Improve CONV2D coverage for conformance
Enable testing of broadcastable bias channel.
Change convolution arguments function to calculate workable
convolutions by adjusting PAD size as needed for FP and errors.
Add --conv-kernel build_tests option to allow different sized
convolution kernels
Improve coverage with batch size, specific shapes and kernels
of 7x7 and 9x9
Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: I59bb737f398f4363cc606b24fea7812b2059bc01
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index 28b3d28..71d7fcc 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -3172,64 +3172,54 @@
return False
def createDynamicOpLists(self):
-
- if "conv2d_TEMPLATE" not in self.TOSA_OP_LIST:
- # Already created these lists (can occur when class is initialized more than once)
- return
-
- # Dynamically create op lists for convolutions with a list of kernel sizes
- if not self.args.level8k:
- KERNELS_2D = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]]
- KERNELS_3D = [[1, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]]
- else:
- bigK = self.TOSA_8K_LEVEL_MAX_KERNEL
- KERNELS_2D = [[1, bigK], [bigK, 2]]
- KERNELS_3D = [[1, bigK, 1], [2, 2, bigK]]
-
- for k in KERNELS_2D:
- testName = "conv2d_{}x{}".format(k[0], k[1])
- self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv2d_TEMPLATE"].copy()
- self.TOSA_OP_LIST[testName]["filter"] = k
- self.TOSA_OP_LIST[testName]["template"] = False
- self.TOSA_OP_LIST[testName]["real_name"] = "conv2d"
-
- testName = "depthwise_conv2d_{}x{}".format(k[0], k[1])
- self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[
- "depthwise_conv2d_TEMPLATE"
- ].copy()
- self.TOSA_OP_LIST[testName]["filter"] = k
- self.TOSA_OP_LIST[testName]["template"] = False
- self.TOSA_OP_LIST[testName]["real_name"] = "depthwise_conv2d"
-
- testName = "transpose_conv2d_{}x{}".format(k[0], k[1])
- self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[
- "transpose_conv2d_TEMPLATE"
- ].copy()
- self.TOSA_OP_LIST[testName]["filter"] = k
- self.TOSA_OP_LIST[testName]["template"] = False
- self.TOSA_OP_LIST[testName]["real_name"] = "transpose_conv2d"
-
- for k in KERNELS_3D:
- testName = "conv3d_{}x{}x{}".format(k[0], k[1], k[2])
- self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv3d_TEMPLATE"].copy()
- self.TOSA_OP_LIST[testName]["filter"] = k
- self.TOSA_OP_LIST[testName]["template"] = False
- self.TOSA_OP_LIST[testName]["real_name"] = "conv3d"
-
- # Delete any templates after having created any dynamic ops
- # This is a two-pass operation because it's bad practice to delete
- # keys from dictionaries while iterating
- keyList = []
- for k in self.TOSA_OP_LIST:
+ # Find all the ops marked as templates
+ templateKeys = []
+ for opName in self.TOSA_OP_LIST:
try:
- if self.TOSA_OP_LIST[k]["template"]:
- keyList.append(k)
- continue
+ if self.TOSA_OP_LIST[opName]["template"]:
+ templateKeys.append(opName)
except KeyError:
pass
- for k in keyList:
- del self.TOSA_OP_LIST[k]
+ bigK = self.TOSA_8K_LEVEL_MAX_KERNEL
+
+ # Add dynamic ops based on kernel sizes
+ for opName in templateKeys:
+ assert opName.endswith("_TEMPLATE"), "Found incorrect template"
+ realName = opName[: len(opName) - len("_TEMPLATE")]
+ template = self.TOSA_OP_LIST[opName]
+ k_rank = 3 if realName == "conv3d" else 2
+
+ # Choose kernels to build tests for from the template or args
+ if self.args.level8k:
+ if k_rank == 3:
+ kernels = [[1, bigK, 1], [2, 2, bigK]]
+ else:
+ kernels = [[1, bigK], [bigK, 2]]
+ else:
+ kernels = []
+ if len(self.args.conv_kernels) > 0:
+ kernels = [k for k in self.args.conv_kernels if len(k) == k_rank]
+ if len(kernels) == 0:
+ logger.debug(
+ f"{realName} op using defaults as no rank {k_rank} kernels found in {self.args.conv_kernels}"
+ )
+ if len(kernels) == 0:
+ # Fallback to use the defined template kernels
+ kernels = self.TOSA_OP_LIST[opName]["filter"]
+
+ # Dynamically create ops for listed kernel sizes
+ for k in kernels:
+ kernelStr = "x".join([str(d) for d in k])
+ testName = f"{realName}_{kernelStr}"
+ kernelOp = template.copy()
+ kernelOp["filter"] = k
+ kernelOp["template"] = False
+ kernelOp["real_name"] = realName
+ self.TOSA_OP_LIST[testName] = kernelOp
+
+ # Delete the template after having created the dynamic ops
+ del self.TOSA_OP_LIST[opName]
def initOpListDefaults(self):
"""Fill in default fields for ops if they aren't already specified.
@@ -3333,6 +3323,9 @@
DEFAULT_RANK_RANGE = (1, gtu.MAX_TENSOR_RANK)
+ KERNELS_2D = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]]
+ KERNELS_3D = [[1, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]]
+
TOSA_OP_LIST = {
# Tensor operators
"argmax": {
@@ -3426,6 +3419,8 @@
"data_gen": {
"fp": (gtu.DataGenType.DOT_PRODUCT,),
},
+ "broadcastable_bias": True,
+ "filter": KERNELS_2D,
"template": True,
},
# Templated operator. Filled in by createDynamicOpLists
@@ -3460,13 +3455,13 @@
"data_gen": {
"fp": (gtu.DataGenType.DOT_PRODUCT,),
},
+ "filter": KERNELS_3D,
"template": True,
},
# Templated operator. Filled in by createDynamicOpLists
"depthwise_conv2d_TEMPLATE": {
"op": Op.DEPTHWISE_CONV2D,
"operands": (1, 2),
- "filter": [1, 1],
"rank": (4, 4),
"build_fcn": (
build_depthwise_conv2d,
@@ -3495,6 +3490,7 @@
"data_gen": {
"fp": (gtu.DataGenType.DOT_PRODUCT,),
},
+ "filter": KERNELS_2D,
"template": True,
},
"fully_connected": {
@@ -3608,6 +3604,7 @@
"data_gen": {
"fp": (gtu.DataGenType.DOT_PRODUCT,),
},
+ "filter": KERNELS_2D,
"template": True,
},
# Activation functions