import math import torch from torch._six import inf from typing import Optional class __PrinterOptions(object): precision: int = 4 threshold: float = 1000 edgeitems: int = 3 linewidth: int = 80 sci_mode: Optional[bool] = None PRINT_OPTS = __PrinterOptions() # We could use **kwargs, but this will give better docs def set_printoptions( precision=None, threshold=None, edgeitems=None, linewidth=None, profile=None, sci_mode=None ): r"""Set options for printing. Items shamelessly taken from NumPy Args: precision: Number of digits of precision for floating point output (default = 4). threshold: Total number of array elements which trigger summarization rather than full `repr` (default = 1000). edgeitems: Number of array items in summary at beginning and end of each dimension (default = 3). linewidth: The number of characters per line for the purpose of inserting line breaks (default = 80). Thresholded matrices will ignore this parameter. profile: Sane defaults for pretty printing. Can override with any of the above options. (any one of `default`, `short`, `full`) sci_mode: Enable (True) or disable (False) scientific notation. If None (default) is specified, the value is defined by `torch._tensor_str._Formatter`. This value is automatically chosen by the framework. Example:: >>> torch.set_printoptions(precision=2) >>> torch.tensor([1.12345]) tensor([1.12]) >>> torch.set_printoptions(threshold=5) >>> torch.arange(10) tensor([0, 1, 2, ..., 7, 8, 9]) """ if profile is not None: if profile == "default": PRINT_OPTS.precision = 4 PRINT_OPTS.threshold = 1000 PRINT_OPTS.edgeitems = 3 PRINT_OPTS.linewidth = 80 elif profile == "short": PRINT_OPTS.precision = 2 PRINT_OPTS.threshold = 1000 PRINT_OPTS.edgeitems = 2 PRINT_OPTS.linewidth = 80 elif profile == "full": PRINT_OPTS.precision = 4 PRINT_OPTS.threshold = inf PRINT_OPTS.edgeitems = 3 PRINT_OPTS.linewidth = 80 if precision is not None: PRINT_OPTS.precision = precision if threshold is not None: PRINT_OPTS.threshold = threshold if edgeitems is not None: PRINT_OPTS.edgeitems = edgeitems if linewidth is not None: PRINT_OPTS.linewidth = linewidth PRINT_OPTS.sci_mode = sci_mode def tensor_totype(t): dtype = torch.float if t.is_mps else torch.double return t.to(dtype=dtype) class _Formatter(object): def __init__(self, tensor): self.floating_dtype = tensor.dtype.is_floating_point self.int_mode = True self.sci_mode = False self.max_width = 1 with torch.no_grad(): tensor_view = tensor.reshape(-1) if not self.floating_dtype: for value in tensor_view: value_str = '{}'.format(value) self.max_width = max(self.max_width, len(value_str)) else: nonzero_finite_vals = torch.masked_select(tensor_view, torch.isfinite(tensor_view) & tensor_view.ne(0)) if nonzero_finite_vals.numel() == 0: # no valid number, do nothing return # Convert to double for easy calculation. HalfTensor overflows with 1e8, and there's no div() on CPU. nonzero_finite_abs = tensor_totype(nonzero_finite_vals.abs()) nonzero_finite_min = tensor_totype(nonzero_finite_abs.min()) nonzero_finite_max = tensor_totype(nonzero_finite_abs.max()) for value in nonzero_finite_vals: if value != torch.ceil(value): self.int_mode = False break if self.int_mode: # in int_mode for floats, all numbers are integers, and we append a decimal to nonfinites # to indicate that the tensor is of floating type. add 1 to the len to account for this. if nonzero_finite_max / nonzero_finite_min > 1000. or nonzero_finite_max > 1.e8: self.sci_mode = True for value in nonzero_finite_vals: value_str = ('{{:.{}e}}').format(PRINT_OPTS.precision).format(value) self.max_width = max(self.max_width, len(value_str)) else: for value in nonzero_finite_vals: value_str = ('{:.0f}').format(value) self.max_width = max(self.max_width, len(value_str) + 1) else: # Check if scientific representation should be used. if nonzero_finite_max / nonzero_finite_min > 1000.\ or nonzero_finite_max > 1.e8\ or nonzero_finite_min < 1.e-4: self.sci_mode = True for value in nonzero_finite_vals: value_str = ('{{:.{}e}}').format(PRINT_OPTS.precision).format(value) self.max_width = max(self.max_width, len(value_str)) else: for value in nonzero_finite_vals: value_str = ('{{:.{}f}}').format(PRINT_OPTS.precision).format(value) self.max_width = max(self.max_width, len(value_str)) if PRINT_OPTS.sci_mode is not None: self.sci_mode = PRINT_OPTS.sci_mode def width(self): return self.max_width def format(self, value): if self.floating_dtype: if self.sci_mode: ret = ('{{:{}.