!100 Use env variances for dump_ir and dump_code

Merge pull request !100 from lvwenyuan/gpu-env

python/akg/composite/build_module.py

@@ -176,7 +176,8 @@ def build_cuda(outputs, args, sch_name, kernel_name):
     }
     with tvm.target.cuda() as cuda:
         s = scheduler[sch_name](outputs)
-        with tvm.build_config(dump_pass_ir = True):
+        dump_ir = os.getenv('MS_AKG_DUMP_IR') == "on"
+        with tvm.build_config(dump_pass_ir = dump_ir):
             mod = tvm.build(s, args, cuda, name = kernel_name)
             dump_cuda_meta.dump(mod, kernel_name, s, list(args))
             return mod

python/akg/ms/message.py

@@ -82,7 +82,10 @@ def compilewithjson_to_func(json_str):
             if kernel_info['attr']:
                 for ext_arg in kernel_info['attr']:
                     op_attrs.append(ext_arg['value'])
-            mod = utils.op_build(op_func, input_shapes, input_types, op_attrs, kernel_info['op'])
+            dump_ir = os.getenv('MS_AKG_DUMP_IR') == "on"
+            dump_code = os.getenv('MS_AKG_DUMP_CODE') == "on"
+            mod = utils.op_build(op_func, input_shapes, input_types, op_attrs, kernel_info['op'], dump_ir=dump_ir,
+                                 dump_code=dump_code)
             return True
         else:
             op_func = getattr(cce, op_name, None)

python/akg/ms/op_build.py

@@ -31,7 +31,7 @@ from akg.utils import validation_check as vc_util
 
 BINDS = "binds"
 MS_AKG_DUMP_IR = "MS_AKG_DUMP_IR"
-MS_AKG_DUMP_CCE = "MS_AKG_DUMP_CCE"
+MS_AKG_DUMP_CODE = "MS_AKG_DUMP_CODE"
 MS_DAVINCI_KERNEL_PATH = "./kernel_meta/"
 
 

python/akg/ops/nn/maxpool.py

@@ -294,7 +294,7 @@ def maxpool_manual_schedule(shape, kernel, stride, padding, dtype, attrs=None, p
         mod = akg.build(s, [data, res], "cce", name="maxpool_manual_schedule", attrs=attrs, polyhedral=polyhedral)
         source_code = mod.imported_modules[0].get_source()
         kernel_name = "maxpool_ad_manual_schedule"
-        utils.create_cce(kernel_name, './', source_code)
+        utils.create_code(kernel_name, './', source_code)
     return mod
 
 def pad_strategy_check(strategy):

python/akg/ops/nn/maxpool_ad.py

@@ -387,7 +387,7 @@ def maxpool_ad_manual_schedule_all_max(shape, kernel, stride, pad, dtype, polyhe
                         attrs=attrs, polyhedral=polyhedral)
         source_code = mod.imported_modules[0].get_source()
         kernel_name = "maxpool_ad_manual_schedule_all_max"
-        utils.create_cce(kernel_name, './', source_code)
+        utils.create_code(kernel_name, './', source_code)
     return mod
 
 
@@ -489,5 +489,5 @@ def maxpool_ad_manual_schedule_no_overlap_all_max(shape, kernel, stride, pad, dt
                         name="maxpool_ad_manual_schedule_no_overlap_all_max", attrs=attrs, polyhedral=polyhedral)
         source_code = mod.imported_modules[0].get_source()
         kernel_name = "maxpool_ad_manual_schedule_no_overlap_all_max"
-        utils.create_cce(kernel_name, './', source_code)
+        utils.create_code(kernel_name, './', source_code)
     return mod

python/akg/utils/dump_cuda_meta.py

@@ -64,7 +64,19 @@ def save_gpu_params(s, args, kernel_info):
     ptx_code = kernel_info[0]
     file_name = kernel_info[1]
     kernel_name = kernel_info[2]
-    ir = str(akg.tvm.lower(s, args, simple_mode=True))
+
+    
+    dump_ir = os.getenv('MS_AKG_DUMP_IR') == "on"
+    if dump_ir:
+        schedule_path = os.path.realpath(kernel_name)
+        all_passes = os.listdir(schedule_path)
+        for cur_pass in all_passes:
+            if cur_pass.startswith("00_"):
+                with open(schedule_path + '/' + cur_pass, "r") as file:
+                    ir = file.read()
+                break
+    else:
+        ir = str(akg.tvm.lower(s, args, simple_mode=True))
     file_path = os.path.realpath(file_name)
     if os.path.exists(file_path):
         os.remove(file_path)

python/akg/utils/kernel_exec.py

@@ -67,29 +67,42 @@ def func_time_required(func_name):
     return wrapper
 
 
-def create_cce(kernel_name, cce_path=None, code=None):
+def create_code(kernel_name, code_path=None, code=None, code_type="CCE"):
     """
-    Create cce file.
+    Create cce or cuda file.
 
