| import sys |
| import warnings |
| |
| import torch |
| from torch.onnx import symbolic_helper |
| from torch.onnx import symbolic_opset9 as opset9 |
| from torch.onnx import symbolic_opset10 as opset10 |
| from torch.onnx import utils |
| from torch.onnx._globals import GLOBALS |
| |
| # EDITING THIS FILE? READ THIS FIRST! |
| # see Note [Edit Symbolic Files] in symbolic_helper.py |
| |
| # This file exports ONNX ops for opset 11 |
| |
| |
| @symbolic_helper.parse_args("v", "f", "f") |
| def hardtanh(g, self, min_val, max_val): |
| dtype = self.type().scalarType() |
| if dtype is None: |
| dtype = symbolic_helper.ScalarType.FLOAT |
| else: |
| dtype = symbolic_helper.scalar_type_to_onnx.index( |
| symbolic_helper.cast_pytorch_to_onnx[dtype] |
| ) |
| min_val = g.op( |
| "Constant", |
| value_t=torch.tensor( |
| min_val, dtype=symbolic_helper.scalar_type_to_pytorch_type[dtype] |
| ), |
| ) |
| max_val = g.op( |
| "Constant", |
| value_t=torch.tensor( |
| max_val, dtype=symbolic_helper.scalar_type_to_pytorch_type[dtype] |
| ), |
| ) |
| return opset9.op_with_optional_float_cast( |
| g, "Clip", self, min_val, max_val, opset_before=12 |
| ) |
| |
| |
| def clamp(g, self, min, max): |
| dtype = self.type().scalarType() |
| |
| def _cast_if_not_none(tensor, dtype): |
| if tensor is not None and not symbolic_helper._is_none(tensor): |
| return g.op( |
| "Cast", tensor, to_i=symbolic_helper.cast_pytorch_to_onnx[dtype] |
| ) |
| else: |
| return tensor |
| |
| if dtype is not None: |
| min = _cast_if_not_none(min, dtype) |
| max = _cast_if_not_none(max, dtype) |
| |
| if symbolic_helper._is_none(min): |
| return clamp_max(g, self, max) |
| elif symbolic_helper._is_none(max): |
| return clamp_min(g, self, min) |
| else: |
| if ( |
| symbolic_helper._get_tensor_rank(min) == 0 |
| and symbolic_helper._get_tensor_rank(max) == 0 |
| ): |
| return opset9.op_with_optional_float_cast( |
| g, "Clip", self, min, max, opset_before=12 |
| ) |
| else: |
| return clamp_max(g, clamp_min(g, self, min), max) |
| |
| |
| @symbolic_helper.parse_args("v", "v") |
| def clamp_min(g, self, min): |
| dtype = self.type().scalarType() |
| min = g.op("Cast", min, to_i=symbolic_helper.cast_pytorch_to_onnx[dtype]) |
| if symbolic_helper._get_tensor_rank(min) == 0: |
| max = opset9.unused(g) |
| return opset9.op_with_optional_float_cast( |
| g, "Clip", self, min, max, opset_before=12 |
| ) |
| else: |
| return opset9.op_with_optional_float_cast(g, "Max", self, min, opset_before=12) |
| |
| |
| @symbolic_helper.parse_args("v", "v") |
| def clamp_max(g, self, max): |
| dtype = self.type().scalarType() |
| max = g.op("Cast", max, to_i=symbolic_helper.cast_pytorch_to_onnx[dtype]) |
| if symbolic_helper._get_tensor_rank(max) == 0: |
| min = opset9.unused(g) |
| return opset9.op_with_optional_float_cast( |
| g, "Clip", self, min, max, opset_before=12 |
| ) |
| else: |
| return opset9.op_with_optional_float_cast(g, "Min", self, max, opset_before=12) |
| |
| |
| def relu6(g, input): |
| relu = opset9.op_with_optional_float_cast(g, "Relu", input, opset_before=14) |
| dtype = input.type().scalarType() |
| if dtype is None: |
| dtype = symbolic_helper.ScalarType.FLOAT |
| else: |
| dtype = symbolic_helper.scalar_type_to_onnx.index( |
| symbolic_helper.cast_pytorch_to_onnx[dtype] |
| ) |
| min_val = g.op( |
| "Constant", |
| value_t=torch.tensor( |
| 0, dtype=symbolic_helper.scalar_type_to_pytorch_type[dtype] |
| ), |
| ) |
| max_val = g.op( |
| "Constant", |
| value_t=torch.tensor( |
| 6, dtype=symbolic_helper.scalar_type_to_pytorch_type[dtype] |
| ), |
| ) |
| return clamp(g, relu, min_val, max_val) |
| |
| |
| # Opset 11 gather accepts negative indices |
| @symbolic_helper.parse_args("v", "i", "v") |
| def select(g, self, dim, index): |
| return g.op("Gather", self, index, axis_i=dim) |
| |
| |
| def index_put(g, self, indices_list_value, values, accumulate=False): |
| if symbolic_helper._is_packed_list(indices_list_value): |
| indices_list = symbolic_helper._unpack_list(indices_list_value) |
| else: |
| indices_list = [indices_list_value] |
| if symbolic_helper.is_caffe2_aten_fallback(): |
| args = [self] + indices_list + [values, accumulate] |
| return g.at("index_put", *args) |
| |
| accumulate = symbolic_helper._parse_arg(accumulate, "b") |
| |
| if len(indices_list) == 0: |
| return values |
| |
| if len(indices_list) > 1: |
| for idx_ in range(len(indices_list)): |
| if indices_list[idx_].type().scalarType() == "Bool": |
| indices_list[idx_] = g.op("NonZero", indices_list[idx_]) |
| index = indices_list[0] |
| |
| for ind in indices_list[1:]: |
| index = opset9.add(g, index, ind) |
| broadcast_index_shape = g.op("Shape", index) |
| indices_list = [ |
| symbolic_helper._unsqueeze_helper( |
| g, opset9.expand(g, ind, broadcast_index_shape, None), [-1] |
| ) |
| for ind in indices_list |
| ] |
| index = g.op("Concat", *indices_list, axis_i=-1) |
| else: |
| # Replace index_put node with masked_scatter or masked_fill |
| # when inputs to the index_put node contains a single boolean input. |
| # |
| # index_put -> masked_fill |
| # * input index contains single tensor of Bool type (e.g.: %24 <- %23). |
| # * input value contains single element (e.g.: %18). |
| # |
| # Torch IR |
| # %mask : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = aten::clone(%0, %6) |
| # %16 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = |
| # aten::to(%8, %26, %27, %11, %12, %28, %29, %15) |
| # %18 : Float(requires_grad=0, device=cpu) = prim::Constant[value={1}]() |
| # %23 : Bool(8, strides=[1], device=cpu) = aten::view(%16, %22) |
