diff options
Diffstat (limited to 'verif/tosa_test_gen.py')
| -rw-r--r-- | verif/tosa_test_gen.py | 175 |
1 files changed, 142 insertions, 33 deletions
diff --git a/verif/tosa_test_gen.py b/verif/tosa_test_gen.py index 302e4f4..0e57a7b 100644 --- a/verif/tosa_test_gen.py +++ b/verif/tosa_test_gen.py @@ -158,6 +158,29 @@ class TosaTensorGen(): return shape_list @staticmethod + def tgScatter(testGen, opName, rank): + pl, const = opName['operands'] + + assert(pl == 2) + assert(const == 0) + assert(rank == 3) + + values_in_shape = testGen.makeShape(rank) + + # Constrict the batch size? + if testGen.args.max_batch_size: + values_in_shape[0] = (values_in_shape[0] % testGen.args.max_batch_size) + 1 + + W = testGen.randInt(testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1]) + input_shape = [values_in_shape[0], W, values_in_shape[2]] + + shape_list = [] + shape_list.append(values_in_shape.copy()) + shape_list.append(input_shape.copy()) + + return shape_list + + @staticmethod def tgBroadcastFuzz(testGen, op, rank): shape = testGen.makeShape(rank) @@ -650,6 +673,8 @@ class TosaArgGen: outputDTypeList = [ DType.INT8 ] elif m == ResizeMode.BILINEAR and dtype == DType.INT16: outputDTypeList = [ DType.INT48 ] + elif dtype == DType.FLOAT: + outputDTypeList = [ DType.FLOAT ] else: continue @@ -659,19 +684,52 @@ class TosaArgGen: # Randomly generate legal output dimensions and shift # and then compute the stride and offset based on them output_dims = [ testGen.randInt(), testGen.randInt() ] - - shift = testGen.randInt(1, 11) - - stride = [ (ifm_shape[1] << shift) // output_dims[0], - (ifm_shape[2] << shift) // output_dims[1] ] - - offset = [ testGen.randInt(-stride[0], (ifm_shape[1] << shift) - (output_dims[0] - 1) * stride[0]), - testGen.randInt(-stride[1], (ifm_shape[2] << shift) - (output_dims[1] - 1) * stride[1]) ] - - arg_list.append(('mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}'.format(m, shift, output_dims[0], output_dims[1], - testGen.typeStr(outputDType), stride[0], stride[1], - offset[0], offset[1]), - [m, stride, offset, shift, output_dims, dtype, outputDType])) + in_center_h = (ifm_shape[1] - 1) / 2.0 + in_center_w = (ifm_shape[2] - 1) / 2.0 + out_center_h = (output_dims[0] - 1) / 2.0 + out_center_w = (output_dims[1] - 1) / 2.0 + + fp_stride_y = float(ifm_shape[1]) / float(output_dims[0]) + fp_stride_x = float(ifm_shape[2]) / float(output_dims[1]) + fp_offset_y = in_center_h - fp_stride_y * out_center_h + fp_offset_x = in_center_w - fp_stride_x * out_center_w + + if outputDType == DType.FLOAT: + shift = 0 + stride = [0, 0] + offset = [0, 0] + stride_fp = [ fp_stride_y, fp_stride_x] + offset_fp = [ fp_offset_y, fp_offset_x] + arg_list.append(('mode{}_odim{}x{}_out{}_st{:.2f}x{:.2f}_off{:.2f}x{:.2f}'.format(m, output_dims[0], output_dims[1], + testGen.typeStr(outputDType), stride_fp[0], stride_fp[1], + offset_fp[0], offset_fp[1]), + [m, stride, offset, shift, stride_fp, offset_fp, output_dims, dtype, outputDType])) + else: + shift = 11 + unit = float(1 << shift) + stride_y = int(round(fp_stride_y * unit)) + stride_x = int(round(fp_stride_x * unit)) + offset_y = int(round(fp_offset_y * unit)) + offset_x = int(round(fp_offset_x * unit)) + + while (stride_y >= 32768 or stride_x >= 32768 or offset_y >= 32768 or offset_x >= 32768 or offset_y < -32768 or offset_x < -32768): + shift = shift - 1 + unit = float(1 << shift) + stride_y = int(round(fp_stride_y * unit)) + stride_x = int(round(fp_stride_x * unit)) + offset_y = int(round(fp_offset_y * unit)) + offset_x = int(round(fp_offset_x * unit)) + + stride = [ stride_y, stride_x] + offset = [ offset_y, offset_x] + + stride_fp = [0.0, 0.0] + offset_fp = [0.0, 0.0] + + arg_list.append(('mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}'.format(m, shift, output_dims[0], output_dims[1], + testGen.typeStr(outputDType), stride[0], stride[1], + offset[0], offset[1]), + [m, stride, offset, shift, stride_fp, offset_fp, output_dims, dtype, outputDType])) return arg_list @@ -1139,29 +1197,44 @@ class TosaTestGen: return result_tens - def build_gather(self, op, values, axis): + def build_gather(self, op, values): # Create a new indicies tensor # here with data that doesn't exceed the dimensions of the values tensor - max_val = values.shape[axis] - indicies_arr = np.int32(self.rng.integers(low=0, high=max_val, size=[self.randInt(1, max_val + 1)])) + K = values.shape[1] # K + W = self.randInt(self.args.tensor_shape_range[0], self.args.tensor_shape_range[1]) # W + indicies_arr = np.int32(self.rng.integers(low=0, high=K, size=[values.shape[0], W])) # (N, W) indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, Usage.INDEX, [], indicies_arr) - result_tens = OutputShaper.gatherOp(self.ser, values, indicies, axis) + result_tens = OutputShaper.gatherOp(self.ser, values, indicies) - attr = ts.TosaSerializerAttribute() - attr.AxisAttribute(axis) + self.ser.addOperator(op, [values.name, indicies.name], [result_tens.name]) - self.ser.addOperator(op, [values.name, indicies.name], [result_tens.name], attr) + return result_tens + + def build_scatter(self, op, values_in, input): + + # Create a new indicies tensor + # here with data that doesn't exceed the dimensions of the values_in tensor + + K = values_in.shape[1] # K + W = input.shape[1] # W + indicies_arr = np.int32(self.rng.integers(low=0, high=K, size=[values_in.shape[0], W])) # (N, W) + indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, Usage.INDEX, [], indicies_arr) + + result_tens = OutputShaper.scatterOp(self.ser, values_in, indicies, input) + + self.ser.addOperator(op, [values_in.name, indicies.name, input.name], [result_tens.name]) return result_tens - def build_resize(self, op, input, mode, stride, offset, shift, output_dims, input_dtype, output_dtype): - result_tens = OutputShaper.resizeOp(self.ser, input, mode, stride, offset, shift, output_dims, input_dtype, output_dtype) + def build_resize(self, op, input, mode, stride, offset, shift, stride_fp, offset_fp, output_dims, input_dtype, output_dtype): + result_tens = OutputShaper.resizeOp(self.ser, input, mode, stride, offset, shift, stride_fp, offset_fp, output_dims, input_dtype, output_dtype) attr = ts.TosaSerializerAttribute() - attr.ResizeAttribute(output_dims, stride, offset, shift, mode) + + attr.ResizeAttribute(output_dims, stride, offset, shift, stride_fp, offset_fp, mode) self.ser.addOperator(op, [input.name], [result_tens.name], attr) return result_tens @@ -1966,10 +2039,20 @@ class TosaTestGen: # Scatter/Gather 'gather': { 'op': Op.GATHER, + # Only specify 'values' tensor here. 