     Args:
-        kernel_name: cce file name.
-        cce_path: cce file path.
-        code: cce code.
+        kernel_name: file name.
+        code_path: file path.
+        code: code.
+        code_type: code type.
     """
-    if cce_path:
-        if len(cce_path) > 4 and cce_path[-4:].lower() == ".cce":
-            real_path = cce_path
+    if code_type == "CCE":
+        postfix = ".cce"
+    elif code_type == "CUDA":
+        postfix = ".cu"
+    else:
+        logging.info("the target code type %s is not supported.", code_type)
+
+    if not code_path:
+        code_path = "./"
+    
+    if code_type == "CCE" and len(code_path) > 4 and code_path[-4:].lower() == postfix:
+        real_path = code_path
+    elif code_type == "CUDA" and len(code_path) > 3 and code_path[-3:].lower() == postfix:
+        real_path = code_path
+    else:
+        if code_path[-1] == r"/":
+            real_path = code_path + kernel_name + postfix
         else:
-            if cce_path[-1] == r"/":
-                real_path = cce_path + kernel_name + ".cce"
-            else:
-                real_path = cce_path + r"/" + kernel_name + ".cce"
-        dir_path = r"/".join(real_path.split(r"/")[:-1])
-        if not os.path.isdir(dir_path):
-            os.makedirs(dir_path)
+            real_path = code_path + r"/" + kernel_name + postfix
+    dir_path = r"/".join(real_path.split(r"/")[:-1])
+    if not os.path.isdir(dir_path):
+        os.makedirs(dir_path)
+    
+    with open(real_path, 'wt') as ss:
+        ss.write(code)
 
-        with open(real_path, 'wt') as ss:
-            ss.write(code)
 
 
 def gen_name_kernel(kernel, dtype, shapes):
@@ -538,7 +551,7 @@ def gen_kernel_name(input_shapes, input_types, op_attrs=None, kernel_name=""):
 
 @func_time_required
 def op_build_test(op_func, input_shapes, input_types, op_attrs=None, kernel_name="",
-                  attrs=None, log_cce=False, dump_ir=True, dump_cce=True,
+                  attrs=None, log_cce=False, dump_ir=True, dump_code=True,
                   polyhedral=True, tuning=False):
     """
     Return module from op_build with given inputs, distinguish tuning mode.
@@ -552,7 +565,7 @@ def op_build_test(op_func, input_shapes, input_types, op_attrs=None, kernel_name
         attrs (dict): tiling parameter.
         log_cce (bool): False by default.
         dump_ir (bool): True by default.
-        dump_cce (bool): False by default.
+        dump_code (bool): False by default.
         polyhedral (bool): True by default.
         tuning (bool): False by default.
 
@@ -565,7 +578,7 @@ def op_build_test(op_func, input_shapes, input_types, op_attrs=None, kernel_name
         kernel_name = gen_kernel_name(input_shapes, input_types, op_attrs, kernel_name)
     logging.debug('kernel_name---------- %s', str(kernel_name))
     mod = op_build(op_func, input_shapes, input_types, op_attrs, kernel_name,
-                   attrs, log_cce, dump_ir, dump_cce,
+                   attrs, log_cce, dump_ir, dump_code,
                    polyhedral, tuning)
     return mod
 
@@ -593,7 +606,7 @@ def recursive_copy(obj):
 
 
 def op_build(op_func, input_shapes, input_types, op_attrs=None, kernel_name="",
-             attrs=None, log_cce=False, dump_ir=True, dump_cce=True,
+             attrs=None, log_cce=False, dump_ir=True, dump_code=True,
              polyhedral=True, tuning=False):
     """
     Return module built from op_func with given inputs.
@@ -607,7 +620,7 @@ def op_build(op_func, input_shapes, input_types, op_attrs=None, kernel_name="",
         attrs (dict): tiling parameter.
         log_cce (bool): False by default.
         dump_ir (bool): True by default.
-        dump_cce (bool): False by default.
+        dump_code (bool): False by default.
         polyhedral (bool): True by default.
         tuning (bool): False by default.
 
@@ -730,9 +743,13 @@ def op_build(op_func, input_shapes, input_types, op_attrs=None, kernel_name="",
         kernel_name = kernel_name if kernel_name != "" else sch_tmpl['op_name']
         with akg.tvm.target.cuda() as target:
             s = sch_tmpl['schedule'](sch_tmpl['output'])
-            with akg.build_config(dump_pass_ir=True):
-                mod = akg.build(s, op_var, "cuda", shape_var, name=kernel_name, attrs=attrs, polyhedral=polyhedral, binds=binds)
+            with akg.tvm.build_config(dump_pass_ir=dump_ir):
+                mod = akg.build(s, op_var, "cuda", shape_var, name=kernel_name, attrs=attrs,
+                                polyhedral=polyhedral, binds=binds)
                 dump_cuda_meta.dump(mod, kernel_name, s, op_var)
+                if dump_code:
+                    source_code = mod.imported_modules[0].get_source()
+                    create_code(kernel_name, "./", source_code, "CUDA")
                 return mod
 
     if isinstance(output, (list, tuple)):
@@ -781,9 +798,9 @@ def op_build(op_func, input_shapes, input_types, op_attrs=None, kernel_name="",
     if log_cce:
         logging.debug("#################cce code####################")
         logging.debug(source_code)
-    if dump_cce:
-        cce_path = "./"
-        create_cce(kernel_name, cce_path, source_code)
+    if dump_code:
+        code_path = "./"
+        create_code(kernel_name, code_path, source_code)
 
     return mod
 

src/codegen/build_module.cc

@@ -1110,8 +1110,8 @@ air::runtime::Module BuildToModule(const NodeRef &ref, const std::string &target
     mhost.Import(mdev);
   }
 