| # %24 : Tensor?[] = prim::ListConstruct(%23) |
| # %25 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = |
| # aten::index_put(%mask, %24, %18, %30) |
| # return (%25) |
| # |
| # |
| # index_put -> masked_scatter |
| # * input index contains single tensor of Bool type (e.g.: %32 <- %31). |
| # * input value contains multiple elements (e.g.: %28). |
| # |
| # Torch IR |
| # %mask : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = aten::clone(%0, %6) |
| # %28 : Float(8, strides=[1], requires_grad=0, device=cpu) |
| # = prim::Constant[value= 1 1 1 1 1 1 1 1 [ CPUFloatType{8} ]]() |
| # %15 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) |
| # = aten::ne(%mask, %some_const) |
| # %23 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) |
| # = aten::to(%15, %34, %35, %18, %19, %36, %37, %22) |
| # %38 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]() |
| # %30 : int[] = prim::Constant[value=[-1]]() |
| # %31 : Bool(8, strides=[1], device=cpu) = aten::view(%23, %30) |
| # %32 : Tensor?[] = prim::ListConstruct(%31) |
| # %33 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) |
| # = aten::index_put(%mask, %32, %28, %38) |
| # return (%33) |
| index = indices_list[0] |
| bool_inp = index |
| if bool_inp.type() is not None and bool_inp.type().scalarType() == "Bool": |
| rank = symbolic_helper._get_tensor_rank(values) |
| if rank is not None and rank == 0: |
| return opset9.masked_fill(g, self, bool_inp, values) |
| return masked_scatter(g, self, bool_inp, values) |
| broadcast_index_shape = g.op("Shape", index) |
| index = symbolic_helper._unsqueeze_helper(g, index, [-1]) |
| sub_data_shape = symbolic_helper._slice_helper( |
| g, g.op("Shape", self), axes=[0], starts=[len(indices_list)], ends=[sys.maxsize] |
| ) |
| values_shape = g.op("Concat", broadcast_index_shape, sub_data_shape, axis_i=0) |
| # Check if values is a singular value and expand accordingly |
| rank = symbolic_helper._get_tensor_rank(values) |
| if rank is not None and rank == 0: |
| values = opset9.expand(g, values, values_shape, None) |
| values = symbolic_helper._reshape_helper(g, values, values_shape) |
| |
| dtype = self.type().scalarType() |
| if dtype is not None and dtype != values.type().scalarType(): |
| values = g.op("Cast", values, to_i=symbolic_helper.cast_pytorch_to_onnx[dtype]) |
| dtype = symbolic_helper.scalar_type_to_onnx.index( |
| symbolic_helper.cast_pytorch_to_onnx[dtype] |
| ) |
| dtype = symbolic_helper.scalar_type_to_pytorch_type[dtype] |
| |
| if accumulate: |
| zeros = g.op( |
| "ConstantOfShape", |
| g.op("Shape", self), |
| value_t=torch.tensor([0], dtype=dtype), |
| ) |
| result = g.op("ScatterND", zeros, index, values) |
| result = add(g, self, result) |
| else: |
| result = g.op("ScatterND", self, index, values) |
| |
| return result |
| |
| |
| @symbolic_helper.parse_args("v", "i") |
| def pixel_shuffle(g, self, upscale_factor): |
| rank = symbolic_helper._get_tensor_rank(self) |
| if rank is not None and rank != 4: |
| return symbolic_helper._unimplemented("pixel_shuffle", "only support 4d input") |
| return g.op("DepthToSpace", self, blocksize_i=upscale_factor, mode_s="CRD") |
| |
| |
| def _interpolate(name, dim, interpolate_mode): |
| return symbolic_helper._interpolate_helper(name, dim, interpolate_mode) |
| |
| |
| upsample_nearest1d = _interpolate("upsample_nearest1d", 3, "nearest") |
| upsample_nearest2d = _interpolate("upsample_nearest2d", 4, "nearest") |
| upsample_nearest3d = _interpolate("upsample_nearest3d", 5, "nearest") |
| upsample_linear1d = _interpolate("upsample_linear1d", 3, "linear") |
| upsample_bilinear2d = _interpolate("upsample_bilinear2d", 4, "linear") |
| upsample_trilinear3d = _interpolate("upsample_trilinear3d", 5, "linear") |
| upsample_bicubic2d = _interpolate("upsample_bicubic2d", 4, "cubic") |
| |
| |
| @symbolic_helper.quantized_args(True, False, False, False, False, False, False) |
| def __interpolate( |
| g, input, size, scale_factor, mode, align_corners, recompute_scale_factor, antialias |
| ): |
| return symbolic_helper.__interpolate_helper( |
| g, input, size, scale_factor, mode, align_corners, recompute_scale_factor |
| ) |
| |
| |
| @symbolic_helper.parse_args("v", "i", "v", "v") |
| def gather(g, self, dim, index, sparse_grad=False): |
| if symbolic_helper._maybe_get_const(sparse_grad, "i"): |
| return symbolic_helper._unimplemented("gather", "sparse_grad == True") |
| if symbolic_helper.is_caffe2_aten_fallback(): |
| return g.at("gather", self, dim, index, sparse_grad) |
| return g.op("GatherElements", self, index, axis_i=dim) |
| |
| |
| @symbolic_helper.parse_args("v", "i", "v", "v") |
| def scatter(g, self, dim, index, src): |
| if symbolic_helper.is_caffe2_aten_fallback(): |
| return g.at("scatter", self, dim, index, src, overload_name="src") |
| src_type = src.type().scalarType() |
| src = symbolic_helper._maybe_get_scalar(src) |
| if symbolic_helper._is_value(src): |
| return g.op("ScatterElements", self, index, src, axis_i=dim) |
| else: |
| # Check if scalar "src" has same type as self (PyTorch allows different |
| # type for scalar src (but not when src is tensor)). If not, insert Cast node. |
| if self.type().scalarType() != src_type: |
| src = g.op( |
| "Cast", |
| src, |
| to_i=symbolic_helper.cast_pytorch_to_onnx[self.type().scalarType()], |
| ) |
| return g.op( |
| "ScatterElements", self, index, opset9.expand_as(g, src, index), axis_i=dim |
| ) |
| |
| |
| @symbolic_helper.parse_args("v", "i", "none") |
| def cumsum(g, self, dim, dtype=None): |
