Multi-process pipe (#90)

Adds support for distributing pipeline stages across multiple processes (and therefore multiple machines)
* Adds a style argument to the Pipe constructor, defaulting to PipelineStyle.SingleProcess, but also supporting PipelineStyle.MultiProcess
* Added support for lazy construction of modules (see lazy_construction for an example)
* Added two implementations of inter-process communication: one based on rpc with globally visible queues, one based on send/recv
* Copied all the relevant tests from tests/pipe to tests/pipe_process and modified them to exercise PipelineStyle.MultiProcess

fairscale/nn/pipe/worker.py

@@ -61,7 +61,11 @@ class Task:
     """
 
     def __init__(
-        self, stream: AbstractStream, *, compute: Callable[[], Batch], finalize: Optional[Callable[[Batch], None]],
+        self,
+        stream: Optional[AbstractStream],
+        *,
+        compute: Callable[[], Batch],
+        finalize: Optional[Callable[[Batch], None]],
     ) -> None:
         self.stream = stream
         self._compute = compute

run_mpi_tests.sh

+#!/bin/bash
+
+set -e
+for WORKERS in {1..5}; do
+    mpirun -n $WORKERS python -m pytest tests/nn/pipe_process
+done

stubs/torch/__init__.pyi

@@ -34,7 +34,8 @@ from . import distributed
 from . import version
 #END
 
-class dtype: ...
+class dtype:
+    is_floating_point: bool
 
 class layout: ...
 