-  const char *akg_dump_cce = getenv("MS_AKG_DUMP_CCE");
-  if (akg_dump_cce != nullptr) {
+  const char *akg_dump_code = getenv("MS_AKG_DUMP_CODE");
+  if (akg_dump_code != nullptr) {
     auto mod0 = mhost->imports()[0];
     CHECK(mod0.defined());
 

tests/common/test_op/add_a_conv.py

@@ -207,7 +207,7 @@ def add_a_conv(fmap_shape, filter_shape, pad_, stride_, dilation_,
             mod = akg.build(s, [a_value, b_value, conv], "cce", name=kernel_name, attrs=attrs, polyhedral=True)
     source_code = mod.imported_modules[0].get_source()
     cce_path = '.'
-    utils.create_cce(kernel_name, cce_path, source_code)
+    utils.create_code(kernel_name, cce_path, source_code)
 
     return mod
 

tests/common/test_op/add_b_conv.py

@@ -201,7 +201,7 @@ def add_b_conv(fmap_shape, filter_shape, pad_, stride_, dilation_,
             mod = akg.build(s, [a_value, b_value, conv], "cce", name=kernel_name, attrs=attrs, polyhedral=True)
     source_code = mod.imported_modules[0].get_source()
     cce_path = '.'
-    utils.create_cce(kernel_name, cce_path, source_code)
+    utils.create_code(kernel_name, cce_path, source_code)
 
     return mod
 

tests/common/test_op/col2im_compute.py

@@ -64,5 +64,5 @@ def col2im_manual_schedule(shape, kernel, stride, pad, dtype, output_H_W, polyhe
         mod = akg.build(s, [data, res], "cce", name="col2im_manual_schedule", attrs=attrs, polyhedral=polyhedral)
         source_code = mod.imported_modules[0].get_source()
         kernel_name = "col2im_manual_schedule"
-        utils.create_cce(kernel_name, "./", source_code)
+        utils.create_code(kernel_name, "./", source_code)
     return mod

tests/common/test_op/gather.py

@@ -115,6 +115,6 @@ def gather(params_shape, indices_shape, params_dtype, indices_dtype, axis, kerne
         mod = akg.build(s, [xx, yy, res], "cce", name=kernel_name, attrs=attrs)
 
     source_code = mod.imported_modules[0].get_source()
-    utils.create_cce(kernel_name, cce_path, source_code)
+    utils.create_code(kernel_name, cce_path, source_code)
 
     return mod

tests/common/test_op/im2col_compute.py

@@ -109,5 +109,5 @@ def im2col_manual_schedule(shape, kernel, stride, pad, dtype, polyhedral=True, a
                        attrs=attrs, polyhedral=polyhedral)
         source_code = mod.imported_modules[0].get_source()
         kernel_name = "im2col_manual_schedule"
-        utils.create_cce(kernel_name, './', source_code)
+        utils.create_code(kernel_name, './', source_code)
     return mod

tests/common/test_op/reduce_max_ad.py

@@ -200,5 +200,5 @@ def reduce_max_ad_optimized_manual_schedule(input_shape, dtype, axis, keepdims,
                         name="reduce_max_ad_manual_schedule", attrs=attrs, polyhedral=polyhedral)
         source_code = mod.imported_modules[0].get_source()
         kernel_name = "reduce_max_ad_manual_schedule"
-        utils.create_cce(kernel_name, './', source_code)
+        utils.create_code(kernel_name, './', source_code)
     return mod

tests/common/test_op/reduce_min_ad.py

@@ -159,5 +159,5 @@ def reduce_min_ad_optimized_manual_schedule(input_shape, dtype, axis, keepdims,
                         attrs=attrs, polyhedral=polyhedral)
         source_code = mod.imported_modules[0].get_source()
         kernel_name = "reduce_min_ad_manual_schedule"
-        utils.create_cce(kernel_name, './', source_code)
+        utils.create_code(kernel_name, './', source_code)
     return mod

tests/common/test_op/vector_matmul.py

@@ -105,5 +105,5 @@ def vector_matmul(data_m, data_n, data_k, trans_a, trans_b, dtype, kernel_name,
     with akg.build_config(add_lower_pass=cce.debug_mode(0), dump_pass_ir=True):
         mod = akg.build(forward_s, op_vars, "cce", name=kernel_name, attrs=attrs, polyhedral=True)
         source_code = mod.imported_modules[0].get_source()
-        utils.create_cce(kernel_name, "./", source_code)
+        utils.create_code(kernel_name, "./", source_code)
         return mod, output_shape

tests/common/test_run/IOU_for_train_run.py

@@ -81,7 +81,7 @@ def iou_for_train_run(shape_tensor,
         output = utils.mod_launch(mod, (anchor, ground_truth, output), expect=expect)
 
         source_code = mod.imported_modules[0].get_source()
-        utils.create_cce(kernel_name, "./", source_code)
+        utils.create_code(kernel_name, "./", source_code)
         return input, output, expect, compare_tensor(output, expect, rtol=5e-03, equal_nan=True)
 
 

tests/common/test_run/avgpool_ad_run.py

@@ -41,7 +41,7 @@ def avgpool_ad_run(shape, kernel, stride, pad, dtype, polyhedral=False, attrs=No
         input = random_gaussian(shape, miu=1, sigma=0.1).astype(support_list[dtype])
         y = avgpool_run.benchmark(input, kernel, stride, pad)
         mod = utils.op_build_test(avgpool, [y.shape, shape], [dtype, dtype], op_attrs=[kernel, stride, pad],
-                                  kernel_name=kernel_name, attrs=attrs, log_cce=True, dump_cce=True, tuning=t)
+                                  kernel_name=kernel_name, attrs=attrs, log_cce=True, dump_code=True, tuning=t)
         if t:
             expect, head, output = gen_data(dtype, input, kernel, pad, stride, support_list, y)
             return mod, expect, (head, input, output)
@@ -51,7 +51,7 @@ def avgpool_ad_run(shape, kernel, stride, pad, dtype, polyhedral=False, attrs=No
         input = random_gaussian(shape, miu=1, sigma=0.1).astype(support_list[dtype])
         y = avgpool_run.benchmark(input, kernel, stride, pad)
         mod = utils.op_build_test(avgpool, [y.shape, shape], [dtype, dtype], op_attrs=[kernel, stride, pad],
-                                  kernel_name=kernel_name, attrs=attrs, log_cce=True, dump_cce=True)
+                                  kernel_name=kernel_name, attrs=attrs, log_cce=True, dump_code=True)
         expect, head, output = gen_data(dtype, input, kernel, pad, stride, support_list, y)
         output = utils.mod_launch(mod, [head, input, output], expect=expect)
 