| dim_tensor = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.int)) |
| if dtype and dtype.node().kind() != "prim::Constant": |
| parsed_dtype = symbolic_helper._get_const(dtype, "i", "dtype") |
| cast = g.op( |
| "Cast", self, to_i=symbolic_helper.scalar_type_to_onnx[parsed_dtype] |
| ) |
| else: |
| cast = self |
| csum = g.op("CumSum", cast, dim_tensor) |
| return csum |
| |
| |
| def masked_select(g, self, mask): |
| index = opset9.nonzero(g, opset9.expand_as(g, mask, self)) |
| return g.op("GatherND", self, index) |
| |
| |
| def masked_scatter(g, self, mask, source): |
| index = opset9.nonzero(g, opset9.expand_as(g, mask, self)) |
| # NOTE: source can have more elements than needed. |
| # It could also have arbitrary shape. |
| # This is not supported by ONNX::ScatterND, so we need to flatten and slice source tensor. |
| source = symbolic_helper._reshape_helper(g, source, torch.LongTensor([-1])) |
| source = symbolic_helper._slice_helper( |
| g, |
| source, |
| axes=torch.LongTensor([0]), |
| starts=torch.LongTensor([0]), |
| ends=opset9.size(g, index, torch.LongTensor([0])), |
| dynamic_slice=True, |
| ) |
| return g.op("ScatterND", self, index, source) |
| |
| |
| def _len(g, self): |
| if ( |
| symbolic_helper._is_tensor_list(self) |
| or self.node().kind() == "onnx::SplitToSequence" |
| ): |
| return g.op("SequenceLength", self) |
| sz_0 = size(g, self, g.op("Constant", value_t=torch.LongTensor([0]))) |
| return symbolic_helper._squeeze_helper(g, sz_0, [0]) |
| |
| |
| def __getitem_(g, self, i): |
| if symbolic_helper._is_tensor_list(self): |
| # SequenceAt requires that the input be a List of Tensors |
| return g.op("SequenceAt", self, i) |
| else: |
| from torch.onnx.symbolic_opset9 import __getitem_ as getitem |
| |
| return getitem(g, self, i) |
| |
| |
| def _set_item(g, tensor_list, i, v): |
| tensor_list = g.op("SequenceErase", tensor_list, i) |
| return g.op("SequenceInsert", tensor_list, v, i) |
| |
| |
| def append(g, self, tensor): |
| return g.op("SequenceInsert", self, tensor) |
| |
| |
| def add(g, self, other, alpha=None): |
| if symbolic_helper._is_value(self) and symbolic_helper._is_tensor_list(self): |
| tensor_list_node = other.node() |
| if tensor_list_node.kind() != "prim::ListConstruct": |
| return symbolic_helper._unimplemented( |
| "add", "does not support adding dynamic tensor list to another" |
| ) |
| tensors = symbolic_helper._unpack_list(other) |
| l = self |
| for t in tensors: |
| l = g.op("SequenceInsert", l, t) |
| return l |
| |
| return opset9.add(g, self, other, alpha) |
| |
| |
| def insert(g, self, pos, tensor): |
| return g.op("SequenceInsert", self, tensor, pos) |
| |
| |
| def pop(g, tensor_list, dim): |
| return g.op("SequenceErase", tensor_list, dim) |
| |
| |
| def Delete(g, tensor_list, dim): |
| return g.op("SequenceErase", tensor_list, dim) |
| |
| |
| def cat(g, tensor_list, dim): |
| if symbolic_helper._is_packed_list(tensor_list): |
| return opset9.cat(g, tensor_list, dim) |
| else: |
| dim = symbolic_helper._get_const(dim, "i", "dim") |
| return g.op("ConcatFromSequence", tensor_list, axis_i=dim) |
| |
| |
| def stack(g, tensor_list, dim): |
| if symbolic_helper._is_packed_list(tensor_list): |
| return opset9.stack(g, tensor_list, dim) |
| else: |
| dim = symbolic_helper._get_const(dim, "i", "dim") |
| return g.op("ConcatFromSequence", tensor_list, axis_i=dim, new_axis_i=1) |
| |
| |
| @symbolic_helper.parse_args("v", "i", "i", "i") |
| def _unique2(g, self, sorted, return_inverse, return_counts): |
| u, indices, inverse_indices, counts = g.op( |
| "Unique", self, sorted_i=sorted, outputs=4 |
| ) |
| return u, inverse_indices, counts |
| |
| |
| def _avg_pool(name, tuple_fn): |
| @symbolic_helper.quantized_args(True, False, False, False, False, False, False) |
| @symbolic_helper.parse_args("v", "is", "is", "is", "i", "i", "none") |
| def symbolic_fn( |
| g, |
| input, |
| kernel_size, |
| stride, |
| padding, |
| ceil_mode, |
| count_include_pad, |
| divisor_override=None, |
| ): |
| padding = symbolic_helper._avgpool_helper( |
| tuple_fn, padding, kernel_size, stride, divisor_override, name |
| ) |
| if not stride: |
| stride = kernel_size |
| if count_include_pad: |
| input = g.op( |
| "Pad", |
| input, |
| g.op("Constant", value_t=torch.tensor(((0,) * 2 + padding) * 2)), |
| mode_s="constant", |
| ) |
| padding = (0,) * len(padding) |
| output = g.op( |
| "AveragePool", |
| input, |
| kernel_shape_i=tuple_fn(kernel_size), |
| strides_i=tuple_fn(stride), |
| pads_i=padding * 2, |
| ceil_mode_i=ceil_mode, |
| ) |
| return output |
| |
| return symbolic_fn |
| |
| |
| avg_pool1d = _avg_pool("avg_pool1d", torch.nn.modules.utils._single) |
| avg_pool2d = _avg_pool("avg_pool2d", torch.nn.modules.utils._pair) |
| avg_pool3d = _avg_pool("avg_pool3d", torch.nn.modules.utils._triple) |
| |
| |
| @symbolic_helper.parse_args("v", "i", "i", "i", "i") |
| def unique_dim(g, self, dim, sorted, return_inverse, return_counts): |
| u, indices, inverse_indices, counts = g.op( |
| "Unique", self, axis_i=dim, sorted_i=sorted, outputs=4 |
| ) |
| return u, inverse_indices, counts |
| |
| |
| @symbolic_helper.parse_args("v", "v", "i", "i", "i", "none") |
| def topk(g, self, k, dim, largest, sorted, out=None): |
| return symbolic_helper._topk_helper( |
| g, self, k, dim, largest=largest, sorted=sorted, out=out |
| ) |
| |
| |
| @symbolic_helper.parse_args("v", "i", "i", "none") |
| def sort(g, self, dim, decending, out=None): |
| return symbolic_helper._sort_helper(g, self, dim, decending=decending, out=out) |