@@ -325,7 +326,7 @@ class Tensor:
     def cosh_(self) -> Tensor: ...
     def cpu(self) -> Tensor: ...
     def cross(self, other: Tensor, dim: Optional[_int]=None) -> Tensor: ...
-    def cuda(self, device: Optional[_device]=None, non_blocking: _bool=False) -> Tensor: ...
+    def cuda(self, device: Union[_device, _int, str, None]=None, non_blocking: _bool=False) -> Tensor: ...
     @overload
     def cumprod(self, dim: _int, *, dtype: Optional[_dtype]=None) -> Tensor: ...
     @overload
@@ -611,16 +612,16 @@ class Tensor:
     def neg_(self) -> Tensor: ...
     def nelement(self) -> _int: ...
     @overload
-    def new_empty(self, size: _size, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+    def new_empty(self, size: _size, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
     @overload
-    def new_empty(self, *size: _int, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
-    def new_full(self, size: _size, fill_value: Number, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
-    def new_ones(self, size: _size, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
-    def new_tensor(self, data: Any, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
+    def new_empty(self, *size: _int, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+    def new_full(self, size: _size, fill_value: Number, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+    def new_ones(self, size: _size, dtype: Optional[_dtype]=None, device: Union[_device, _int, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
+    def new_tensor(self, data: Any, dtype: Optional[_dtype]=None, device: Union[_device, _int, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
     @overload
-    def new_zeros(self, size: _size, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+    def new_zeros(self, size: _size, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
     @overload
-    def new_zeros(self, *size: _int, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+    def new_zeros(self, *size: _int, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
     def normal_(self, mean: _float=0, std: _float=1, *, generator: Generator=None) -> Tensor: ...
     def numel(self) -> _int: ...
     def numpy(self) -> Any: ...
@@ -814,7 +815,7 @@ class Tensor:
     @overload
     def to(self, dtype: _dtype, non_blocking: _bool=False, copy: _bool=False) -> Tensor: ...
     @overload
-    def to(self, device: Optional[Union[_device, str]]=None, dtype: Optional[_dtype]=None, non_blocking: _bool=False, copy: _bool=False) -> Tensor: ...
+    def to(self, device: Union[_device, _int, str, None]=None, dtype: Optional[_dtype]=None, non_blocking: _bool=False, copy: _bool=False) -> Tensor: ...
     @overload
     def to(self, other: Tensor, non_blocking: _bool=False, copy: _bool=False) -> Tensor: ...
     def to_dense(self) -> Tensor: ...
@@ -962,7 +963,7 @@ def _convolution_nogroup(input: Tensor, weight: Tensor, bias: Optional[Tensor],
 def _copy_from(self: Tensor, dst: Tensor, non_blocking: _bool=False) -> Tensor: ...
 def _ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: _size, target_lengths: _size, blank: _int=0, zero_infinity: _bool=False) -> Tuple[Tensor, Tensor]: ...
 def _cudnn_ctc_loss(log_probs: Tensor, targets: Tensor, input_lengths: _size, target_lengths: _size, blank: _int, deterministic: _bool, zero_infinity: _bool) -> Tuple[Tensor, Tensor]: ...
-def _cudnn_init_dropout_state(dropout: _float, train: _bool, dropout_seed: _int, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def _cudnn_init_dropout_state(dropout: _float, train: _bool, dropout_seed: _int, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 def _cudnn_rnn(input: Tensor, weight: Union[Tuple[Tensor, ...], List[Tensor]], weight_stride0: _int, weight_buf: Optional[Tensor], hx: Tensor, cx: Optional[Tensor], mode: _int, hidden_size: _int, num_layers: _int, batch_first: _bool, dropout: _float, train: _bool, bidirectional: _bool, batch_sizes: _size, dropout_state: Optional[Tensor]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: ...
 def _cudnn_rnn_flatten_weight(weight_arr: Union[Tuple[Tensor, ...], List[Tensor]], weight_stride0: _int, input_size: _int, mode: _int, hidden_size: _int, num_layers: _int, batch_first: _bool, bidirectional: _bool) -> Tensor: ...
 def _cufft_clear_plan_cache(device_index: _int) -> None: ...
@@ -974,13 +975,13 @@ def _dim_arange(like: Tensor, dim: _int) -> Tensor: ...
 def _dirichlet_grad(x: Tensor, alpha: Tensor, total: Tensor) -> Tensor: ...
 def _embedding_bag(weight: Tensor, indices: Tensor, offsets: Tensor, scale_grad_by_freq: _bool=False, mode: _int=0, sparse: _bool=False, per_sample_weights: Optional[Tensor]=None) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ...
 @overload
-def _empty_affine_quantized(size: _size, *, scale: _float=1, zero_point: _int=0, memory_format: Optional[memory_format]=contiguous_format, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def _empty_affine_quantized(size: _size, *, scale: _float=1, zero_point: _int=0, memory_format: Optional[memory_format]=contiguous_format, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def _empty_affine_quantized(*size: _int, scale: _float=1, zero_point: _int=0, memory_format: Optional[memory_format]=contiguous_format, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def _empty_affine_quantized(*size: _int, scale: _float=1, zero_point: _int=0, memory_format: Optional[memory_format]=contiguous_format, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def _empty_per_channel_affine_quantized(size: _size, *, scales: Tensor, zero_points: Tensor, axis: _int, memory_format: Optional[memory_format]=contiguous_format, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def _empty_per_channel_affine_quantized(size: _size, *, scales: Tensor, zero_points: Tensor, axis: _int, memory_format: Optional[memory_format]=contiguous_format, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def _empty_per_channel_affine_quantized(*size: _int, scales: Tensor, zero_points: Tensor, axis: _int, memory_format: Optional[memory_format]=contiguous_format, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def _empty_per_channel_affine_quantized(*size: _int, scales: Tensor, zero_points: Tensor, axis: _int, memory_format: Optional[memory_format]=contiguous_format, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 def _fft_with_size(self: Tensor, signal_ndim: _int, complex_input: _bool, complex_output: _bool, inverse: _bool, checked_signal_sizes: _size, normalized: _bool, onesided: _bool, output_sizes: _size) -> Tensor: ...
 def _fused_dropout(self: Tensor, p: _float, generator: Generator=None) -> Tuple[Tensor, Tensor]: ...
 def _has_compatible_shallow_copy_type(self: Tensor, from_: Tensor) -> _bool: ...
@@ -1121,11 +1122,11 @@ def any(self: Tensor, dim: Union[str, None], keepdim: _bool=False, *, out: Optio
 @overload
 def any(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 @overload
-def arange(start: Number, end: Number, step: Number, *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
+def arange(start: Number, end: Number, step: Number, *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, device: Union[_device, _int, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
 @overload
-def arange(start: Number, end: Number, *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