tests/common/test_run/bounding_box_encode_run.py

@@ -197,7 +197,7 @@ def bounding_box_encode_run(anchor_box_shape, groundtruth_box_shape, anchor_samp
         mod = utils.op_build_test(bounding_box_encode.bouding_box_encode,
                                   [anchor_box_shape, groundtruth_box_shape, anchor_samples_shape],
                                   [dtype, dtype, "int32"],
-                                  op_attrs, kernel_name=kernel_name, attrs=attrs, dump_cce=True, tuning=t)
+                                  op_attrs, kernel_name=kernel_name, attrs=attrs, dump_code=True, tuning=t)
         if t:
             anchor_box_data, anchor_samples_data, expect, groundtruth_box_data, output_data = gen_data(anchor_box_shape,
                                                                                                        anchor_samples_shape,
@@ -211,7 +211,7 @@ def bounding_box_encode_run(anchor_box_shape, groundtruth_box_shape, anchor_samp
         mod = utils.op_build_test(bounding_box_encode.bouding_box_encode,
                                   [anchor_box_shape, groundtruth_box_shape, anchor_samples_shape],
                                   [dtype, dtype, "int32"],
-                                  op_attrs, kernel_name=kernel_name, attrs=attrs, dump_cce=True)
+                                  op_attrs, kernel_name=kernel_name, attrs=attrs, dump_code=True)
         anchor_box_data, anchor_samples_data, expect, groundtruth_box_data, output_data = gen_data(anchor_box_shape,
                                                                                                    anchor_samples_shape,
                                                                                                    dtype, epsilon,

tests/common/test_run/conv_filter_ad_run.py

@@ -192,7 +192,7 @@ def conv_filter_ad_run(fmap_shape, filter_shape, pad_, stride_, dilation_,  attr
         return np_input, out_data, expect, True
 
     mod = utils.op_build_test(conv_filter_ad.conv_filter_ad, [dw_input_shapes], [conv_dtype],
-                              op_attrs=[fmap_shape, filter_shape, pad_, stride_, dilation_], kernel_name='conv_filter_ad', attrs=attrs, dump_cce = True)
+                              op_attrs=[fmap_shape, filter_shape, pad_, stride_, dilation_], kernel_name='conv_filter_ad', attrs=attrs, dump_code = True)
     args = (dy_data, dx_data, out_data)
     out_data = utils.mod_launch(mod, args, expect=expect)
     rtol, atol = get_rtol_atol("conv_filter_ad", conv_dtype)

tests/common/test_run/conv_run_mansch.py

@@ -34,7 +34,7 @@ def conv_run_mansch(FMap_shape, Filter_shape, Pad, Stride, Dilation=None, use_bi
                       use_bias=use_bias, fp32_mad=fp32_mad, kernel_name="conv_mansch")
 
     source_code = mod.imported_modules[0].get_source()
-    utils.create_cce("conv_mansch", ".", source_code)
+    utils.create_code("conv_mansch", ".", source_code)
     A, B, bias_data, expect = gen_data(FMap_shape, Filter_shape, Pad, Stride, Dilation, use_bias)
 
     expect = expect.reshape((expect.shape[0], expect.shape[1], expect.shape[2]*expect.shape[3],expect.shape[4]))  # output on conv2d is in 4d format

tests/common/test_run/distr_bernoulli_logprob_ad_run.py

@@ -26,7 +26,7 @@ def logprob_ad_run(shape, dtype, kernel_name="", attrs=None):
 
     mod = utils.op_build_test(distr_bernoulli_logprob_ad.bernoulli_logprob_ad, [head.shape, x.shape, probs.shape],
                                          [dtype, dtype, dtype], kernel_name=kernel_name,
-                                         op_attrs=None, attrs=None, log_cce=True, dump_cce=True, polyhedral=True)
+                                         op_attrs=None, attrs=None, log_cce=True, dump_code=True, polyhedral=True)
     outputs = utils.mod_launch(mod, [head, x, probs, *outputs], outputs=tuple(range(-len(outputs), 0)), expect=expects)
     outputs = list(outputs)
 

tests/common/test_run/distr_bernoulli_logprob_run.py

@@ -29,7 +29,7 @@ def log_prob_run(shape, dtype, kernelname="", attrs = None):
 
   mod = utils.op_build_test(log_prob_op, [x.shape, probs.shape],
                             [dtype, dtype], kernel_name=kernelname,
-                            op_attrs=[], attrs=None, log_cce=True, dump_cce=True, polyhedral=True)
+                            op_attrs=[], attrs=None, log_cce=True, dump_code=True, polyhedral=True)
   output = utils.mod_launch(mod, [x, probs, output], expect=expect)
   return (x, probs), output, expect, compare_tensor(output, expect, rtol=1e-03, atol=1e-03, equal_nan=True)
 

tests/common/test_run/distr_normal_diag_KLdiv_ad_run.py

@@ -25,7 +25,7 @@ def KLdiv_ad_run(shape, dtype, kernel_name="", attrs=None):
 