| |
| |
| def round(g, self): |
| return g.op("Round", self) |
| |
| |
| def remainder(g, input, other): |
| if symbolic_helper._is_fp(input) or symbolic_helper._is_fp(other): |
| return opset9.remainder(g, input, other) |
| return g.op("Mod", input, other, fmod_i=0) |
| |
| |
| @symbolic_helper.parse_args("v", "v", "i", "i") |
| def split(g, self, split_size_or_sizes, dim, _outputs=None): |
| if not symbolic_helper._is_split_static(split_size_or_sizes, _outputs): |
| split_out = g.op("SplitToSequence", self, split_size_or_sizes, axis_i=dim) |
| if _outputs is None: |
| return split_out |
| # Convert to multiple slice nodes iff number of splits and number of outputs are statically known. |
| if ( |
| symbolic_helper._is_packed_list(split_size_or_sizes) |
| and len(symbolic_helper._unpack_list(split_size_or_sizes)) == _outputs |
| ): |
| split_sizes = [ |
| symbolic_helper._unsqueeze_helper(g, v, [0]) |
| for v in symbolic_helper._unpack_list(split_size_or_sizes) |
| ] |
| start = g.op("Constant", value_t=torch.tensor([0], dtype=torch.long)) |
| axis = g.op("Constant", value_t=torch.tensor([dim], dtype=torch.long)) |
| res = [] |
| for i in range(_outputs): |
| end = g.op( |
| "Add", start, split_sizes[i] |
| ) # split_sizes is a list of same length as _outputs |
| res.append(g.op("Slice", self, start, end, axis)) |
| start = end |
| return res |
| return [ |
| g.op( |
| "SequenceAt", |
| split_out, |
| g.op("Constant", value_t=torch.tensor([i], dtype=torch.long)), |
| ) |
| for i in range(_outputs) |
| ] |
| else: |
| return opset9.split(g, self, split_size_or_sizes, dim, _outputs) |
| |
| |
| @symbolic_helper.parse_args("v", "v", "i", "i") |
| def split_with_sizes(g, self, split_sizes, dim, _outputs=None): |
| return split(g, self, split_sizes, dim, _outputs) |
| |
| |
| @symbolic_helper.parse_args("v", "i", "i") |
| def unbind(g, self, dim=0, _outputs=None): |
| if _outputs is None: |
| return g.op( |
| "SplitToSequence", |
| self, |
| g.op("Constant", value_t=torch.tensor(1, dtype=torch.long)), |
| axis_i=dim, |
| keepdims_i=0, |
| ) |
| else: |
| return opset9.unbind(g, self, dim, _outputs) |
| |
| |
| # Generate paddings in ONNX order based on pad in pytorch. |
| # Args: |
| # input: the input tensor. |
| # pad: the paddings in pytorch. |
| # The order is dim_n_begin, dim_n_end, dim_n-1_begin, dim_n-1_end, ..., dim_m_begin, dim_m_end, |
| # where m is in range [0, n]. |
| def _prepare_onnx_paddings(g, input, pad): |
| if ( |
| not symbolic_helper._is_packed_list(pad) |
| and symbolic_helper._is_list(pad) |
| and symbolic_helper._is_scalar_list(pad) |
| ): |
| pad = g.op("ConcatFromSequence", pad, axis_i=0, new_axis_i=1) |
| # The desired order of paddings is |
| # dim_0_begin, dim_1_begin, ... , dim_0_end, ..., dim_n_end. |
| # n is the dimension of input. |
| # Assume zero-dimensions in the beginning, pad the "pad" sequence with zeros in the beginning |
| pad_len = opset9.size(g, pad, g.op("Constant", value_t=torch.tensor([0]))) |
| # Set extension = [0] * (dim * 2 - len(pad)) |
| rank = symbolic_helper._get_tensor_rank(input) |
| if rank is None: |
| rank = g.op("Size", g.op("Shape", input)) |
| else: |
| rank = g.op("Constant", value_t=torch.tensor(rank, dtype=torch.int64)) |
| extension = g.op( |
| "Sub", |
| g.op("Mul", rank, g.op("Constant", value_t=torch.tensor(2, dtype=torch.int64))), |
| pad_len, |
| ) |
| # Concat pad with extension: paddings = [dim_n_begin, dim_n_end, dim_n-1_begin, dim_n-1_end, 0, 0, ... ] |
| # Currently ONNX only supports int64 type for Pad |
| pad = g.op("Cast", pad, to_i=symbolic_helper.cast_pytorch_to_onnx["Long"]) |
| paddings = g.op( |
| "Concat", |
| pad, |
| g.op( |
| "ConstantOfShape", extension, value_t=torch.tensor([0], dtype=torch.int64) |
| ), |
| axis_i=0, |
| ) |
| # Reshape and reverse order and collate first beginnings and then ends |
| # paddings = [[..., 0, dim_n-1_begin, dim_n_begin], |
| # [..., 0, dim_n-1_end, dim_n_end]] |
| # Reshape back to 1-D paddings = [..., 0, dim_n - 1_begin, dim_n_begin, ..., 0, dim_n - 1_end, dim_n_end] |
| paddings = symbolic_helper._reshape_helper( |
| g, paddings, g.op("Constant", value_t=torch.tensor([-1, 2])) |
| ) |
| paddings = g.op("Transpose", opset10.flip(g, paddings, [0]), perm_i=[1, 0]) |
| paddings = symbolic_helper._reshape_helper( |
| g, paddings, g.op("Constant", value_t=torch.tensor([-1])) |
| ) |
| padding_c = g.op( |
| "Cast", paddings, to_i=symbolic_helper.cast_pytorch_to_onnx["Long"] |
| ) |
| return padding_c |
| |
| |
| def constant_pad_nd(g, input, padding, value=None): |
| mode = "constant" |
| value = symbolic_helper._maybe_get_scalar(value) |
| value = symbolic_helper._if_scalar_type_as(g, value, input) |
| pad = _prepare_onnx_paddings(g, input, padding) |
| return g.op("Pad", input, pad, value, mode_s=mode) |
| |
| |
| def reflection_pad(g, input, padding): |
| mode = "reflect" |
| paddings = _prepare_onnx_paddings(g, input, padding) |
| return g.op("Pad", input, paddings, mode_s=mode) |
| |
| |
| def replication_pad(g, input, padding): |
| mode = "edge" |
| paddings = _prepare_onnx_paddings(g, input, padding) |
| return g.op("Pad", input, paddings, mode_s=mode) |
| |
| |
| reflection_pad1d = reflection_pad |
| reflection_pad2d = reflection_pad |
| reflection_pad3d = reflection_pad |
| replication_pad1d = replication_pad |
| replication_pad2d = replication_pad |
| replication_pad3d = replication_pad |
| |
| |
| def pad(g, input, pad, mode, value): |
| mode = symbolic_helper._parse_arg(mode, "s") |
| if mode == "replicate": |