'indices' is generated in op building stage 'operands': (1, 0), - 'build_fcn': (build_gather, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_INT }, + 'rank': (3, 3), + 'build_fcn': (build_gather, TosaTensorGen.tgBasic, None), + 'types': TYPE_INT_FP }, + 'scatter': + { 'op': Op.SCATTER, + # Only specify 'values_in' tensor here. + #'indices' and 'input' are generated in op building stage + 'operands': (2, 0), + 'rank': (3, 3), + 'build_fcn': (build_scatter, TosaTensorGen.tgScatter, None), + 'types': TYPE_INT_FP }, # Image operations 'resize': @@ -1977,7 +2060,7 @@ class TosaTestGen: 'operands': (1, 0), 'rank': (4, 4), 'build_fcn': ( build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), - 'types': [ DType.INT8, DType.INT16 ] }, + 'types': [ DType.INT8, DType.INT16, DType.FLOAT ] }, # Data nodes @@ -2319,11 +2402,27 @@ class OutputShaper: return ser.addOutput(output_shape, a.dtype, a.usage, a.dformat) @staticmethod - def gatherOp(ser, values, indicies, axis): - # indicies minus the axis + values - the indexes used to look up values. - output_shape = [*values.shape[0:axis], indicies.shape[0], *values.shape[axis+1:]] + def gatherOp(ser, values, indices): + assert len(values.shape) == 3 + assert len(indices.shape) == 2 + assert values.shape[0] == indices.shape[0] + + output_shape = [values.shape[0], indices.shape[1], values.shape[2]] + + return ser.addOutput(output_shape, values.dtype, values.usage, values.dformat) + + @staticmethod + def scatterOp(ser, values_in, indices, input): + assert len(values_in.shape) == 3 + assert len(indices.shape) == 2 + assert len(input.shape) == 3 + assert values_in.shape[0] == indices.shape[0] # N + assert input.shape[1] == indices.shape[1] # W + assert values_in.shape[2] == input.shape[2] # C + + output_shape = values_in.shape - return ser.addOutput(output_shape, values.dtype, indicies.usage, indicies.dformat) + return ser.addOutput(output_shape, values_in.dtype, values_in.usage, values_in.dformat) @staticmethod def tableOp(ser, input, table): @@ -2331,12 +2430,16 @@ class OutputShaper: return ser.addOutput(input.shape, DType.INT32, input.usage, input.dformat) @staticmethod - def resizeOp(ser, input, mode, stride, offset, shift, output_dims, input_dtype, output_dtype): + def resizeOp(ser, input, mode, stride, offset, shift, stride_fp, offset_fp, output_dims, input_dtype, output_dtype): output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3]] - if stride[0] <= 0 or stride[1] <= 0: - ser.setExpectedFailure(True, 'Negative or zero stride') + if input_dtype == DType.FLOAT: + if stride_fp[0] <= 0 or stride_fp[1] <= 0: + ser.setExpectedFailure(True, 'Negative or zero stride') + else: + if stride[0] <= 0 or stride[1] <= 0: + ser.setExpectedFailure(True, 'Negative or zero stride') if mode == ResizeMode.BILINEAR: if input_dtype == DType.INT8: @@ -2345,6 +2448,9 @@ class OutputShaper: elif input_dtype == DType.INT16: if output_dtype != DType.INT48: ser.setexpectedfailure(true, 'Invalid output data type') + elif input_dtype == DType.FLOAT: + if output_dtype != DType.FLOAT: + ser.setexpectedfailure(true, 'Invalid output data type') else: ser.setexpectedfailure(true, 'Invalid input data type') @@ -2355,6 +2461,9 @@ class OutputShaper: elif input_dtype == DType.INT16: if output_dtype != DType.INT16: ser.setexpectedfailure(true, 'Invalid output data type') + elif input_dtype == DType.FLOAT: + if output_dtype != DType.FLOAT: + ser.setexpectedfailure(true, 'Invalid output data type') else: ser.setexpectedfailure(true, 'Invalid input data type') |