+def arange(start: Number, end: Number, *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, device: Union[_device, _int, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
 @overload
-def arange(end: Number, *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
+def arange(end: Number, *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, device: Union[_device, _int, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
 def argmax(self: Tensor, dim: Optional[_int]=None, keepdim: _bool=False) -> Tensor: ...
 def argmin(self: Tensor, dim: Optional[_int]=None, keepdim: _bool=False) -> Tensor: ...
 @overload
@@ -1152,9 +1153,9 @@ def baddbmm(beta: Number, self: Tensor, batch1: Tensor, batch2: Tensor) -> Tenso
 @overload
 def baddbmm(beta: Number, self: Tensor, batch1: Tensor, batch2: Tensor, *, out: Tensor) -> Tensor: ...
 @overload
-def bartlett_window(window_length: _int, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def bartlett_window(window_length: _int, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def bartlett_window(window_length: _int, periodic: _bool, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def bartlett_window(window_length: _int, periodic: _bool, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 def batch_norm(input: Tensor, weight: Optional[Tensor], bias: Optional[Tensor], running_mean: Optional[Tensor], running_var: Optional[Tensor], training: _bool, momentum: _float, eps: _float, cudnn_enabled: _bool) -> Tensor: ...
 def batch_norm_backward_elemt(grad_out: Tensor, input: Tensor, mean: Tensor, invstd: Tensor, weight: Optional[Tensor], mean_dy: Tensor, mean_dy_xmu: Tensor) -> Tensor: ...
 def batch_norm_backward_reduce(grad_out: Tensor, input: Tensor, mean: Tensor, invstd: Tensor, weight: Optional[Tensor], input_g: _bool, weight_g: _bool, bias_g: _bool) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ...
@@ -1175,9 +1176,9 @@ def bitwise_xor(self: Tensor, other: Number, *, out: Optional[Tensor]=None) -> T
 @overload
 def bitwise_xor(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 @overload
-def blackman_window(window_length: _int, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def blackman_window(window_length: _int, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def blackman_window(window_length: _int, periodic: _bool, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def blackman_window(window_length: _int, periodic: _bool, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 def bmm(self: Tensor, mat2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 def can_cast(from_: _dtype, to: _dtype) -> _bool: ...
 @overload
@@ -1251,18 +1252,18 @@ def embedding(weight: Tensor, indices: Tensor, padding_idx: _int=-1, scale_grad_
 def embedding_bag(weight: Tensor, indices: Tensor, offsets: Tensor, scale_grad_by_freq: _bool=False, mode: _int=0, sparse: _bool=False, per_sample_weights: Optional[Tensor]=None) -> Tuple[Tensor, Tensor, Tensor, Tensor]: ...
 def embedding_renorm_(self: Tensor, indices: Tensor, max_norm: _float, norm_type: _float) -> Tensor: ...
 @overload
-def empty(size: _size, *, names: Optional[List[Union[str, None]]], memory_format: Optional[memory_format]=None, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def empty(size: _size, *, names: Optional[List[Union[str, None]]], memory_format: Optional[memory_format]=None, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int , str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def empty(*size: _int, names: Optional[List[Union[str, None]]], memory_format: Optional[memory_format]=None, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def empty(*size: _int, names: Optional[List[Union[str, None]]], memory_format: Optional[memory_format]=None, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def empty(size: _size, *, memory_format: Optional[memory_format]=None, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def empty(size: _size, *, memory_format: Optional[memory_format]=None, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def empty(*size: _int, memory_format: Optional[memory_format]=None, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def empty(*size: _int, memory_format: Optional[memory_format]=None, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
 def empty_like(self: Tensor, *, memory_format: Optional[memory_format]=None) -> Tensor: ...
 @overload
-def empty_like(self: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
-def empty_strided(size: _size, stride: _size, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def empty_like(self: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def empty_strided(size: _size, stride: _size, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
 def eq(self: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
 @overload
@@ -1278,9 +1279,9 @@ def exp_(self: Tensor) -> Tensor: ...
 def expm1(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 def expm1_(self: Tensor) -> Tensor: ...
 @overload
-def eye(n: _int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def eye(n: _int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def eye(n: _int, m: _int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def eye(n: _int, m: _int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 def fake_quantize_per_channel_affine(self: Tensor, scale: Tensor, zero_point: Tensor, axis: _int, quant_min: _int, quant_max: _int) -> Tensor: ...
 def fake_quantize_per_tensor_affine(self: Tensor, scale: _float, zero_point: _int, quant_min: _int, quant_max: _int) -> Tensor: ...
 def fbgemm_linear_fp16_weight(input: Tensor, packed_weight: Tensor, bias: Tensor) -> Tensor: ...
@@ -1323,16 +1324,16 @@ def frac_(self: Tensor) -> Tensor: ...
 def frobenius_norm(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 @overload
 def frobenius_norm(self: Tensor, dim: Union[_int, _size], keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
-def from_file(filename: str, shared: Optional[_bool]=None, size: Optional[_int]=0, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def from_file(filename: str, shared: Optional[_bool]=None, size: Optional[_int]=0, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 def from_numpy(ndarray) -> Tensor: ...
 @overload
-def full(size: _size, fill_value: Number, *, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def full(size: _size, fill_value: Number, *, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def full(size: _size, fill_value: Number, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def full(size: _size, fill_value: Number, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
 def full_like(self: Tensor, fill_value: Number, *, memory_format: Optional[memory_format]=None) -> Tensor: ...
 @overload
-def full_like(self: Tensor, fill_value: Number, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def full_like(self: Tensor, fill_value: Number, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
 def gather(self: Tensor, dim: _int, index: Tensor, *, sparse_grad: _bool=False, out: Optional[Tensor]=None) -> Tensor: ...
 @overload
@@ -1360,17 +1361,17 @@ def gt(self: Tensor, other: Number, *, out: Optional[Tensor]=None) -> Tensor: ..
 @overload
 def gt(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 @overload