| return replication_pad(g, input, pad) |
| elif mode == "reflect": |
| return reflection_pad(g, input, pad) |
| elif mode == "constant": |
| return constant_pad_nd(g, input, pad, value) |
| elif mode == "circular": |
| return opset9._pad_circular(g, input, pad) |
| else: |
| raise RuntimeError(f"Unrecognized padding mode {mode}") |
| |
| |
| def linalg_det(g, self): |
| return g.op("Det", self) |
| |
| |
| def logdet(g, input): |
| return opset9.log(g, linalg_det(g, input)) |
| |
| |
| def arange(g, *args): |
| def _get_arange_dtype(dtype): |
| dtype = symbolic_helper._maybe_get_const(dtype, "i") |
| return dtype |
| |
| if len(args) == 2 or len(args) == 5: |
| if len(args) == 2: |
| # aten::arange(Scalar end, Tensor out) |
| dtype = None |
| else: |
| # aten::arange(Scalar end, ScalarType dtype, Layout, Device, bool pin_memory) |
| dtype = _get_arange_dtype(args[1]) |
| type, end, start, step = symbolic_helper._arange_cast_helper( |
| g, end=args[0], dtype=dtype |
| ) |
| start_default = g.op( |
| "Constant", |
| value_t=torch.tensor( |
| 0, dtype=symbolic_helper.scalar_type_to_pytorch_type[type] |
| ), |
| ) |
| delta_default = g.op( |
| "Constant", |
| value_t=torch.tensor( |
| 1, dtype=symbolic_helper.scalar_type_to_pytorch_type[type] |
| ), |
| ) |
| arange_tensor = g.op("Range", start_default, end, delta_default) |
| elif len(args) == 4 or len(args) == 7: |
| if len(args) == 4: |
| # aten::arange(Scalar start, Scalar end, Scalar step, Tensor out) |
| dtype = None |
| else: |
| # aten::arange(Scalar start, Scalar end, Scalar step, ScalarType dtype, Layout, Device, bool pin_memory) |
| dtype = _get_arange_dtype(args[3]) |
| type, end, start, step = symbolic_helper._arange_cast_helper( |
| g, start=args[0], end=args[1], step=args[2], dtype=dtype |
| ) |
| arange_tensor = g.op("Range", start, end, step) |
| elif len(args) == 6: |
| # aten::arange(Scalar start, Scalar end, ScalarType dtype, Layout, Device, bool pin_memory) |
| dtype = _get_arange_dtype(args[2]) |
| type, end, start, step = symbolic_helper._arange_cast_helper( |
| g, start=args[0], end=args[1], dtype=dtype |
| ) |
| delta_default = g.op( |
| "Constant", |
| value_t=torch.tensor( |
| 1, dtype=symbolic_helper.scalar_type_to_pytorch_type[type] |
| ), |
| ) |
| arange_tensor = g.op("Range", start, end, delta_default) |
| else: |
| raise NotImplementedError( |
| "Unknown aten::arange signature taking " + str(len(args)) + " arguments." |
| ) |
| return arange_tensor |
| |
| |
| @symbolic_helper.parse_args("v", "i") |
| def _dim_arange(g, like, dim): |
| like_shape = g.op("Shape", like) |
| stop = g.op( |
| "Gather", like_shape, g.op("Constant", value_t=torch.tensor(dim)), axis_i=0 |
| ) |
| if symbolic_helper.is_caffe2_aten_fallback(): |
| return g.op("_caffe2::Range", stop) |
| return arange(g, stop, 4, None, None, None) |
| |
| |
| def size(g, self, dim=None): |
| if dim is None: |
| return g.op("Shape", self) |
| return symbolic_helper._size_helper(g, self, dim) |
| |
| |
| def squeeze(g, self, dim=None): |
| if dim is None: |
| return g.op("Squeeze", self) |
| |
| # dim as a tensor |
| if not symbolic_helper._is_constant(dim): |
| return symbolic_helper._squeeze_helper(g, self, [dim]) |
| |
| dim = symbolic_helper._get_const(dim, "i", "dim") |
| |
| input_rank = symbolic_helper._get_tensor_rank(self) |
| adjusted_dim = dim |
| if input_rank is not None and dim < 0: |
| adjusted_dim += input_rank |
| dim_size = symbolic_helper._get_tensor_dim_size(self, adjusted_dim) |
| if (dim < 0 and input_rank is None) or dim_size is None: |
| # If onnx shape inference is not on, export always as dynamic. |
| # Because we cannot tell if observed static shape is also static at runtime. |
| # create "cond" node (condition is shape[i]==1) |
| dim_constant = g.op("Constant", value_t=torch.tensor([dim])) |
| size = symbolic_helper._size_helper(g, self, dim_constant) |
| const_one = g.op("Constant", value_t=torch.ones(1, dtype=torch.int64)) |
| cond = g.op("Equal", size, const_one) |
| # create the "If" node and add the "then" and "else" blocks to it. |
| if_node_outputs = g.op("If", cond) |
| if_node = if_node_outputs.node() |
| if_block = utils._add_block(if_node) |
| squeeze_ = symbolic_helper._squeeze_helper(if_block, self, [dim]) |
| utils._add_output_to_block(if_block, squeeze_) |
| else_block = utils._add_block(if_node) |
| identity_ = else_block.op("Identity", self) |
| utils._add_output_to_block(else_block, identity_) |
| return if_node_outputs |
| |
| # For static input shape |
| dim = adjusted_dim |
| if dim_size > 1: |
| warnings.warn( |
| "This model contains a squeeze operation on dimension " |
| + str(dim) |
| + ". The size of " |
| + "this dimension in the given input is " |
| + str(dim_size) |
| + ". The model will " |
| + "be exported without the squeeze node. If the model is intended to be used with dynamic " |
| + "input shapes, please export with dynamic_axes argument." |
| ) |
| return self |
| return symbolic_helper._squeeze_helper(g, self, [dim]) |
| |
| |
| def unsqueeze(g, self, dim): |
| if symbolic_helper._is_constant(dim): |
| dim = symbolic_helper._get_const(dim, "i", "dim") |
| |
| return symbolic_helper._unsqueeze_helper(g, self, [dim]) |
| |
| |
| def mm(g, self, other): |
| return g.op("Gemm", self, other, beta_f=0.0, alpha_f=1.0) |
| |
| |
| def index(g, self, index): |
| if symbolic_helper.is_caffe2_aten_fallback(): |
| return g.at("index", self, index, overload_name="Tensor") |
| |
| if symbolic_helper._is_packed_list(index): |
| indices = symbolic_helper._unpack_list(index) |
| else: |