{}e}}').format(self.max_width, PRINT_OPTS.precision).format(value) elif self.int_mode: ret = '{:.0f}'.format(value) if not (math.isinf(value) or math.isnan(value)): ret += '.' else: ret = ('{{:.{}f}}').format(PRINT_OPTS.precision).format(value) else: ret = '{}'.format(value) return (self.max_width - len(ret)) * ' ' + ret def _scalar_str(self, formatter1, formatter2=None): if formatter2 is not None: real_str = _scalar_str(self.real, formatter1) imag_str = (_scalar_str(self.imag, formatter2) + "j").lstrip() # handles negative numbers, +0.0, -0.0 if imag_str[0] == '+' or imag_str[0] == '-': return real_str + imag_str else: return real_str + "+" + imag_str else: return formatter1.format(self.item()) def _vector_str(self, indent, summarize, formatter1, formatter2=None): # length includes spaces and comma between elements element_length = formatter1.width() + 2 if formatter2 is not None: # width for imag_formatter + an extra j for complex element_length += formatter2.width() + 1 elements_per_line = max(1, int(math.floor((PRINT_OPTS.linewidth - indent) / (element_length)))) char_per_line = element_length * elements_per_line def _val_formatter(val, formatter1=formatter1, formatter2=formatter2): if formatter2 is not None: real_str = formatter1.format(val.real) imag_str = (formatter2.format(val.imag) + "j").lstrip() # handles negative numbers, +0.0, -0.0 if imag_str[0] == '+' or imag_str[0] == '-': return real_str + imag_str else: return real_str + "+" + imag_str else: return formatter1.format(val) if summarize and self.size(0) > 2 * PRINT_OPTS.edgeitems: data = ([_val_formatter(val) for val in self[:PRINT_OPTS.edgeitems].tolist()] + [' ...'] + [_val_formatter(val) for val in self[-PRINT_OPTS.edgeitems:].tolist()]) else: data = [_val_formatter(val) for val in self.tolist()] data_lines = [data[i:i + elements_per_line] for i in range(0, len(data), elements_per_line)] lines = [', '.join(line) for line in data_lines] return '[' + (',' + '\n' + ' ' * (indent + 1)).join(lines) + ']' # formatter2 is only used for printing complex tensors. # For complex tensors, formatter1 and formatter2 are the formatters for tensor.real # and tensor.imag respesectively def _tensor_str_with_formatter(self, indent, summarize, formatter1, formatter2=None): dim = self.dim() if dim == 0: return _scalar_str(self, formatter1, formatter2) if dim == 1: return _vector_str(self, indent, summarize, formatter1, formatter2) if summarize and self.size(0) > 2 * PRINT_OPTS.edgeitems: slices = ([_tensor_str_with_formatter(self[i], indent + 1, summarize, formatter1, formatter2) for i in range(0, PRINT_OPTS.edgeitems)] + ['...'] + [_tensor_str_with_formatter(self[i], indent + 1, summarize, formatter1, formatter2) for i in range(len(self) - PRINT_OPTS.edgeitems, len(self))]) else: slices = [_tensor_str_with_formatter(self[i], indent + 1, summarize, formatter1, formatter2) for i in range(0, self.size(0))] tensor_str = (',' + '\n' * (dim - 1) + ' ' * (indent + 1)).join(slices) return '[' + tensor_str + ']' def _tensor_str(self, indent): if self.numel() == 0: return '[]' if self.has_names(): # There are two main codepaths (possibly more) that tensor printing goes through: # - tensor data can fit comfortably on screen # - tensor data needs to be summarized # Some of the codepaths don't fully support named tensors, so we send in # an unnamed tensor to the formatting code as a workaround. self = self.rename(None) summarize = self.numel() > PRINT_OPTS.threshold if self._is_zerotensor(): self = self.clone() # handle the negative bit if self.is_neg(): self = self.resolve_neg() if self.dtype is torch.float16 or self.dtype is torch.bfloat16: self = self.float() if self.dtype is torch.complex32: self = self.cfloat() if self.dtype.is_complex: # handle the conjugate bit self = self.resolve_conj() real_formatter = _Formatter(get_summarized_data(self.real) if summarize else self.real) imag_formatter = _Formatter(get_summarized_data(self.imag) if summarize else self.imag) return _tensor_str_with_formatter(self, indent, summarize, real_formatter, imag_formatter) else: formatter = _Formatter(get_summarized_data(self) if summarize else self) return _tensor_str_with_formatter(self, indent, summarize, formatter) def _add_suffixes(tensor_str, suffixes, indent, force_newline): tensor_strs = [tensor_str] last_line_len = len(tensor_str) - tensor_str.rfind('\n') + 1 for suffix in suffixes: suffix_len = len(suffix) if force_newline or last_line_len + suffix_len + 2 > PRINT_OPTS.linewidth: tensor_strs.append(',\n' + ' ' * indent + suffix) last_line_len = indent + suffix_len force_newline = False else: tensor_strs.append(', ' + suffix) last_line_len += suffix_len + 2 tensor_strs.append(')') return ''.join(tensor_strs) def get_summarized_data(self): dim = self.dim() if dim == 0: return self if dim == 1: if self.size(0) > 2 * PRINT_OPTS.edgeitems: return torch.cat((self[:PRINT_OPTS.edgeitems], self[-PRINT_OPTS.edgeitems:])) else: return self if self.size(0) > 2 * PRINT_OPTS.edgeitems: start = [self[i] for i in range(0, PRINT_OPTS.edgeitems)] end = ([self[i] for i in range(len(self) - PRINT_OPTS.edgeitems, len(self))]) return torch.stack([get_summarized_data(x) for x in (start + end)]) else: return torch.stack([get_summarized_data(x) for x in self]) def _str_intern(inp, *, tensor_contents=None): is_plain_tensor = type(inp) is torch.Tensor or type(inp) is torch.nn.Parameter if inp.is_nested: prefix = "nested_tensor(" elif is_plain_tensor: prefix = 'tensor(' else: prefix = f"{type(inp).__name__}(" indent = len(prefix) suffixes = [] custom_contents_provided = tensor_contents is not None if custom_contents_provided: tensor_str = tensor_contents # This is used to extract the primal value and thus disable the forward AD # within this function. # TODO(albanD) This needs to be updated when more than one level is supported self, tangent = torch.autograd.forward_ad.unpack_dual(inp) # Note [Print tensor device]: # A general logic here is we only print device when it doesn't match # the device specified in default tensor type. # Currently torch.set_default_tensor_type() only supports CPU/CUDA, thus # torch._C._get_default_device() only returns either cpu or cuda. # In other cases, we don't have a way to set them as default yet, # and we should always print out device for them. if self.device.type != torch._C._get_default_device()\ or (self.device.type == 'cuda' and torch.cuda.current_device() != self.device.index)\ or (self.device.type == 'mps'): suffixes.append('device=\'' + str(self.device) + '\'') # Tensor printing performs tensor operations like slice, indexing, etc to make it in a # representable format. These operations on xla/lazy tensor results in compilations. Hence, # to avoid compilations, copying the tensor to cpu before printing. if self.device.type == 'xla' or self.device.type == 'lazy': self = self.to('cpu') # TODO: add an API to map real -> complex dtypes _default_complex_dtype = torch.cdouble if torch.get_default_dtype() == torch.double else torch.cfloat has_default_dtype = self.dtype in (torch.get_default_dtype(), _default_complex_dtype, torch.int64, torch.bool) if self.is_sparse: suffixes.append('size=' + str(tuple(self.shape))) suffixes.append('nnz=' + str(self._nnz())) if not has_default_dtype: suffixes.append('dtype=' + str(self.dtype)) if not custom_contents_provided: indices_prefix = 'indices=tensor(' indices = self._indices().detach() indices_str = _tensor_str(indices, indent + len(indices_prefix)) if indices.numel() == 0: indices_str += ', size=' + str(tuple(indices.shape)) values_prefix = 'values=tensor(' values = self._values().detach() values_str = _tensor_str(values, indent + len(values_prefix)) if values.numel() == 0: values_str += ', size=' + str(tuple(values.shape)) tensor_str = indices_prefix + indices_str + '),\n' + ' ' * indent + values_prefix + values_str + ')' elif self.layout in {torch.sparse_csr, torch.sparse_csc, torch.sparse_bsr, torch.sparse_bsc}: suffixes.append('size=' + str(tuple(self.shape))) suffixes.append('nnz=' + str(self._nnz())) if not has_default_dtype: suffixes.append('dtype=' + str(self.dtype)) if not custom_contents_provided: compressed_indices_method, plain_indices_method = { torch.sparse_csr: (torch.Tensor.crow_indices, torch.Tensor.col_indices), torch.sparse_csc: (torch.Tensor.ccol_indices, torch.Tensor.row_indices), torch.sparse_bsr: (torch.Tensor.crow_indices, torch.Tensor.col_indices), torch.sparse_bsc: (torch.Tensor.ccol_indices, torch.Tensor.row_indices), }[self.layout] if self.layout in {torch.sparse_csr, torch.sparse_bsr}: cdimname, pdimname = 'row', 'column' else: cdimname, pdimname = 'column', 