-def hamming_window(window_length: _int, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def hamming_window(window_length: _int, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def hamming_window(window_length: _int, periodic: _bool, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def hamming_window(window_length: _int, periodic: _bool, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def hamming_window(window_length: _int, periodic: _bool, alpha: _float, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def hamming_window(window_length: _int, periodic: _bool, alpha: _float, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def hamming_window(window_length: _int, periodic: _bool, alpha: _float, beta: _float, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def hamming_window(window_length: _int, periodic: _bool, alpha: _float, beta: _float, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def hann_window(window_length: _int, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def hann_window(window_length: _int, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def hann_window(window_length: _int, periodic: _bool, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def hann_window(window_length: _int, periodic: _bool, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 def hardshrink(self: Tensor, lambd: Number=0.5) -> Tensor: ...
 def histc(self: Tensor, bins: _int=100, min: Number=0, max: Number=0, *, out: Optional[Tensor]=None) -> Tensor: ...
 def hspmm(mat1: Tensor, mat2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
@@ -1425,7 +1426,7 @@ def lerp(self: Tensor, end: Tensor, weight: Number, *, out: Optional[Tensor]=Non
 @overload
 def lerp(self: Tensor, end: Tensor, weight: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 def lgamma(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
-def linspace(start: Number, end: Number, steps: _int=100, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def linspace(start: Number, end: Number, steps: _int=100, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 def log(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 def log10(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 def log10_(self: Tensor) -> Tensor: ...
@@ -1441,7 +1442,7 @@ def log_softmax(self: Tensor, dim: Union[str, None], *, dtype: Optional[_dtype]=
 def logdet(self: Tensor) -> Tensor: ...
 def logical_not(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 def logical_xor(self: Tensor, other: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
-def logspace(start: Number, end: Number, steps: _int=100, base: _float=10.0, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def logspace(start: Number, end: Number, steps: _int=100, base: _float=10.0, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
 def logsumexp(self: Tensor, dim: Union[_int, _size], keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
 @overload
@@ -1540,24 +1541,24 @@ def normal(mean: _float, std: Tensor, *, generator: Generator=None, out: Optiona
 @overload
 def normal(mean: Tensor, std: Tensor, *, generator: Generator=None, out: Optional[Tensor]=None) -> Tensor: ...
 @overload
-def normal(mean: _float, std: _float, size: _size, *, generator: Generator=None, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def normal(mean: _float, std: _float, size: _size, *, generator: Generator=None, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
 def nuclear_norm(self: Tensor, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
 @overload
 def nuclear_norm(self: Tensor, dim: Union[_int, _size], keepdim: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
 def numel(self: Tensor) -> _int: ...
 @overload
-def ones(size: _size, *, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def ones(size: _size, *, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def ones(*size: _int, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def ones(*size: _int, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def ones(size: _size, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def ones(size: _size, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def ones(*size: _int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def ones(*size: _int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
 def ones_like(self: Tensor, *, memory_format: Optional[memory_format]=None) -> Tensor: ...
 @overload
-def ones_like(self: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def ones_like(self: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 def orgqr(self: Tensor, input2: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 def ormqr(self: Tensor, input2: Tensor, input3: Tensor, left: _bool=True, transpose: _bool=False, *, out: Optional[Tensor]=None) -> Tensor: ...
 def pairwise_distance(x1: Tensor, x2: Tensor, p: _float=2, eps: _float=1e-06, keepdim: _bool=False) -> Tensor: ...
@@ -1603,62 +1604,62 @@ def quantized_max_pool2d(self: Tensor, kernel_size: Union[_int, _size], stride:
 def quantized_rnn_relu_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Number, scale_hh: Number, zero_point_ih: Number, zero_point_hh: Number) -> Tensor: ...
 def quantized_rnn_tanh_cell(input: Tensor, hx: Tensor, w_ih: Tensor, w_hh: Tensor, b_ih: Tensor, b_hh: Tensor, packed_ih: Tensor, packed_hh: Tensor, col_offsets_ih: Tensor, col_offsets_hh: Tensor, scale_ih: Number, scale_hh: Number, zero_point_ih: Number, zero_point_hh: Number) -> Tensor: ...
 @overload
-def rand(size: _size, *, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def rand(size: _size, *, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def rand(*size: _int, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def rand(*size: _int, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def rand(size: _size, *, generator: Generator, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def rand(size: _size, *, generator: Generator, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def rand(*size: _int, generator: Generator, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def rand(*size: _int, generator: Generator, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def rand(size: _size, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def rand(size: _size, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def rand(*size: _int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def rand(*size: _int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def rand(size: _size, *, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def rand(size: _size, *, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def rand(*size: _int, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def rand(*size: _int, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
 def rand_like(self: Tensor, *, memory_format: Optional[memory_format]=None) -> Tensor: ...
 @overload
-def rand_like(self: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def rand_like(self: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def randint(low: _int, high: _int, size: _size, *, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
+def randint(low: _int, high: _int, size: _size, *, dtype: Optional[_dtype]=None, device: Union[_device, _int, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
 @overload
-def randint(high: _int, size: _size, *, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
+def randint(high: _int, size: _size, *, dtype: Optional[_dtype]=None, device: Union[_device, _int, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
 @overload
 def randint_like(self: Tensor, high: _int, *, memory_format: Optional[memory_format]=None) -> Tensor: ...
 @overload