| indices = [index] |
| |
| # Handle single mask index. |
| if len(indices) == 1: |
| index = indices[0] |
| if not symbolic_helper._is_none(index) and ( |
| index.type().scalarType() == "Bool" or index.type().scalarType() == "Byte" |
| ): |
| index = opset9.nonzero(g, index) |
| return g.op("GatherND", self, index) |
| return opset9.index(g, self, index) |
| |
| |
| def index_fill(g, self, dim, index, value): |
| dim_value = symbolic_helper._parse_arg(dim, "i") |
| if symbolic_helper.is_caffe2_aten_fallback(): |
| return g.at( |
| "index_fill", |
| self, |
| index, |
| value, |
| overload_name="int_Scalar", |
| dim_i=dim_value, |
| ) |
| |
| expanded_index_shape, expanded_index = symbolic_helper._index_fill_reshape_helper( |
| g, self, dim, index |
| ) |
| value = symbolic_helper._maybe_get_scalar(value) |
| value = symbolic_helper._if_scalar_type_as(g, value, self) |
| expanded_value = opset9.expand(g, value, expanded_index_shape, None) |
| return scatter(g, self, dim, expanded_index, expanded_value) |
| |
| |
| def index_copy(g, self, dim, index, source): |
| dim_value = symbolic_helper._parse_arg(dim, "i") |
| if symbolic_helper.is_caffe2_aten_fallback(): |
| return g.at("index_copy", self, index, source, dim_i=dim_value) |
| expanded_index_shape, expanded_index = symbolic_helper._index_fill_reshape_helper( |
| g, self, dim, index |
| ) |
| return scatter(g, self, dim, expanded_index, source) |
| |
| |
| def __rshift_(g, self, other): |
| # make sure to cast other to self's type |
| # (when self is long, make sure that other is not float) |
| if other.type().scalarType() != self.type().scalarType(): |
| other = g.op( |
| "Cast", |
| other, |
| to_i=symbolic_helper.cast_pytorch_to_onnx[self.type().scalarType()], |
| ) |
| |
| if self.type().scalarType() == "Byte": |
| return g.op("BitShift", self, other, direction_s="RIGHT") |
| |
| two = g.op("Constant", value_t=torch.tensor(2, dtype=torch.float32)) |
| # exponent (same type as self) has to be float or double in onnx::Pow |
| if not symbolic_helper._is_fp(self): |
| other = g.op("Cast", other, to_i=symbolic_helper.cast_pytorch_to_onnx["Float"]) |
| two_pow = g.op("Pow", two, other) |
| two_pow = g.op( |
| "Cast", |
| two_pow, |
| to_i=symbolic_helper.cast_pytorch_to_onnx[self.type().scalarType()], |
| ) |
| rshift = g.op("Div", self, two_pow) |
| return rshift |
| |
| |
| def __lshift_(g, self, other): |
| # make sure to cast other to self's type |
| # (when self is long, make sure that other is not float) |
| if other.type().scalarType() != self.type().scalarType(): |
| other = g.op( |
| "Cast", |
| other, |
| to_i=symbolic_helper.cast_pytorch_to_onnx[self.type().scalarType()], |
| ) |
| |
| if self.type().scalarType() == "Byte": |
| return g.op("BitShift", self, other, direction_s="LEFT") |
| |
| two = g.op("Constant", value_t=torch.tensor(2, dtype=torch.float32)) |
| # exponent (same type as self) has to be float or double in onnx::Pow |
| if not symbolic_helper._is_fp(self): |
| other = g.op("Cast", other, to_i=symbolic_helper.cast_pytorch_to_onnx["Float"]) |
| two_pow = g.op("Pow", two, other) |
| two_pow = g.op( |
| "Cast", |
| two_pow, |
| to_i=symbolic_helper.cast_pytorch_to_onnx[self.type().scalarType()], |
| ) |
| lshift = g.op("Mul", self, two_pow) |
| return lshift |
| |
| |
| def _get_im2col_indices_along_dim( |
| g, input_d, kernel_size_d, dilation_d, padding_d, stride_d |
| ): |
| # Input is always 4-D (N, C, H, W) |
| # Calculate indices of sliding blocks along spatial dimension |
| # Slide kernel over input each dim d: |
| # each dimension d ranges from 0 to input[d]+2xpadding[d]-dilation[d]x(kernel_size[d]-1) |
| # with steps = stride |
| |
| blocks_d = g.op( |
| "Add", input_d, g.op("Constant", value_t=torch.tensor(padding_d * 2)) |
| ) |
| blocks_d = g.op( |
| "Sub", |
| blocks_d, |
| g.op("Constant", value_t=torch.tensor(dilation_d * (kernel_size_d - 1))), |
| ) |
| |
| # Stride kernel over input and find starting indices along dim d |
| blocks_d_indices = g.op( |
| "Range", |
| g.op("Constant", value_t=torch.tensor(0)), |
| blocks_d, |
| g.op("Constant", value_t=torch.tensor(stride_d)), |
| ) |
| |
| # Apply dilation on kernel and find its indices along dim d |
| kernel_grid = torch.arange(0, kernel_size_d * dilation_d, dilation_d) |
| kernel_grid = g.op("Constant", value_t=kernel_grid.unsqueeze(0)) |
| |
| # Broadcast and add kernel staring positions (indices) with |
| # kernel_grid along dim d, to get block indices along dim d |
| blocks_d_indices = symbolic_helper._unsqueeze_helper( |
| g, blocks_d_indices, [0] |
| ) # Reshape to [1, -1] |
| kernel_mask = symbolic_helper._reshape_helper( |
| g, kernel_grid, g.op("Constant", value_t=torch.tensor([-1, 1])) |
| ) |
| block_mask = g.op("Add", blocks_d_indices, kernel_mask) |
| |
| return block_mask |
| |
| |
| def _get_im2col_padded_input(g, input, padding_h, padding_w): |
| # Input is always 4-D tensor (N, C, H, W) |
| # Padding tensor has the following format: (padding_h, padding_w) |
| # Reshape the padding to follow ONNX format: (dim1_begin, dim2_begin,...,dim1_end, dim2_end,...) |
| pad = g.op("Constant", value_t=torch.LongTensor([0, 0, padding_h, padding_w] * 2)) |
| return g.op("Pad", input, pad) |
| |
| |
| def _get_im2col_output_shape(g, input, kernel_h, kernel_w): |
| batch_dim = size(g, input, g.op("Constant", value_t=torch.tensor(0))) |
| channel_dim = size(g, input, g.op("Constant", value_t=torch.tensor(1))) |
| channel_unfolded = g.op( |
| "Mul", channel_dim, g.op("Constant", value_t=torch.tensor(kernel_h * kernel_w)) |