     mod = utils.op_build_test(distr_normal_diag_KLdiv_ad.normal_diag_KLdiv_ad, [head.shape, mean.shape, scale.shape],
                                          [dtype, dtype, dtype], kernel_name=kernel_name,
-                                         op_attrs=None, attrs=None, log_cce=True, dump_cce=True, polyhedral=True)
+                                         op_attrs=None, attrs=None, log_cce=True, dump_code=True, polyhedral=True)
     outputs = utils.mod_launch(mod, [head, mean, scale, *outputs], outputs=tuple(range(-len(outputs), 0)), expect=expects)
     outputs = list(outputs)
 

tests/common/test_run/distr_normal_diag_KLdiv_run.py

@@ -27,7 +27,7 @@ def KLdiv_run(shape, dtype, kernelname="", attrs = None):
 
   mod = utils.op_build_test(KLdiv_op, [mean.shape, scale.shape],
                             [dtype, dtype], kernel_name=kernelname,
-                            op_attrs=[], attrs=None, log_cce=True, dump_cce=True, polyhedral=True)
+                            op_attrs=[], attrs=None, log_cce=True, dump_code=True, polyhedral=True)
   output = utils.mod_launch(mod, [mean, scale, output], expect = expect)
   return (mean, scale), output, expect, compare_tensor(output, expect, rtol=5e-03, equal_nan=True)
 

tests/common/test_run/distr_normal_diag_logprob_ad_run.py

@@ -30,7 +30,7 @@ def logprob_ad_run(shape, dtype, kernel_name="", attrs=None):
         op_attrs=None,
         attrs=None,
         log_cce=True,
-        dump_cce=True,
+        dump_code=True,
         polyhedral=True,
     )
     outputs = utils.mod_launch(

tests/common/test_run/distr_normal_diag_logprob_run.py

@@ -28,7 +28,7 @@ def logprob_run(shape, dtype, kernelname="", attrs = None):
 
   mod = utils.op_build_test(logprob_op, [x.shape, mean.shape, scale.shape],
                             [dtype, dtype, dtype], kernel_name=kernelname,
-                            op_attrs=[], attrs=None, log_cce=True, dump_cce=True, polyhedral=True)
+                            op_attrs=[], attrs=None, log_cce=True, dump_code=True, polyhedral=True)
   output = utils.mod_launch(mod, [x, mean, scale, output], expect = expect)
   return (x, mean, scale), output, expect, compare_tensor(output, expect, rtol=5e-03, equal_nan=True)
 

tests/common/test_run/distr_normal_diag_sample_ad_run.py

@@ -24,7 +24,7 @@ def sample_ad_run(shape, dtype, kernel_name="", attrs=None):
 
     mod = utils.op_build_test(distr_normal_diag_sample_ad.normal_diag_sample_ad, [head.shape, mean.shape, scale.shape, eps.shape],
                                          [dtype, dtype, dtype, dtype], kernel_name=kernel_name,
-                                         op_attrs=None, attrs=None, log_cce=True, dump_cce=True, polyhedral=True)
+                                         op_attrs=None, attrs=None, log_cce=True, dump_code=True, polyhedral=True)
     outputs = utils.mod_launch(mod, [head, mean, scale, eps, *outputs], outputs=tuple(range(-len(outputs), 0)), expect=expects)
     outputs = list(outputs)
     

tests/common/test_run/distr_normal_diag_sample_run.py

@@ -26,7 +26,7 @@ def sample_run(shape, dtype, kernel_name="", attrs=None):
 
     mod = utils.op_build_test(sample_op, [mean.shape, scale.shape, eps.shape],
                                          [dtype, dtype, dtype], kernel_name=kernel_name,
-                                         op_attrs=None, attrs=None, log_cce=True, dump_cce=True, polyhedral=True)
+                                         op_attrs=None, attrs=None, log_cce=True, dump_code=True, polyhedral=True)
     output = utils.mod_launch(mod, [mean, scale, eps, output], expect=expect)
 
     return (mean, scale, eps), output, expect, compare_tensor(output, expect, rtol=5e-03, atol=0.1, equal_nan=True)

tests/common/test_run/distr_normal_prob_regr_train_run.py

@@ -25,7 +25,7 @@ def prob_regression_run(shape, dtype, kernel_name, attrs):
 
     mod = utils.op_build_test(distr_normal_prob_regr_train.prob_regression_train, [x.shape, w.shape, y.shape],
                                          [dtype, dtype, dtype], kernel_name=kernel_name,
-                                         op_attrs=[], attrs=None, log_cce=True, dump_cce=True, polyhedral=True)
+                                         op_attrs=[], attrs=None, log_cce=True, dump_code=True, polyhedral=True)
 
     output = utils.mod_launch(mod, [x, w, y, output], expect=expect)
     return (x, w, y), output, expect, compare_tensor(output, expect, rtol=5e-03, equal_nan=True)

tests/common/test_run/dropout_run.py

@@ -83,7 +83,7 @@ def dropout_execute(shape_tensor, keep_prob, dtype, kernel_name, attrs=None):
         output = utils.mod_launch(mod, (input, mask, output), expect=expect)
 
         source_code = mod.imported_modules[0].get_source()
-        utils.create_cce(kernel_name, "./", source_code)
+        utils.create_code(kernel_name, "./", source_code)
 
         rtol, atol = get_rtol_atol("dropout", dtype)
         return (input, mask), output, expect, compare_tensor(output, expect, rtol=rtol, atol=atol, equal_nan=True)