 def randint_like(self: Tensor, low: _int, high: _int, *, memory_format: Optional[memory_format]=None) -> Tensor: ...
 @overload
-def randint_like(self: Tensor, high: _int, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def randint_like(self: Tensor, high: _int, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def randint_like(self: Tensor, low: _int, high: _int, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def randint_like(self: Tensor, low: _int, high: _int, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def randn(size: _size, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def randn(size: _size, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def randn(*size: _int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def randn(*size: _int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def randn(size: _size, *, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def randn(size: _size, *, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def randn(*size: _int, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def randn(*size: _int, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def randn(size: _size, *, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def randn(size: _size, *, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def randn(*size: _int, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def randn(*size: _int, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def randn(size: _size, *, generator: Generator, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def randn(size: _size, *, generator: Generator, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def randn(*size: _int, generator: Generator, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def randn(*size: _int, generator: Generator, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
 def randn_like(self: Tensor, *, memory_format: Optional[memory_format]=None) -> Tensor: ...
 @overload
-def randn_like(self: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def randn_like(self: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def randperm(n: _int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def randperm(n: _int, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def randperm(n: _int, *, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
-def range(start: Number, end: Number, step: Number=1, *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
+def randperm(n: _int, *, generator: Generator, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def range(start: Number, end: Number, step: Number=1, *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, device: Union[_device, _int, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
 def real(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 def reciprocal(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 def reciprocal_(self: Tensor) -> Tensor: ...
@@ -1708,7 +1709,7 @@ def rsqrt_(self: Tensor) -> Tensor: ...
 def rsub(self: Tensor, other: Tensor, *, alpha: Number=1) -> Tensor: ...
 @overload
 def rsub(self: Tensor, other: Number, alpha: Number=1) -> Tensor: ...
-def scalar_tensor(s: Number, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def scalar_tensor(s: Number, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
 def scatter(self: Tensor, dim: _int, index: Tensor, src: Tensor) -> Tensor: ...
 @overload
@@ -1748,7 +1749,7 @@ def solve(self: Tensor, A: Tensor, *, out: Optional[Tensor]=None) -> Tuple[Tenso
 def sort(self: Tensor, dim: _int=-1, descending: _bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
 @overload
 def sort(self: Tensor, dim: Union[str, None], descending: _bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
-def sparse_coo_tensor(indices: Tensor, values: Union[Tensor,List], size: Optional[_size]=None, *, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def sparse_coo_tensor(indices: Tensor, values: Union[Tensor,List], size: Optional[_size]=None, *, dtype: Optional[_dtype]=None, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 def split_with_sizes(self: Tensor, split_sizes: _size, dim: _int=0) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
 def sqrt(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 def sqrt_(self: Tensor) -> Tensor: ...
@@ -1799,7 +1800,7 @@ def tan(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 def tan_(self: Tensor) -> Tensor: ...
 def tanh(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 def tanh_(self: Tensor) -> Tensor: ...
-def tensor(data: Any, dtype: Optional[_dtype]=None, device: Union[_device, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
+def tensor(data: Any, dtype: Optional[_dtype]=None, device: Union[_device, _int, str, None]=None, requires_grad: _bool=False) -> Tensor: ...
 def threshold(self: Tensor, threshold: Number, value: Number, *, out: Optional[Tensor]=None) -> Tensor: ...
 def threshold_(self: Tensor, threshold: Number, value: Number) -> Tensor: ...
 def topk(self: Tensor, k: _int, dim: _int=-1, largest: _bool=True, sorted: _bool=True, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
@@ -1814,9 +1815,9 @@ def trapz(y: Tensor, x: Tensor, *, dim: _int=-1) -> Tensor: ...
 def trapz(y: Tensor, *, dx: _float=1, dim: _int=-1) -> Tensor: ...
 def triangular_solve(self: Tensor, A: Tensor, upper: _bool=True, transpose: _bool=False, unitriangular: _bool=False, *, out: Optional[Tensor]=None) -> Tuple[Tensor, Tensor]: ...
 def tril(self: Tensor, diagonal: _int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
-def tril_indices(row: _int, col: _int, offset: _int=0, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def tril_indices(row: _int, col: _int, offset: _int=0, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 def triu(self: Tensor, diagonal: _int=0, *, out: Optional[Tensor]=None) -> Tensor: ...
-def triu_indices(row: _int, col: _int, offset: _int=0, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def triu_indices(row: _int, col: _int, offset: _int=0, *, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 def trunc(self: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
 def trunc_(self: Tensor) -> Tensor: ...
 @overload
@@ -1843,17 +1844,17 @@ def where(condition: Tensor, self: Tensor, other: Tensor) -> Tensor: ...
 def where(condition: Tensor) -> Union[Tuple[Tensor, ...], List[Tensor]]: ...
 def zero_(self: Tensor) -> Tensor: ...
 @overload
-def zeros(size: _size, *, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def zeros(size: _size, *, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def zeros(*size: _int, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def zeros(*size: _int, names: Optional[List[Union[str, None]]], out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def zeros(size: _size, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def zeros(size: _size, *, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
-def zeros(*size: _int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def zeros(*size: _int, out: Optional[Tensor]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 @overload
 def zeros_like(self: Tensor, *, memory_format: Optional[memory_format]=None) -> Tensor: ...
 @overload
-def zeros_like(self: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
+def zeros_like(self: Tensor, *, memory_format: Optional[memory_format]=None, dtype: _dtype=None, layout: _layout=strided, device: Union[_device, _int, str, None]=None, requires_grad:_bool=False) -> Tensor: ...
 
