echuraev commented on code in PR #13670:
URL: https://github.com/apache/tvm/pull/13670#discussion_r1059279528
##########
cmake/modules/contrib/CLML.cmake:
##########
@@ -22,7 +22,21 @@ if(USE_CLML)
if(NOT USE_CLML_GRAPH_EXECUTOR)
list(APPEND COMPILER_SRCS ${CLML_RUNTIME_MODULE})
endif()
- message(STATUS "Build with CLML support...")
+ message(STATUS "Build with CLML support : " ${USE_CLML})
+ if (NOT USE_CLML STREQUAL "ON")
+ set(CLML_VERSION_HEADER "${USE_CLML}/CL/cl_qcom_ml_ops.h")
+ if(EXISTS ${CLML_VERSION_HEADER})
+ file(READ ${CLML_VERSION_HEADER} ver)
+ string(REGEX MATCH "CL_QCOM_ML_OPS_H_MAJOR_VERSION ([0-9]*)" _
${ver})
+ set(CLML_VERSION_MAJOR ${CMAKE_MATCH_1})
Review Comment:
Is it possible situation that we build TVM with a new version of CLML but
our target device doesn't support it? Maybe it is better to restrict version of
CLML as it was done for OpenCL (we use OpenCL 1.2)?
##########
src/runtime/contrib/clml/clml_runtime.cc:
##########
@@ -153,13 +153,24 @@ class CLMLRuntime : public JSONRuntimeBase {
ICHECK(result == CL_SUCCESS) << "clQueryMLInterfaceVersionsQCOM:" <<
result;
for (cl_uint i = 0; i < numVersions; ++i) {
+#if CL_QCOM_ML_OPS_H_MAJOR_VERSION == 2
if (majorVersions[i] == 2) {
- LOG(WARNING) << "CLML Version Selected:" << majorVersions[i] << " : "
<< majorVersions[i];
h_ClmlIntf = clGetMLInterfaceV2QCOM(0);
- ICHECK(h_ClmlIntf != NULL) << "clGetMLInterfaceV2QCOM:" << result;
+ LOG(WARNING) << "CLML Target version:" << majorVersions[i];
break;
}
+#endif
+#if CL_QCOM_ML_OPS_H_MAJOR_VERSION == 3
+ if (majorVersions[i] == 3) {
+ h_ClmlIntf = clGetMLInterfaceV3QCOM(0);
+ LOG(WARNING) << "CLML Target version:" << majorVersions[i];
+ break;
+ }
+#endif
}
+ ICHECK(h_ClmlIntf != NULL)
+ << "clGetMLInterfaceVxQCOM:" << result
+ << " Perhaps there is mispatch between CLML SDK version to target
supported version";
Review Comment:
Let's print target supported version
##########
tests/scripts/task_build_adreno_bins.sh:
##########
@@ -29,7 +29,7 @@ cd ${output_directory}
cp ../cmake/config.cmake .
echo set\(USE_MICRO OFF\) >> config.cmake
-echo set\(USE_CLML ON\) >> config.cmake
+echo set\(USE_CLML "${ADRENO_OPENCL}"\) >> config.cmake
Review Comment:
Is it not necessary to do this check (`[[ -z "${ADRENO_OPENCL}" ]] &&
CLML_PATH='ON' || CLML_PATH="${ADRENO_OPENCL}"`) here?
##########
tests/python/contrib/test_clml/infrastructure.py:
##########
@@ -181,16 +199,34 @@ def extract_clml_modules(module):
def verify_codegen(
- module,
+ mod,
known_good_codegen,
+ device,
+ params,
num_clml_modules=1,
tvm_ops=0,
- target="llvm -mtriple=aarch64-linux-gnu",
):
"""Check clml codegen against a known good output."""
- module = build_module(module, target, tvm_ops=tvm_ops,
clml_partitions=num_clml_modules)
- clml_modules = extract_clml_modules(module)
+ if isinstance(mod, tvm.relay.expr.Call):
+ mod = tvm.IRModule.from_expr(mod)
+ with tvm.transform.PassContext(opt_level=3,
disabled_pass=["AlterOpLayout"]):
+ mod = clml.partition_for_clml(mod, params)
+ tvm_op_count = get_cpu_op_count(mod)
+ assert tvm_op_count == tvm_ops, "Got {} TVM operators, expected
{}".format(
Review Comment:
What if the user didn't pass value of `tvm_ops`? Probably `tvm_ops` should
be required parameter?
##########
tests/python/contrib/test_clml/test_ops.py:
##########
@@ -54,11 +62,8 @@ def _get_conv_model(
shape = (shape[0], shape[1], shape[2] + padding[0] * 2, shape[3] +
padding[1] * 2)
is_depthwise = shape[1] == channels == groups
- weight_format = "OIHW" if is_depthwise else "OIHW"
- if weight_format == "IOHW":
- weight_shape = (shape[1] // groups, channels, kernel_h, kernel_w)
- else:
- weight_shape = (channels, shape[1] // groups, kernel_h, kernel_w)
+ weight_format = "OIHW"
Review Comment:
Why did you remove check on the dephtwise convolution?
##########
tests/python/contrib/test_clml/test_ops.py:
##########
@@ -86,31 +91,122 @@ def _get_conv_model(
if has_activation:
out = relay.nn.relu(out)
- print("Out:", out)
-
return out, params
+def _get_conv_expected_codegen(
+ shape,
+ kernel_h,
+ kernel_w,
+ padding,
+ strides,
+ dilation,
+ groups,
+ dtype,
+ channels,
+ has_bias=False,
+ has_activation=False,
+):
+ if len(padding) == 2:
+ padding = (padding[0], padding[1], padding[0], padding[1])
+ output_height = ((shape[2] - kernel_h + padding[0] + padding[2]) /
strides[0]) + 1
+ output_width = ((shape[3] - kernel_w + padding[1] + padding[3]) /
strides[1]) + 1
+ output_shape = (1, channels, int(output_height), int(output_width))
+ out_dtype = dtype
+ is_depthwise = shape[1] == channels == groups
+
+ weight_format = "IOHW" if is_depthwise else "OIHW"
+ if weight_format == "OIHW":
+ weight_shape = (channels, shape[1] // groups, kernel_h, kernel_w)
+ else:
+ weight_shape = (shape[1] // groups, channels, kernel_h, kernel_w)
+ # weight_shape = (channels, shape[1] // groups, kernel_h, kernel_w)
Review Comment:
Redundant comment
##########
tests/python/contrib/test_clml/infrastructure.py:
##########
@@ -155,9 +173,9 @@ def build_and_run(
for _ in range(no_runs):
gen_module.run()
out.append([gen_module.get_output(i) for i in range(outputs)])
- time_f = gen_module.module.time_evaluator("run", device.device.cl(0),
number=1)
- cost = time_f().mean
- print("%g secs/iteration\n" % cost)
+ # time_f = gen_module.module.time_evaluator("run", device.device.cl(0),
number=1)
+ # cost = time_f().mean
+ # print("%g secs/iteration\n" % cost)
Review Comment:
Why did you remove it?
##########
src/relay/backend/contrib/clml/codegen.cc:
##########
@@ -332,6 +332,10 @@ class CLMLJSONSerializer : public
backend::contrib::JSONSerializer {
bias = dense;
dense = dense->args[0].as<CallNode>();
}
+ if (backend::IsOp(dense, "nn.bias_add")) {
Review Comment:
Maybe unify `add` and `nn.bias_add`?
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