tests/common/test_run/kldiv_loss_grad_run.py

@@ -28,7 +28,7 @@ def kldiv_loss_grad_run(shape, dtype, kernel_name="kldiv_loss_grad", attrs=None)
         t = attrs.get("tuning", False)
         kernel_name = attrs.get("kernel_name", False)
         mod = utils.op_build_test(kldiv_loss_grad.kldiv_loss_grad, [shape, shape, shape], [dtype, dtype, dtype],
-                                  kernel_name=kernel_name, attrs=attrs, dump_cce=True, tuning=t)
+                                  kernel_name=kernel_name, attrs=attrs, dump_code=True, tuning=t)
         if t:
             cur_deriv, output, pre_deriv, prediction, target = gen_data(attrs, dtype, shape)
             return mod, cur_deriv, (pre_deriv, prediction, target, output)
@@ -36,7 +36,7 @@ def kldiv_loss_grad_run(shape, dtype, kernel_name="kldiv_loss_grad", attrs=None)
             return mod
     else:
         mod = utils.op_build_test(kldiv_loss_grad.kldiv_loss_grad, [shape, shape, shape], [dtype, dtype, dtype],
-                                  kernel_name=kernel_name, attrs=attrs, dump_cce=True)
+                                  kernel_name=kernel_name, attrs=attrs, dump_code=True)
         cur_deriv, output, pre_deriv, prediction, target = gen_data(attrs, dtype, shape)
         output = utils.mod_launch(mod, (pre_deriv, prediction, target, output), expect=cur_deriv)
         return (pre_deriv, prediction, target), output, cur_deriv, compare_tensor(output, cur_deriv, rtol=0.005,

tests/common/test_run/l1_loss_grad_run.py

@@ -28,7 +28,7 @@ def l1_loss_grad_run(shape, dtype, kernel_name="l1_loss_grad", attrs=None):
         t = attrs.get("tuning", False)
         kernel_name = attrs.get("kernel_name", False)
         mod = utils.op_build_test(l1_loss_grad.l1_loss_grad, [shape, shape, shape], [dtype, dtype, dtype],
-                                  kernel_name=kernel_name, attrs=attrs, dump_cce=True, tuning=t)
+                                  kernel_name=kernel_name, attrs=attrs, dump_code=True, tuning=t)
         if t:
             dloss, expect, output, prediction, target = gen_data(dtype, shape)
             return mod, expect, (dloss, prediction, target, output)
@@ -36,7 +36,7 @@ def l1_loss_grad_run(shape, dtype, kernel_name="l1_loss_grad", attrs=None):
             return mod
     else:
         mod = utils.op_build_test(l1_loss_grad.l1_loss_grad, [shape, shape, shape], [dtype, dtype, dtype],
-                                  kernel_name=kernel_name, attrs=attrs, dump_cce=True)
+                                  kernel_name=kernel_name, attrs=attrs, dump_code=True)
         dloss, expect, output, prediction, target = gen_data(dtype, shape)
         output = utils.mod_launch(mod, (dloss, prediction, target, output), expect=expect)
         return (dloss, prediction, target), output, expect, compare_tensor(output, expect, rtol=0.001, atol=0.001)

tests/common/test_run/matmul_run_mansch.py

@@ -45,7 +45,7 @@ def matmul_run_mansch(MatrixShape, l1_tiling, l0_tiling, kernel_name, attrs=None
     # launch the kernel
     mod = matmul_mansch.gemm_dsl(MatrixShape, l1_tiling, l0_tiling, kernel_name)
     source_code = mod.imported_modules[0].get_source()
-    utils.create_cce(kernel_name, ".", source_code)
+    utils.create_code(kernel_name, ".", source_code)
     res = utils.mod_launch(mod, [A, B, out_data])
 
     # transform numpy data to compute benchMark

tests/common/test_run/maxpool_ad_run.py

@@ -44,14 +44,14 @@ def maxpool_ad_run(shape, kernel, stride, pad, dtype, optimized, polyhedral=Fals
             else:
                 mod = utils.op_build_test(maxpool_ad_no_custom_diff_poly_all_max, [head.shape, shape],
                                 [dtype, dtype], kernel_name="maxpool_ad_no_custom_diff_poly_all_max",
-                                op_attrs=[kernel, stride, pad], attrs=attrs, log_cce=False, dump_cce=True, polyhedral=polyhedral)
+                                op_attrs=[kernel, stride, pad], attrs=attrs, log_cce=False, dump_code=True, polyhedral=polyhedral)
                 output = utils.mod_launch(mod, [head, input, output], expect=expect)
     else:
         if optimized:
             if first_max:
                 mod = utils.op_build_test(maxpool_ad, [head.shape, shape, forward.shape, mask.shape],
                                         [dtype, dtype, dtype, dtype], kernel_name="maxpool_ad_first_max",
-                                        op_attrs=[kernel, stride, pad], attrs=attrs, log_cce=False, dump_cce=True, polyhedral=polyhedral)
+                                        op_attrs=[kernel, stride, pad], attrs=attrs, log_cce=False, dump_code=True, polyhedral=polyhedral)
                 output = utils.mod_launch(mod, [head, input, forward, mask, output], expect=expect)
             else:
                 mod = maxpool_ad_manual_schedule_all_max(shape, kernel, stride, pad, dtype, attrs=attrs, polyhedral=polyhedral)
@@ -62,7 +62,7 @@ def maxpool_ad_run(shape, kernel, stride, pad, dtype, optimized, polyhedral=Fals
             else:
                 mod = utils.op_build_test(maxpool_ad_no_custom_diff_manual_schedule_all_max, [head.shape, shape],
                                 [dtype, dtype], kernel_name="maxpool_ad_no_custom_diff_manual_schedule_all_max",
-                                op_attrs=[kernel, stride, pad], attrs=attrs, log_cce=False, dump_cce=True, polyhedral=polyhedral)
+                                op_attrs=[kernel, stride, pad], attrs=attrs, log_cce=False, dump_code=True, polyhedral=polyhedral)
                 output = utils.mod_launch(mod, [head, input, output], expect=expect)
     