 class DoubleStorage(Storage): ...
 class FloatStorage(Storage): ...

stubs/torch/cuda/__init__.pyi

@@ -69,8 +69,8 @@ class stream:
     def __enter__(self) -> None: ...
     def __exit__(self, *args: Any) -> None: ...
 
-def current_stream(device: Optional[_device_t]) -> Stream: ...
-def default_stream(device: Optional[_device_t]) -> Stream: ...
+def current_stream(device: Optional[_device_t] = None) -> Stream: ...
+def default_stream(device: Optional[_device_t] = None) -> Stream: ...
 #END
 #
 default_generators: Tuple[Any]

stubs/torch/distributed/__init__.pyi

@@ -4,8 +4,13 @@ from typing import Any, List, Union, Optional
 from torch import Tensor
 import datetime
 
+from . import rpc as rpc
+
 class Backend: ...
-class ProcessGroup: ...
+
+class ProcessGroup:
+    def size(self) -> int: ...
+    def rank(self) -> int: ...
 
 class ReduceOp:
     SUM: ReduceOp
@@ -29,5 +34,12 @@ def new_group(ranks: List[int], timeout: datetime.timedelta = datetime.timedelta
 def all_reduce(tensor: Tensor, op: ReduceOp = ReduceOp.SUM, group:Optional[ProcessGroup] = None, async_op: bool = False): ...
 def all_gather(tensor_list: List[Tensor], tensor: Tensor, group:Optional[ProcessGroup] = None, async_op: bool = False): ...
 
+def send(tensor: Tensor, dst: int, group: Optional[ProcessGroup] = None, tag: Optional[int] = None) -> None: ...
+def isend(tensor: Tensor, dst: int, group: Optional[ProcessGroup] = None, tag: Optional[int] = None) -> None: ...
+def recv(tensor: Tensor, src: Optional[int] = None, group: Optional[ProcessGroup] = None, tag: Optional[int] = None) -> int: ...
+def irecv(tensor: Tensor, src: Optional[int] = None, group: Optional[ProcessGroup] = None, tag: Optional[int] = None) -> int: ...
+
 class group(object):
     WORLD: Any
+
+class RRef: ...

stubs/torch/distributed/rpc/__init__.pyi

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+from typing import Union, Callable, Optional
+
+
+class RRef:
+    ...
+
+
+class WorkerInfo:
+    ...
+
+
+def rpc_async(
+    to: Union[str, WorkerInfo],
+    func: Callable,
+    args: Optional[tuple] = None,
+    kwargs: Optional[dict] = None,
+    timeout=-1.0,
+) -> None:
+    ...
+
+
+def rpc_sync(
+    to: Union[str, WorkerInfo],
+    func: Callable,
+    args: Optional[tuple] = None,
+    kwargs: Optional[dict] = None,
+    timeout=-1.0,
+) -> None:
+    ...

stubs/torch/nn/modules/module.pyi

@@ -33,7 +33,7 @@ class Module(Generic[T_co]):
 
     def apply(self: T, fn: Callable[['Module'], None]) -> T: ...
 
-    def cuda(self: T, device: Optional[Union[int, device]] = ...) -> T: ...
+    def cuda(self: T, device: Optional[Union[int, str, device]] = ...) -> T: ...
 
     def cpu(self: T) -> T: ...
 

tests/nn/model_parallel/commons.py

@@ -19,14 +19,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import functools
+import inspect
 import os
 import random
 
 import numpy
+from packaging import version
+import pytest
 import torch
 import torch.distributed as dist
+from torch.distributed import rpc
 import torch.multiprocessing as mp
 
+from fairscale.nn.model_parallel import initialize_model_parallel
 from fairscale.nn.model_parallel.random import model_parallel_cuda_manual_seed
 
 
@@ -39,7 +45,7 @@ class IdentityLayer(torch.nn.Module):
         return self.weight
 
 
-def set_random_seed(seed):
+def set_random_seed(seed: int) -> None:
     """Set random seed for reproducability."""
     random.seed(seed)
     numpy.random.seed(seed)
@@ -47,11 +53,40 @@ def set_random_seed(seed):
     model_parallel_cuda_manual_seed(seed)
 
 
-def dist_init(rank, world_size):
-    os.environ["MASTER_ADDR"] = "localhost"
-    os.environ["MASTER_PORT"] = "29501"
-    dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
-    torch.cuda.set_device(rank)
+def dist_init(rank, world_size, hostname=None):
+    if hostname is None:
+        hostname = "localhost"
+    print(f"dist init r={rank}, world={world_size}, host={hostname}")
+    os.environ["MASTER_ADDR"] = hostname
+    os.environ["MASTER_PORT"] = "10638"
+    os.environ["WORLD_SIZE"] = str(world_size)
+    os.environ["RANK"] = str(rank)
+
+    if version.parse(torch.__version__).release >= (1, 6, 0):
+        init_method = f"tcp://{os.environ['MASTER_ADDR']}:{os.environ['MASTER_PORT']}"
+        torch.distributed.init_process_group(backend="nccl", rank=rank, world_size=world_size, init_method=init_method)
+        os.environ["MASTER_ADDR"] = hostname
+        os.environ["MASTER_PORT"] = "10639"
+        init_method = f"tcp://{os.environ['MASTER_ADDR']}:{os.environ['MASTER_PORT']}"
+        rpc.init_rpc(
+            f"Test{rank}",
+            rank=rank,
+            world_size=world_size,
+            backend=rpc.BackendType.TENSORPIPE,
+            rpc_backend_options=rpc.TensorPipeRpcBackendOptions(init_method=init_method),
+        )
+    else:
+        if world_size > 1:
+            rpc.init_rpc(f"Test{rank}", rank=rank, world_size=world_size)
+        else:
+            torch.distributed.init_process_group(backend="nccl", rank=rank, world_size=world_size)
+
+    if torch.cuda.is_available() and torch.cuda.device_count():
+        torch.cuda.set_device(rank % torch.cuda.device_count())
+
+
+def get_worker_map():
+    return {rank: f"Test{rank}" for rank in range(dist.get_world_size())}
 
 
 def get_world_sizes():
@@ -59,6 +94,54 @@ def get_world_sizes():
     return [x for x in [1, 2, 4, 8] if x <= limit]
 
