diff options
Diffstat (limited to 'verif/frameworks/tosa_verif_framework_generator.py')
| -rwxr-xr-x | verif/frameworks/tosa_verif_framework_generator.py | 73 |
1 files changed, 69 insertions, 4 deletions
diff --git a/verif/frameworks/tosa_verif_framework_generator.py b/verif/frameworks/tosa_verif_framework_generator.py index 097fe1f..fb7f35a 100755 --- a/verif/frameworks/tosa_verif_framework_generator.py +++ b/verif/frameworks/tosa_verif_framework_generator.py @@ -60,6 +60,7 @@ TYPE_FHIB = [tf.float32, tf.float16, tf.int32, tf.bool] # processing in createDynamicOpLists) # 'bias': boolean indicating that there is a bias component to be generated # 'qtypes': List of QuantType quantized types to generate for this op +# 'rank': tuple (lowest rank, highest rank). Dimension range of input tensor. TF_OP_LIST = { "add": { @@ -433,6 +434,37 @@ TF_OP_LIST = { "bias": True, "template": True, }, + "conv3d_TEMPLATE": { + "operands": (1, 1), + "build_fcn": (TBuilder.Conv3d, TGen.tgConv3d, ArgGen.agConv3d), + "types": { + "tf": [tf.float32], + "tflite": [ + tf.float32, + QuantType.CONV_U8_U8, + QuantType.CONV_I8_I8, + # Quantization to 16x8-bit not yet supported by tflite. + ], + }, + "template": True, + "rank": (1, 5), + }, + "conv3d_bias_TEMPLATE": { + "operands": (1, 2), + "build_fcn": (TBuilder.Conv3dWithBias, TGen.tgConv3d, ArgGen.agConv3d), + "types": { + "tf": [tf.float32], + "tflite": [ + tf.float32, + QuantType.CONV_U8_U8, + QuantType.CONV_I8_I8, + # Quantization to 16x8-bit not yet supported by tflite. + ], + }, + "bias": True, + "template": True, + "rank": (1, 5), + }, "depthwise_conv2d_TEMPLATE": { "operands": (1, 1), "build_fcn": ( @@ -762,6 +794,9 @@ shape_list = [ (1, 4, 8, 19), (1, 32, 32, 8), (1, 7, 7, 9), + (2, 2, 7, 7, 2), + (1, 4, 8, 21, 17), + (3, 32, 16, 16, 5), ] @@ -776,13 +811,13 @@ def gen_rand_shapes(args): max_total_volume = 32 * 32 * 4 shape_list = [] - # Only iterate over ranks 2, 3, and 4 - for rank in range(2, 5): + # Only iterate over ranks 2, 3, 4, and 5 + for rank in range(2, 6): for n in range(args.random_shapes): new_shape = rng.integers(1, 48, size=rank) - # Set the batch dimension on 4D objects to 1 - if rank == 4: + # Set the batch dimension on 4D or 5D objects to 1 + if rank == 4 or rank == 5: new_shape[0] = 1 # Limit the total shape volume and throw out any @@ -1190,6 +1225,16 @@ def build_const_net( op = TF_OP_LIST[op_name] op_fcn, tensor_gen_fcn, arg_gen_fcn = op["build_fcn"] + try: + rank_lo, rank_hi = op["rank"] + except KeyError: + # Set testing rank to (1, 4) in default. + rank_lo = 1 + rank_hi = 4 + + if len(curr_shape) not in range(rank_lo, rank_hi + 1): + return + addl_args_tuple = arg_gen_fcn(op, curr_shape, rng) for desc, addl_args in addl_args_tuple: # Only filter on the full test_name, not the output directory @@ -1335,6 +1380,13 @@ def createDynamicOpLists(): [5, 5], ] + # dim = [D, H, W] + KERNELS_3D = [ + [1, 1, 1], + [2, 3, 3], + [3, 5, 5], + ] + TEMPLATE_LIST = [ "conv2d", "conv2d_bias", @@ -1347,6 +1399,11 @@ def createDynamicOpLists(): "transpose_conv2d", ] + TEMPLATE_LIST_CONV3D = [ + "conv3d", + "conv3d_bias", + ] + for t in TEMPLATE_LIST: for k in KERNELS: testName = "{}_{}x{}".format(t, k[0], k[1]) @@ -1354,6 +1411,14 @@ def createDynamicOpLists(): TF_OP_LIST[testName]["filter"] = k TF_OP_LIST[testName]["template"] = False + # The existing operators don't support the dimension of kernel that is higher than 2. + for t in TEMPLATE_LIST_CONV3D: + for k in KERNELS_3D: + testName = "{}_{}x{}x{}".format(t, k[0], k[1], k[2]) + TF_OP_LIST[testName] = TF_OP_LIST["{}_TEMPLATE".format(t)].copy() + TF_OP_LIST[testName]["filter"] = k + TF_OP_LIST[testName]["template"] = False + # 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 |