| ) |
| |
| return g.op( |
| "Concat", |
| symbolic_helper._unsqueeze_helper(g, batch_dim, [0]), |
| symbolic_helper._unsqueeze_helper(g, channel_unfolded, [0]), |
| g.op("Constant", value_t=torch.tensor([-1])), |
| axis_i=0, |
| ) |
| |
| |
| @symbolic_helper.parse_args("v", "is", "is", "is", "is") |
| def im2col(g, input, kernel_size, dilation, padding, stride): |
| # Input is always 4-D tensor (N, C, H, W) |
| # All other args are int[2] |
| |
| input_h = size(g, input, g.op("Constant", value_t=torch.tensor(2))) |
| input_w = size(g, input, g.op("Constant", value_t=torch.tensor(3))) |
| |
| stride_h, stride_w = stride[0], stride[1] |
| padding_h, padding_w = padding[0], padding[1] |
| dilation_h, dilation_w = dilation[0], dilation[1] |
| kernel_h, kernel_w = kernel_size[0], kernel_size[1] |
| |
| blocks_row_indices = _get_im2col_indices_along_dim( |
| g, input_h, kernel_h, dilation_h, padding_h, stride_h |
| ) |
| blocks_col_indices = _get_im2col_indices_along_dim( |
| g, input_w, kernel_w, dilation_w, padding_w, stride_w |
| ) |
| |
| output_shape = _get_im2col_output_shape(g, input, kernel_h, kernel_w) |
| padded_input = _get_im2col_padded_input(g, input, padding_h, padding_w) |
| |
| # For a 4D matrix of size (1, 1, 3, 3) as below with kernel_size=2, stride=1, and dilation=1 |
| # [[[[1., 2., 3.,], |
| # [4., 5., 6.,], |
| # [7., 8., 9.,]]]] |
| # First gather indices along rows (dim=2) with blocks_row_indices = [[0,1], [1,2]] to get: |
| # [[[[[1., 2., 3.], |
| # [4., 5., 6.]], |
| # [[4., 5., 6.], |
| # [7., 8., 9.]]]]] |
| # And then gather along cols (dim=4) with blocks_row_indices = [[0,1], [1,2]] to get: |
| # [[[[[[1., 2.], |
| # [4., 5.]], |
| # [[2., 3.], |
| # [5., 6]]], |
| # [[[4., 5.], |
| # [7., 8.]], |
| # [[5., 6.], |
| # [8., 9.]]]]]] |
| # Transpose dims 3 (depth) and 4 (rows), and then reshape to output shape (1, 1, 4, 4) to get: |
| # [[[1., 2., 4., 5.], |
| # [2., 3., 5., 6.], |
| # [4., 5., 7., 8.], |
| # [5., 6., 8., 9.]]] |
| output = g.op("Gather", padded_input, blocks_row_indices, axis_i=2) |
| output = g.op("Gather", output, blocks_col_indices, axis_i=4) |
| output = g.op("Transpose", output, perm_i=[0, 1, 2, 4, 3, 5]) |
| return symbolic_helper._reshape_helper(g, output, output_shape) |
| |
| |
| def narrow(g, input, dim, start, length): |
| end = g.op("Add", start, length) |
| return symbolic_helper._slice_helper( |
| g, input, axes=dim, starts=start, ends=end, dynamic_slice=True |
| ) |
| |
| |
| @symbolic_helper.quantized_args(True, False, False) |
| @symbolic_helper.parse_args("v", "i", "i") |
| def flatten(g, input, start_dim, end_dim): |
| dim = symbolic_helper._get_tensor_rank(input) |
| if dim == 1: |
| return input |
| # use ONNX's Flatten operator for cases where the output shape is 2D |
| if start_dim == 1: |
| if end_dim == -1 or (dim is not None and end_dim == dim - 1): |
| return g.op("Flatten", input, axis_i=start_dim) |
| elif start_dim == 0: |
| if end_dim == -2 or (dim is not None and end_dim == dim - 2): |
| return g.op("Flatten", input, axis_i=end_dim + 1) |
| if dim is None: |
| return symbolic_helper._unimplemented( |
| "dim", |
| "ONNX and PyTorch use different strategies to split the input. " |
| "Input rank must be known at export time.", |
| ) |
| # if end_dim is negative add dim |
| if end_dim < 0: |
| end_dim = dim + end_dim |
| |
| return symbolic_helper._flatten_helper(g, input, start_dim, end_dim, dim) |
| |
| |
| @symbolic_helper.parse_args("v", "f", "is", "i", "v") |
| def linalg_vector_norm(g, self, ord, dim, keepdim, dtype): |
| if ord == 0: |
| if dim is None: |
| self = symbolic_helper._reshape_helper( |
| g, self, g.op("Constant", value_t=torch.tensor([-1], dtype=torch.int64)) |
| ) |
| keepdim = None |
| cond_op = g.op( |
| "Not", g.op("Equal", self, g.op("Constant", value_t=torch.LongTensor([0]))) |
| ) |
| cond_op = g.op( |
| "Cast", cond_op, to_i=symbolic_helper.cast_pytorch_to_onnx["Long"] |
| ) |
| return symbolic_helper._reducesum_helper( |
| g, cond_op, axes_i=dim, keepdims_i=keepdim |
| ) |
| else: |
| return opset9.linalg_vector_norm(g, self, ord, dim, keepdim, dtype) |
| |
| |
| @symbolic_helper.parse_args("v", "v", "v", "i", "i", "i", "v", "i", "i") |
| def embedding_bag( |
| g, |
| embedding_matrix, |
| indices, |
| offsets, |
| scale_grad_by_freq, |
| mode, |
| sparse, |
| per_sample_weights, |
| include_last_offset, |
| padding_idx, |
| ): |
| if scale_grad_by_freq and GLOBALS.training_mode: |
| return symbolic_helper._onnx_unsupported( |
| "embedding_bag with scale_grad_by_freq for training mode" |
| ) |
| if padding_idx is not None and padding_idx >= 0: |
| raise RuntimeError("embedding_bag with padding_idx") |
| |
| loop_condition = g.op("Constant", value_t=torch.tensor(1)) |
| loop_condition = g.op("Cast", loop_condition, to_i=9) |
| zero = g.op("Constant", value_t=torch.tensor([0])) |
| |
| indices_len = symbolic_helper._unsqueeze_helper( |
| g, |
| symbolic_helper._size_helper( |
| g, indices, g.op("Constant", value_t=torch.tensor(0)) |
| ), |
| [0], |
| ) |
| if not include_last_offset: |
| offsets = [offsets, indices_len] |
| offsets = g.op("Concat", *offsets, axis_i=0) |
| |
| # Offsets holds the starting index position of each bag. So we create a list of the indices slices (determined by |
| # offsets) and gather those indices in indices_row. Then we use this subset of indices to gather from embeddings. |
| # The embeddings output is a loop scan output, so we can avoid creating a sequence and inserting elements in. |
| offsets_starts = symbolic_helper._slice_helper( |