 
-def spawn_for_all_world_sizes(test_func, world_sizes=get_world_sizes()):
+def spawn_for_all_world_sizes(test_func, world_sizes=get_world_sizes(), args=[]):
     for world_size in world_sizes:
-        mp.spawn(test_func, args=(world_size,), nprocs=world_size, join=True)
+        mp.spawn(test_func, args=(world_size, *args), nprocs=world_size, join=True)
+
+
+def helper(rank, world_size, func, args):
+    dist_init(rank, world_size)
+    initialize_model_parallel(1, world_size)
+    func(*args)
+
+
+def torch_spawn(world_sizes=None):
+    if world_sizes is None:
+        world_sizes = get_world_sizes()
+
+    def fixer(func):
+
+        name = func.__name__
+        parameters = inspect.signature(func).parameters
+
+        if name.startswith("test"):
+            raise ValueError(
+                f"Tests marked with @torch_spawn (i.e. '{name}') should not have names beginning in 'test' as they will"
+                " be picked up by pytest without running the spawn wrapper"
+            )
+
+        @functools.wraps(func)
+        def replacement(*args, **kwargs):
+            assert args == tuple()
+            args = tuple(
+                kwargs[p] for p in parameters if p != "rank"
+            )  # converting named parameters to positional parameters to pass to `spawn`
+
+            if "OMPI_COMM_WORLD_RANK" in os.environ:
+                torch.distributed.init_process_group("mpi")
+                world_size = torch.distributed.get_world_size()
+                initialize_model_parallel(1, world_size)
+                torch.cuda.set_device(torch.distributed.get_rank() % torch.cuda.device_count())
+                if world_size in world_sizes:
+                    func(*args)
+                else:
+                    pytest.skip(f"requested world size doesn't match current world size")
+            else:
+                spawn_for_all_world_sizes(helper, world_sizes, (func, args))
+
+        caller_module = inspect.getmodule(inspect.currentframe().f_back)
+        setattr(caller_module, f"test_{name}", replacement)
+
+        return func
+
+    return fixer

tests/nn/model_parallel/test_initialize.py

@@ -125,10 +125,11 @@ def test_adjacency(monkeypatch):
     for group in new_groups:
         buckets[len(group)].append(group)
 
-    assert sorted(list(buckets.keys())) == [model_parallel_size, data_parallel_size]
+    assert sorted(list(buckets.keys())) == [model_parallel_size, pipeline_length, data_parallel_size]
 
     assert len(buckets[model_parallel_size]) == pipeline_length * data_parallel_size
     assert len(buckets[data_parallel_size]) == model_parallel_size * pipeline_length
+    assert len(buckets[pipeline_length]) == model_parallel_size * data_parallel_size
 
     # Check that model_parallel groups are contiguous
     for group in buckets[model_parallel_size]:

tests/nn/model_parallel/test_layers.py

@@ -19,15 +19,25 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
+
+import pytest
 import torch
 from torch import nn
+from torch.distributed import rpc
 import torch.nn.init as init
 from torch.nn.parameter import Parameter
 
 from fairscale.nn.model_parallel import initialize as mpu
 from fairscale.nn.model_parallel import layers
 from fairscale.nn.pipe import Pipe
-from tests.nn.model_parallel.commons import dist_init, get_world_sizes, set_random_seed, spawn_for_all_world_sizes
+from tests.nn.model_parallel.commons import (
+    dist_init,
+    get_world_sizes,
+    set_random_seed,
+    spawn_for_all_world_sizes,
+    torch_spawn,
+)
 
 
 def run_test_parallel_embedding(rank, model_parallel_size):
@@ -297,33 +307,43 @@ def run_test_row_parallel_linear(rank, model_parallel_size):
         print(" >> passed the test :-)")
 
 
-def run_test_pipe(rank, model_parallel_size):
+def run_test_pipe(rank, world_size, skip_dist_init=False):
     pipe_world_size = 2
-    dist_init(rank, model_parallel_size)
 
