diff options
Diffstat (limited to 'verif/tosa_test_gen.py')
| -rw-r--r-- | verif/tosa_test_gen.py | 160 |
1 files changed, 151 insertions, 9 deletions
diff --git a/verif/tosa_test_gen.py b/verif/tosa_test_gen.py index 44582ac..9555195 100644 --- a/verif/tosa_test_gen.py +++ b/verif/tosa_test_gen.py @@ -257,6 +257,35 @@ class TosaTensorGen: return [ifm_shape, filter_shape, bias_shape] @staticmethod + def tgConv3D(testGen, op, rank): + pl, const = op["operands"] + + assert rank == 5 + + # IFM dimensions are NDHWC + ifm_shape = testGen.makeShape(rank) + + # Constrict the batch size? + if testGen.args.max_batch_size: + ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 + + # Get the filter depth/height/width from the operator parameters + filter_dhw = op["filter"] + + # Generate a random OFM channel + ofm_channel = testGen.makeShape(1)[0] + + # The filter dimensions are ODHWI + filter_shape = np.asarray( + [ofm_channel, filter_dhw[0], filter_dhw[1], filter_dhw[2], ifm_shape[4]] + ) + + # The bias is OC + bias_shape = np.asarray([ofm_channel]) + + return [ifm_shape, filter_shape, bias_shape] + + @staticmethod def tgTransposeConv2D(testGen, op, rank): pl, const = op["operands"] @@ -463,6 +492,43 @@ class TosaArgGen: return arg_list @staticmethod + def agConv3D(testGen, opName, shapeList, dtype): + arg_list = [] + + ifm_shape = shapeList[0] + filter_shape = shapeList[1] + + # Must be rank 5 + assert len(ifm_shape) == 5 + assert len(filter_shape) == 5 + + # Generate basic argument list now + # TODO: increase coverage + s = [1, 1, 1] + p = [0, 0, 0, 0, 0, 0] + d = [1, 1, 1] + arg_list.append( + ( + "st{}{}{}_pad{}{}{}{}{}{}_dilat{}{}{}".format( + s[0], + s[1], + s[2], + p[0], + p[1], + p[2], + p[3], + p[4], + p[5], + d[0], + d[1], + d[2], + ), + [s, p, d], + ) + ) + return arg_list + + @staticmethod def agTransposeConv2D(testGen, opName, shapeList, dtype): arg_list = [] @@ -1357,6 +1423,20 @@ class TosaTestGen: ) return result_tens + def build_conv3d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): + assert len(padding) == 6 + result_tens = OutputShaper.conv3dOp( + self.ser, ifm, filter, strides, padding, dilations + ) + + attr = ts.TosaSerializerAttribute() + attr.ConvAttribute(padding, strides, dilations) + + self.ser.addOperator( + op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo + ) + return result_tens + def build_transpose_conv2d( self, op, ifm, filter, bias, stride, outpad, dilation, output_shape, qinfo ): @@ -1859,7 +1939,9 @@ class TosaTestGen: # Filter out the rank? if rankFilter is not None and r not in rankFilter: continue - if ( + if opName.startswith("conv3d"): + assert r == 5, "conv3d test must have input rank == 5" + elif ( rankFilter is None and shapeFilter[0] is None and r not in default_test_rank_range @@ -2188,9 +2270,9 @@ class TosaTestGen: def createDynamicOpLists(self): # Dynamically create op lists for convolutions with a list of kernel sizes - KERNELS = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]] + KERNELS_2D = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]] - for k in KERNELS: + 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 @@ -2210,6 +2292,13 @@ class TosaTestGen: self.TOSA_OP_LIST[testName]["filter"] = k self.TOSA_OP_LIST[testName]["template"] = False + KERNELS_3D = [[1, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]] + 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 + # 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 @@ -2286,7 +2375,7 @@ class TosaTestGen: TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FLOAT] - TYPE_CONV2D = [ + TYPE_CONV = [ [DType.INT8, DType.INT4, DType.INT32], [DType.INT8, DType.INT8, DType.INT32], [DType.INT16, DType.INT8, DType.INT48], @@ -2319,11 +2408,20 @@ class TosaTestGen: "rank": (4, 4), "build_fcn": (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv2D), "qgen": TosaQuantGen.qgConv, - "types": TYPE_CONV2D, + "types": TYPE_CONV, "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), "template": True, }, - # Conv3d TBD + # Templated operator. Filled in by createDynamicOpLists + "conv3d_TEMPLATE": { + "op": Op.CONV3D, + "operands": (1, 2), + "rank": (5, 5), + "build_fcn": (build_conv3d, TosaTensorGen.tgConv3D, TosaArgGen.agConv3D), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_CONV, + "template": True, + }, # Templated operator. Filled in by createDynamicOpLists "depthwise_conv2d_TEMPLATE": { "op": Op.DEPTHWISE_CONV2D, @@ -2336,7 +2434,7 @@ class TosaTestGen: TosaArgGen.agConv2D, ), "qgen": TosaQuantGen.qgConv, - "types": TYPE_CONV2D, + "types": TYPE_CONV, "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), "template": True, }, @@ -2346,7 +2444,7 @@ class TosaTestGen: "rank": (2, 2), "build_fcn": (build_fully_connected, TosaTensorGen.tgFullyConnected, None), "qgen": TosaQuantGen.qgConv, - "types": TYPE_CONV2D, + "types": TYPE_CONV, }, "matmul": { "op": Op.MATMUL, @@ -2375,7 +2473,7 @@ class TosaTestGen: TosaArgGen.agTransposeConv2D, ), "qgen": TosaQuantGen.qgConv, - "types": TYPE_CONV2D, + "types": TYPE_CONV, "invalid_test_validators": (TosaInvalidValidator.ivNonPositiveOutputShape,), "template": True, }, @@ -2909,6 +3007,50 @@ class OutputShaper: return ser.addOutput(ofm_shape, out_dtype) @staticmethod + def conv3dOp(ser, ifm, filter, strides, padding, dilations): + + # IFM: NDHWC + # Filter: ODHWI + # OFM: NDHWC + + d = ( + ifm.shape[1] + - filter.shape[1] + - (filter.shape[1] - 1) * (dilations[0] - 1) + + padding[0] + + padding[1] + ) // strides[0] + 1 + + h = ( + ifm.shape[2] + - filter.shape[2] + - (filter.shape[2] - 1) * (dilations[1] - 1) + + padding[2] + + padding[3] + ) // strides[1] + 1 + + w = ( + ifm.shape[3] + - filter.shape[3] + - (filter.shape[3] - 1) * (dilations[2] - 1) + + padding[4] + + padding[5] + ) // strides[2] + 1 + + ofm_shape = [ifm.shape[0], d, h, w, filter.shape[0]] + + if ifm.dtype == DType.INT8: + out_dtype = DType.INT32 + elif ifm.dtype == DType.INT16: + out_dtype = DType.INT48 + elif ifm.dtype == DType.FLOAT: + out_dtype = DType.FLOAT + else: + raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) + + return ser.addOutput(ofm_shape, out_dtype) + + @staticmethod def depthwiseConv2dOp(ser, ifm, filter, strides, padding, dilations): # IFM: NHWC # Filter: HWCM |