'row' compressed_indices_prefix = f'c{cdimname[:3]}_indices=tensor(' compressed_indices = compressed_indices_method(self).detach() compressed_indices_str = _tensor_str(compressed_indices, indent + len(compressed_indices_prefix)) if compressed_indices.numel() == 0: compressed_indices_str += ', size=' + str(tuple(compressed_indices.shape)) plain_indices_prefix = f'{pdimname[:3]}_indices=tensor(' plain_indices = plain_indices_method(self).detach() plain_indices_str = _tensor_str(plain_indices, indent + len(plain_indices_prefix)) if plain_indices.numel() == 0: plain_indices_str += ', size=' + str(tuple(plain_indices.shape)) values_prefix = 'values=tensor(' values = self.values().detach() values_str = _tensor_str(values, indent + len(values_prefix)) if values.numel() == 0: values_str += ', size=' + str(tuple(values.shape)) tensor_str = compressed_indices_prefix + compressed_indices_str + '),\n' + ' ' * indent +\ plain_indices_prefix + plain_indices_str + '),\n' + ' ' * indent +\ values_prefix + values_str + ')' elif self.is_quantized: suffixes.append('size=' + str(tuple(self.shape))) if not has_default_dtype: suffixes.append('dtype=' + str(self.dtype)) suffixes.append('quantization_scheme=' + str(self.qscheme())) if self.qscheme() == torch.per_tensor_affine or self.qscheme() == torch.per_tensor_symmetric: suffixes.append('scale=' + str(self.q_scale())) suffixes.append('zero_point=' + str(self.q_zero_point())) elif self.qscheme() == torch.per_channel_affine or self.qscheme() == torch.per_channel_symmetric \ or self.qscheme() == torch.per_channel_affine_float_qparams: suffixes.append('scale=' + str(self.q_per_channel_scales())) suffixes.append('zero_point=' + str(self.q_per_channel_zero_points())) suffixes.append('axis=' + str(self.q_per_channel_axis())) if not custom_contents_provided: tensor_str = _tensor_str(self.dequantize(), indent) elif self.is_nested: if not custom_contents_provided: def indented_str(s, indent): return "\n".join(f" {line}" for line in s.split("\n")) strs = ",\n".join(indented_str(str(t), indent + 1) for t in torch.ops.aten.unbind.int(self, 0)) tensor_str = f"[\n{strs}\n]" else: if self.is_meta: suffixes.append('size=' + str(tuple(self.shape))) if self.dtype != torch.get_default_dtype(): suffixes.append('dtype=' + str(self.dtype)) # TODO: This implies that ellipses is valid syntax for allocating # a meta tensor, which it could be, but it isn't right now if not custom_contents_provided: tensor_str = '...' else: if self.numel() == 0 and not self.is_sparse: # Explicitly print the shape if it is not (0,), to match NumPy behavior if self.dim() != 1: suffixes.append('size=' + str(tuple(self.shape))) # In an empty tensor, there are no elements to infer if the dtype # should be int64, so it must be shown explicitly. if self.dtype != torch.get_default_dtype(): suffixes.append('dtype=' + str(self.dtype)) if not custom_contents_provided: tensor_str = '[]' else: if not has_default_dtype: suffixes.append('dtype=' + str(self.dtype)) if not custom_contents_provided: if self.layout != torch.strided: tensor_str = _tensor_str(self.to_dense(), indent) else: tensor_str = _tensor_str(self, indent) if self.layout != torch.strided: suffixes.append('layout=' + str(self.layout)) # Use inp here to get the original grad_fn and not the one generated by the forward grad # unpacking. if inp.grad_fn is not None: name = type(inp.grad_fn).__name__ if name == 'CppFunction': name = inp.grad_fn.name().rsplit('::', 1)[-1] suffixes.append('grad_fn=<{}>'.format(name)) elif inp.requires_grad: suffixes.append('requires_grad=True') if self.has_names(): suffixes.append('names={}'.format(self.names)) if tangent is not None: suffixes.append('tangent={}'.format(tangent)) string_repr = _add_suffixes(prefix + tensor_str, suffixes, indent, force_newline=self.is_sparse) # Check if this instance is flagged as a parameter and change the repr accordingly. # Unfortunately, this function has to be aware of this detail. # NB: This is currently skipped for plain tensor parameters to maintain BC. In the future, # this should be done for those as well to produce a valid repr. if isinstance(self, torch.nn.Parameter) and not is_plain_tensor: string_repr = f"Parameter({string_repr})" return string_repr def _str(self, *, tensor_contents=None): with torch.no_grad(): return _str_intern(self, tensor_contents=tensor_contents)