     if 'tuning' in attrs.keys():

tests/common/test_run/maxpool_grad_run.py

@@ -100,7 +100,7 @@ def maxpool_grad_run(shape, kernel, stride, pad, dtype, attrs):
         mod = utils.op_build_test(maxpool_grad.maxpool_grad,
                                   [shape, y_shape, y_shape], [dtype, dtype, dtype],
                                   op_attrs=[kernel, stride, pad],
-                                  kernel_name=kernel_name, attrs=attrs, dump_cce=True, tuning=t)
+                                  kernel_name=kernel_name, attrs=attrs, dump_code=True, tuning=t)
         if t:
             dy, expect, output, x, y = \
                 gen_data(dtype, kernel, pad, shape, stride, y_shape)
@@ -111,7 +111,7 @@ def maxpool_grad_run(shape, kernel, stride, pad, dtype, attrs):
         mod = utils.op_build_test(maxpool_grad.maxpool_grad,
                                   [shape, y_shape, y_shape], [dtype, dtype, dtype],
                                   op_attrs=[kernel, stride, pad],
-                                  kernel_name='maxpool_grad', attrs=attrs, dump_cce=True)
+                                  kernel_name='maxpool_grad', attrs=attrs, dump_code=True)
         dy, expect, output, x, y = \
             gen_data(dtype, kernel, pad, shape, stride, y_shape)
         output = utils.mod_launch(mod, (x, y, dy, output), expect=expect)

tests/common/test_run/maxpool_grad_with_argmax_run.py

@@ -31,7 +31,7 @@ def maxpool_grad_with_argmax_run(shape, kernel, stride, pad, dtype, polyhedral=F
             mod = utils.op_build_test(maxpool_grad_with_argmax, [head.shape, mask.shape],
                                       [dtype, dtype], kernel_name="maxpool_grad_with_argmax",
                                       op_attrs=[shape, kernel, stride, pad], attrs=attrs,
-                                      log_cce=False, dump_cce=True, polyhedral=polyhedral)
+                                      log_cce=False, dump_code=True, polyhedral=polyhedral)
         if t:
             return mod, expect, (head, mask, output)
         else:
@@ -43,7 +43,7 @@ def maxpool_grad_with_argmax_run(shape, kernel, stride, pad, dtype, polyhedral=F
             mod = utils.op_build_test(maxpool_grad_with_argmax, [head.shape, mask.shape],
                                       [dtype, dtype], kernel_name="maxpool_grad_with_argmax",
                                       op_attrs=[shape, kernel, stride, pad], attrs=attrs,
-                                      log_cce=False, dump_cce=True, polyhedral=polyhedral)
+                                      log_cce=False, dump_code=True, polyhedral=polyhedral)
             output = utils.mod_launch(mod, [head, mask, output], expect=expect)
 
         rtol, atol = get_rtol_atol("maxpool_grad_with_argmax", dtype)

tests/common/test_run/nms_run.py

@@ -94,6 +94,6 @@ def nms_run(shape_tensor, thres, dtype, kernel_name, attrs):
         output = utils.mod_launch(mod, (anchor, output), expect=expect)
         output = np.frombuffer(output.tobytes(), np.uint16).reshape(out_shape)
         source_code = mod.imported_modules[0].get_source()
-        utils.create_cce(kernel_name, "./", source_code)
+        utils.create_code(kernel_name, "./", source_code)
         expect = np.frombuffer(expect.tobytes(), np.uint16).reshape(out_shape)
         return anchor, output, expect, np.all(output == expect)

tests/common/test_run/roipool_run.py

@@ -32,7 +32,7 @@ def roipool_run(shape, roibox, pooled_shape, dtype, attrs, cce_path="./"):
     expect = roipool_expect(input1, shape, roibox, pooled_shape)
 
     # source_code = mod.imported_modules[0].get_source()
-    # utils.create_cce(kernel_name, cce_path, source_code)
+    # utils.create_code(kernel_name, cce_path, source_code)
 
     output = np.full(output_shape, np.nan, dtype)
     output = utils.mod_launch(mod, (input1, output), expect=expect)

tests/common/test_run/smooth_l1_loss_grad_run.py

@@ -37,7 +37,7 @@ def smooth_l1_loss_grad_run(shape, dtype, attrs=None, kernel_name="smooth_l1_los
         kernel_name = attrs.get("kernel_name", False)
         mod = utils.op_build_test(smooth_l1_loss_grad.smooth_l1_loss_grad, [sample_shape, shape, shape, sample_shape],
                                   [dtype, dtype, dtype, anchor_samples_dtype], op_attrs=[sigma, anchor_sample_correct],
-                                  attrs=attrs, kernel_name=kernel_name, dump_cce=True, tuning=t)
+                                  attrs=attrs, kernel_name=kernel_name, dump_code=True, tuning=t)
         if t:
             anchor_samples, dloss, expect, output, prediction, prediction_, target, target_ = gen_data(
                 anchor_sample_correct, anchor_samples_dtype, dtype, sample_shape, shape, sigma)
@@ -50,7 +50,7 @@ def smooth_l1_loss_grad_run(shape, dtype, attrs=None, kernel_name="smooth_l1_los
         mod = utils.op_build_test(smooth_l1_loss_grad.smooth_l1_loss_grad,
                                   [sample_shape, shape, shape, sample_shape],
                                   [dtype, dtype, dtype, anchor_samples_dtype], op_attrs=[sigma, anchor_sample_correct],
-                                  attrs=attrs, kernel_name=kernel_name, dump_cce=True)
+                                  attrs=attrs, kernel_name=kernel_name, dump_code=True)
         output = utils.mod_launch(mod, (dloss, prediction, target, anchor_samples, output), expect=expect)
         return (dloss, prediction, target, anchor_samples), output, expect, compare_tensor(output, expect, atol=5e-3,
                                                                                            rtol=5e-3)

