fn->attrs)) {
CanUseBuffers(call->args[i], ttype->shape, kernel_layout)) {
buffers_params.insert(fn->params[i]);
buffers_args.insert(call->args[i]);
scope = "global";
Expand Down Expand Up @@ -426,10 +429,9 @@ class StorageInfo : private transform::DeviceAwareExprVisitor {
}

bool CanUseBuffers(const Expr param, const Array<PrimExpr> shape,
const tvm::DictAttrs param_attrs) const {
const String kernel_layout) const {
bool use_buffer = false;
if (param.as<ConstantNode>() && shape.size() == 5) {
auto kernel_layout = param_attrs.GetAttr<String>("kernel_layout");
if (kernel_layout == "HWOI4o" || kernel_layout == "HWIO4o") {
int a0 = shape[0].as<IntImmNode>()->value;
int a1 = shape[1].as<IntImmNode>()->value;
Expand Down
77 changes: 66 additions & 11 deletions tests/python/relay/opencl_texture/test_conv2d_nchw_texture.py
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Original file line number Diff line number Diff line change
Expand Up @@ -692,7 +692,6 @@ def test_residual_block(remote, target, executor_type, dtype):
{"data": input_shape},
{"data": dtype},
target,
static_memory_scope,
)


Expand Down Expand Up @@ -790,11 +789,12 @@ def test_concat(remote, target, executor_type, dtype):

static_memory_scope = [
"",
"global.texture",
"global",
"global.texture-weight",
"global.texture-weight",
"global",
"global.texture-weight",
"global.texture-nhwc",
"global",
"global.texture-weight",
"",
"",
Expand All @@ -803,8 +803,6 @@ def test_concat(remote, target, executor_type, dtype):
"",
]

static_memory_scope = []

if executor_type == "ge":
build_run_compare(
remote,
Expand All @@ -823,7 +821,6 @@ def test_concat(remote, target, executor_type, dtype):
{"data": input_shape},
{"data": dtype},
target,
static_memory_scope,
)


Expand Down Expand Up @@ -968,7 +965,6 @@ def test_pooling_branching_texture_params(remote, target, executor_type, dtype):
{"data": input_shape},
{"data": dtype},
target,
static_memory_scope,
)


Expand Down Expand Up @@ -1111,7 +1107,6 @@ def test_branching_texture_params(remote, target, executor_type, dtype):
{"data": input_shape},
{"data": dtype},
target,
static_memory_scope,
)


Expand Down Expand Up @@ -1212,7 +1207,6 @@ def test_conv2d_different_lowering_same_op(remote, target, executor_type, dtype)
{"data": input_shape},
{"data": dtype},
target,
static_memory_scope,
)


Expand Down Expand Up @@ -1380,7 +1374,6 @@ def test_injective_nwo_inputs1(remote, target, executor_type, dtype):
{"data": input_shape},
{"data": dtype},
target,
static_memory_scope,
)


Expand Down Expand Up @@ -1495,7 +1488,6 @@ def test_injective_nwo_inputs2(remote, target, executor_type, dtype):
{"data": input_shape},
{"data": dtype},
target,
static_memory_scope,
)


Expand Down Expand Up @@ -1534,5 +1526,68 @@ def test_conv2d_to_3_channels(remote, target, executor_type, dtype):
)


@tvm.testing.requires_opencl
@tvm.testing.parametrize_targets("opencl -device=adreno")
def test_conv2d_weight_on_buffers(remote, target, executor_type, dtype):
target = "opencl -device=adreno"
input_shape = (1, 64, 75, 75)
filter_shape = (64, 64, 3, 3)
bias_shape = (64,)
A = relay.var("data", shape=input_shape, dtype=dtype)
W = relay.var("weight", shape=filter_shape, dtype=dtype)
BS = relay.var("bias", shape=bias_shape, dtype=dtype)
conv = relay.nn.conv2d(A, W, padding=[1, 1, 1, 1], channels=64, kernel_size=(3, 3))
conv = relay.nn.bias_add(conv, BS)
conv = relay.op.nn.relu(conv)

mod = relay.Function([A, W, BS], conv)
np.random.seed(0)
initializer = relay.testing.init.Xavier()
filter_data = np.zeros(filter_shape).astype(dtype)
bias_data = np.zeros(bias_shape).astype(dtype)
initializer("weight", filter_data)
initializer("bias", bias_data)
params1 = {
"weight": tvm.nd.array(filter_data),
"bias": tvm.nd.array(bias_data),
}

if executor_type == "ge":
static_memory_scope = [
"",
"global.texture",
"global",
"global.texture-weight",
"",
"",
]
build_run_compare(
remote,
mod,
params1,
{"data": input_shape},
{"data": dtype},
target,
static_memory_scope,
)
else:
static_memory_scope = """
VM VirtualDevice[0]: device type 1, id 0 and mem_scope
VM VirtualDevice[1]: device type 4, id 0 and mem_scope
VM VirtualDevice[2]: device type 4, id 0 and mem_scope global.texture
VM VirtualDevice[3]: device type 4, id 0 and mem_scope global
VM VirtualDevice[4]: device type 4, id 0 and mem_scope global.texture-weight
"""
build_run_compare_vm(
remote,
mod,
params1,
{"data": input_shape},
{"data": dtype},
target,
static_memory_scope,
)


if __name__ == "__main__":
tvm.testing.main()
18 changes: 5 additions & 13 deletions tests/python/relay/opencl_texture/utils/adreno_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -161,19 +161,11 @@ def build_run_compare_vm(
tvm_mod_nchwc, target=target, target_host=target_host, params=params1
)

# TODO(echuraev): enable scope checking
## verification that storage_scope has expected textures scopes
# graph_json = json.loads(graph)
# if "storage_scope" in graph_json["attrs"]:
# assert (
# len(static_mem_scopes) == len(graph_json["attrs"]["storage_scope"][1])
# or len(static_mem_scopes) == 0
# )
# else:
# assert len(static_mem_scopes) == 0

# for i in range(0, len(static_mem_scopes)):
# assert static_mem_scopes[i] == graph_json["attrs"]["storage_scope"][1][i]
if len(static_mem_scopes) > 0:
mem_scopes_lines = static_mem_scopes.strip().split("\n")
vm_lines = vmc._get_virtual_devices().strip().split("\n")
for i in range(0, len(mem_scopes_lines)):
assert mem_scopes_lines[i].strip() == vm_lines[i].strip()

if remote is None:
dev = tvm.opencl()
Expand Down