| g, offsets, axes=[0], starts=[0], ends=[sys.maxsize], steps=[1] |
| ) |
| offsets_ends = symbolic_helper._slice_helper( |
| g, offsets, axes=[0], starts=[1], ends=[sys.maxsize], steps=[1] |
| ) |
| |
| loop_len = symbolic_helper._size_helper( |
| g, offsets_ends, g.op("Constant", value_t=torch.tensor(0)) |
| ) |
| loop = g.op("Loop", loop_len, loop_condition) |
| |
| loop_block = utils._add_block(loop.node()) |
| block_input_iter = utils._add_input_to_block(loop_block) |
| cond = utils._add_input_to_block(loop_block) |
| |
| indices_start = loop_block.op("Gather", offsets_starts, block_input_iter, axis_i=0) |
| indices_end = loop_block.op("Gather", offsets_ends, block_input_iter, axis_i=0) |
| indices_start = symbolic_helper._unsqueeze_helper(loop_block, indices_start, [0]) |
| indices_end = symbolic_helper._unsqueeze_helper(loop_block, indices_end, [0]) |
| |
| indices_row = loop_block.op("Slice", indices, indices_start, indices_end, zero) |
| embeddings = loop_block.op("Gather", embedding_matrix, indices_row, axis_i=0) |
| if not symbolic_helper._is_none(per_sample_weights): |
| per_sample_weights_row = loop_block.op( |
| "Slice", per_sample_weights, indices_start, indices_end, zero |
| ) |
| per_sample_weights_row = symbolic_helper._unsqueeze_helper( |
| loop_block, per_sample_weights_row, [1] |
| ) |
| embeddings = loop_block.op("Mul", embeddings, per_sample_weights_row) |
| if mode == 0: |
| embeddings = symbolic_helper._reducesum_helper( |
| loop_block, embeddings, axes_i=[0], keepdims_i=0 |
| ) |
| elif mode == 1: |
| embeddings = loop_block.op("ReduceMean", embeddings, axes_i=[0], keepdims_i=0) |
| else: |
| embeddings = loop_block.op("ReduceMax", embeddings, axes_i=[0], keepdims_i=0) |
| |
| cond_out = loop_block.op("Cast", loop_condition, to_i=9) |
| utils._add_output_to_block(loop_block, cond_out) |
| utils._add_output_to_block(loop_block, embeddings) |
| |
| # aten::embedding_bag returns a tuple of 4 elements: output, offset2bag, bag_size, max_indices. |
| # But the last three outputs are not used in torch.nn.EmbeddingBag or torch.nn.functional.embedding_bag. |
| return loop.node().output(), None, None, None |
| |
| |
| @symbolic_helper.parse_args("v", "v", "f", "f") |
| def embedding_renorm(g, weight, indices, max_norm, norm_type): |
| unique_indices = g.op("Unique", indices) |
| partial_weight = g.op("Gather", weight, unique_indices) |
| norm_type = int(norm_type) |
| if norm_type == 1: |
| norm_type = "ReduceL1" |
| elif norm_type == 2: |
| norm_type = "ReduceL2" |
| else: |
| raise RuntimeError( |
| f"Unsupported: ONNX export of embedding_renorm with norm: {norm_type}. " |
| "Only 1. and 2. are supported." |
| ) |
| partial_weight_norm = g.op(norm_type, partial_weight, axes_i=[1], keepdims_i=1) |
| # https://github.com/pytorch/pytorch/blob/0a07488ed2c47765e337e290bd138c0e6e459cbd/aten/src/ATen/native/Embedding.cpp#L177 |
| # Add 1e-7 to prevent division by zero. |
| partial_weight_norm_ = g.op( |
| "Add", partial_weight_norm, g.op("Constant", value_t=torch.tensor(1e-7)) |
| ) |
| max_norm = torch.tensor(max_norm) |
| scales = g.op("Div", max_norm, partial_weight_norm_) |
| partial_weight_renorm = g.op("Mul", partial_weight, scales) |
| partial_weight_renorm = g.op( |
| "Where", |
| g.op("Greater", partial_weight_norm, max_norm), |
| partial_weight_renorm, |
| partial_weight, |
| ) |
| return g.op( |
| "ScatterND", |
| weight, |
| symbolic_helper._unsqueeze_helper(g, unique_indices, [1]), |
| partial_weight_renorm, |
| ) |
| |
| |
| def chunk(g, self, chunks, dim): |
| # Calculate chunk size for dynamic chunk |
| dim_size = g.op("Gather", g.op("Shape", self), dim, axis_i=0) |
| chunk_size_s = g.op( |
| "Sub", chunks, g.op("Constant", value_t=torch.tensor([1], dtype=torch.long)) |
| ) |
| chunk_size = g.op("Div", g.op("Add", dim_size, chunk_size_s), chunks) |
| # Create splits vector |
| chunk_vec = [ |
| opset9.expand(g, chunk_size, chunk_size_s, None), |
| g.op("Sub", dim_size, g.op("Mul", chunk_size, chunk_size_s)), |
| ] |
| chunk_vec = g.op("Concat", *chunk_vec, axis_i=0) |
| return split(g, self, chunk_vec, dim) |
| |
| |
| def normal(g, loc, scale, seed): |
| # If you can sample from a given distribution with mean 0 and variance 1, then you can easily sample from a |
| # scale-location transformation of that distribution, which has mean μ and variance σ's square. If x is a sample |
| # from a mean 0 and variance 1 distribution then |
| # σx+μ |
| # is a sample with mean μ and variance σ's square. |
| result = opset9.mul(g, scale, g.op("RandomNormalLike", loc)) |
| return add(g, result, loc) |
| |
| |
| class Prim: |
| domain = "prim" |
| |
| @staticmethod |
| def ConstantChunk(g, self, chunks, dim): |
| input_shape = g.op("Shape", self) |
| axis = g.op("Constant", value_t=torch.tensor([dim], dtype=torch.long)) |
| input_shape_dim = g.op("Gather", input_shape, axis, axis_i=0) |
| start = g.op("Constant", value_t=torch.tensor([0], dtype=torch.long)) |
| chunk_size = g.op("Constant", value_t=torch.tensor([chunks], dtype=torch.long)) |
| chunk_size_minus_1 = g.op( |
| "Constant", value_t=torch.tensor([chunks - 1], dtype=torch.long) |
| ) |
| input_shape_dim_shift = g.op("Add", input_shape_dim, chunk_size_minus_1) |
| chunk_dim = g.op("Div", input_shape_dim_shift, chunk_size) |
| res = [] |
| for i in range(chunks): |
| index = g.op("Constant", value_t=torch.tensor([i + 1], dtype=torch.long)) |
| end = g.op("Mul", chunk_dim, index) |
| res.append(g.op("Slice", self, start, end, axis)) |
| start = end |
| return res |