tests/common/test_run/square_difference_run.py

@@ -35,7 +35,7 @@ def square_difference_run(shape1, shape2, dtype, kernel_name, attrs, cce_path=".
                                   input_types=[dtype, dtype], kernel_name=kernel_name, attrs=attrs)
         expect, input1, input2, output = gen_data(dtype, shape1, shape2)
         source_code = mod.imported_modules[0].get_source()
-        utils.create_cce(kernel_name, cce_path, source_code)
+        utils.create_code(kernel_name, cce_path, source_code)
         output = utils.mod_launch(mod, (input1, input2, output), expect=expect)
         return (input1, input2), output, expect, compare_tensor(output, expect, rtol=5e-03, equal_nan=True)
 

tests/common/test_run/strided_slice_grad_run.py

@@ -81,7 +81,7 @@ def gen_data(begin, begin_mask, dtype, ellipsis_mask, end, end_mask, grad_shape,
     # source_code = mod.imported_modules[0].get_source()
     # print(source_code)
     # kernel_name = "cce_strided_slice_grad_fp16"
-    # utils.create_cce(kernel_name, './', source_code)
+    # utils.create_code(kernel_name, './', source_code)
     out_shape = input_shape
     output = np.full(out_shape, 0, dtype)
     return expect, grad, output

tests/common/test_run/truncatemod_run.py

@@ -26,7 +26,7 @@ def truncatemod_run(shape1, shape2, dtype, attrs):
         t = attrs.get("tuning", False)
         kernel_name = attrs.get("kernel_name", False)
         mod = utils.op_build_test(truncatemod.truncatemod, [shape1, shape2], [dtype, dtype], kernel_name=kernel_name,
-                                  attrs=attrs, dump_cce=True, tuning=t)
+                                  attrs=attrs, dump_code=True, tuning=t)
         if t:
             expect, input1, input2, output = gen_data(dtype, shape1, shape2)
             return mod, expect, (input1, input2, output)
@@ -35,7 +35,7 @@ def truncatemod_run(shape1, shape2, dtype, attrs):
     else:
         expect, input1, input2, output = gen_data(dtype, shape1, shape2)
         mod = utils.op_build_test(truncatemod.truncatemod, [shape1, shape2], [dtype, dtype], kernel_name="truncatemod",
-                                  attrs=attrs, dump_cce=True)
+                                  attrs=attrs, dump_code=True)
         output = utils.mod_launch(mod, (input1, input2, output), expect=expect)
         rtol, atol = get_rtol_atol("truncatemod", dtype)
         res = compare_tensor(output, expect, rtol=rtol, atol=atol, equal_nan=True)

tests/common/test_run/vector_matmul_run.py

@@ -145,7 +145,7 @@ def vector_matmul_run(case_index, m, n, k, trans_a, trans_b, read_data, dump_dat
     # k = (k+15)//16*16
 
     mod, out_shape = vector_matmul.vector_matmul(m, n, k, trans_a, trans_b, dtype, kernel_name, attrs)
-    utils.create_cce(kernel_name, "./", mod.imported_modules[0].get_source())
+    utils.create_code(kernel_name, "./", mod.imported_modules[0].get_source())
 
     # Generate data
     m_a, m_b, bench_mark = vector_matmul_data(case_index, m, n, k, trans_a, trans_b, read_data, dump_data, dtype)

tests/common/test_run/winograd_ad_run.py

@@ -35,7 +35,7 @@ def winograd_ad_run(filter_shape, tile, dtype, attrs):
         t = attrs.get("tuning", False)
         kernel_name = attrs.get("kernel_name", False)
         mod = utils.op_build_test(winograd_ad, [head_np.shape, filter_shape], [dtype, dtype], kernel_name=kernel_name,
-                                  attrs=attrs, log_cce=True, dump_cce=True, tuning=t)
+                                  attrs=attrs, log_cce=True, dump_code=True, tuning=t)
         if t:
             expect, input_np, output = gen_data(filter_shape, RANGEFILL, dtype)
             return mod, expect, (head_np, input_np, output)
@@ -45,7 +45,7 @@ def winograd_ad_run(filter_shape, tile, dtype, attrs):
         # scenario 1:
         expect, input_np, output = gen_data(filter_shape, RANGEFILL, dtype)
         mod = utils.op_build_test(winograd_ad, [head_np.shape, filter_shape], [dtype, dtype], kernel_name="winograd_ad",
-                                  attrs=attrs, log_cce=True, dump_cce=True)
+                                  attrs=attrs, log_cce=True, dump_code=True)
         output = utils.mod_launch(mod, [head_np, input_np, output], expect=expect)
         if not compare_tensor(output, expect, atol=0.1):
             return [head_np, input_np], output, expect, compare_tensor(output, expect, rtol=5e-03, atol=5e-03,

tests/operators/cube/quant_conv.py

@@ -403,7 +403,7 @@ def test_CCE_Conv(fmap_shape, filter_shape, pad_, stride_,
                 mod = akg.build(s, [A, B, ScaleQ, OffsetQ, out], "cce", name=kernel_name, attrs=attrs, attrs={"dim": info}, polyhedral=True)
     source_code = mod.imported_modules[0].get_source()
     # print(source_code)
-    # utils.create_cce(kernel_name, cce_path, source_code)
+    # utils.create_code(kernel_name, cce_path, source_code)
     if run_cce:
         run_conv(mod, fmap_shape, filter_shape, pad_[0], stride_[0], use_bias)