-    mpu.initialize_model_parallel(model_parallel_size)
+    if world_size == 1:
+        return
+
+    if not skip_dist_init:
+        dist_init(rank, world_size)
+    else:
+        os.environ["MASTER_ADDR"] = "localhost"
+        os.environ["MASTER_PORT"] = "29502"
+        rpc.init_rpc(f"Test{rank}", rank=rank, world_size=world_size)
+
+    mpu.initialize_model_parallel(world_size / pipe_world_size, pipe_world_size)
+    model_parallel_size = mpu.get_model_parallel_world_size()
     if torch.distributed.get_rank() == 0:
         print(
             "> testing Sequential + Pipe with model parallel size: {}, pipe: {}".format(
                 model_parallel_size, pipe_world_size
             )
         )
-    model_parallel_size = mpu.get_model_parallel_world_size()
-    chunk_size = 8
+    chunk_size = 4
 
     seed = 12345
     set_random_seed(seed)
-    input_size_coeff = 13
+    input_size_coeff = 3
     input_size = input_size_coeff * model_parallel_size
-    output_size_coeff = 17
+    output_size_coeff = 7
     output_size = output_size_coeff * model_parallel_size
-    batch_size = 7 * chunk_size
+    batch_size = 3 * chunk_size
+
+    target = torch.rand((batch_size, input_size), requires_grad=True).cuda()
+    print(f"target = {target}")
 
     identity = IdentityLayer2D(batch_size, input_size).cuda()
 
     pipeline_devices = mpu.get_pipeline_parallel_group()
-    if pipe_world_size == 2 and len(pipeline_devices) == 1:
-        pipeline_devices.append(pipeline_devices[0] + model_parallel_size)
 
     set_random_seed(seed)
     model = nn.Sequential(
@@ -331,33 +351,196 @@ def run_test_pipe(rank, model_parallel_size):
         nn.ReLU(),
         layers.RowParallelLinear(output_size, input_size, keep_master_weight_for_test=True, bias=False).cuda(),
     )
-
     set_random_seed(seed)
-    reference = nn.Sequential(
+
+    reference = [
         nn.Linear(input_size, output_size, bias=False).cuda(),
         nn.ReLU(),
         nn.Linear(output_size, input_size, bias=False).cuda(),
-    )
+    ]
 
-    reference[0].weight.data = model[0].master_weight.cuda()
-    reference[-1].weight.data = model[-1].master_weight.cuda()
+    print(f"setup {reference[0].weight.size()}, {model[0].weight.size()}, {(input_size, output_size)}")
+    print(f"setup {reference[2].weight.size()}, {(output_size, input_size)}")
+
+    reference[0].weight = Parameter(model[0].get_master_weight().clone()).cuda()
+    reference[2].weight = Parameter(model[2].get_master_weight().clone()).cuda()
+
+    reference = nn.Sequential(*reference)
+
+    def grad_graph(depth, grad):
+        result = depth * " " + str(grad)
+        if grad:
+            for x in grad.next_functions:
+                result += "\n" + grad_graph(depth + 1, x[0])
+        return result
+
+    def check_weights(x, y, key: str, index=None):
+        for i in [2, 0]:
+            if index is not None and i != index:
+                continue
+            left = x[i].get_master_weight()
+            right = y[i].weight.data
+            if not torch.allclose(left, right, atol=1.0e-6) or index is not None:
+                print(f"check_weights {key}-{i}: left = {left}, \nright = {right}")
+            if not torch.equal(left, right):
+                print(f"check_weights NOT_EQUAL {key}-{i}: left = {left}, \nright = {right}")
+            assert torch.allclose(left, right, atol=1.0e-6)
+
+    def dump_opt_params(opt):
+        for i, group in enumerate(opt.param_groups):
+            for j, p in enumerate(group["params"]):
+                print(f"{torch.distributed.get_rank()}:param {(i,j)} = {p}")
+                print(f"{torch.distributed.get_rank()}:param.grad {(i,j)} = {p.grad}")
+
+    def forward_model(model_, target, step=False):
+        optimizer = torch.optim.SGD(model_.parameters(), lr=0.01, momentum=0.9)
+        optimizer.zero_grad()
+        model_.zero_grad()
+        output = model_(identity())
+        loss = nn.MSELoss()
+        model_.zero_grad()
+        if step:
+            loss(output, target).backward()
+            saved_weight_0 = model_[0].weight.data.clone()
+            saved_weight_2 = model_[2].weight.data.clone()
+            dump_opt_params(optimizer)
+            optimizer.step()
+            assert not torch.allclose(saved_weight_0, model_[0].weight.data, atol=1.0e-6)
+            assert not torch.allclose(saved_weight_2, model_[2].weight.data, atol=1.0e-6)
+        return output
+
+    output = forward_model(model, target)
+    reference_output = forward_model(reference, target)
 
-    loss_weight = torch.randn([batch_size, output_size]).cuda()
-    output = model(identity())
-    reference_output = reference(identity())
+    error = reference_output.sub(output).max()
+    torch.distributed.barrier()
+    assert error < 1.0e-6
+
+    output = forward_model(model, target)
+    error = reference_output.sub(output).max()
+    torch.distributed.barrier()
+    assert error < 1.0e-6
 
+    output = forward_model(model, target)
     error = reference_output.sub(output).max()
     torch.distributed.barrier()
     assert error < 1.0e-6
 
+    check_weights(model, reference, "before")
+    saved_weight_0 = model[0].weight.data.clone()
+    saved_weight_2 = model[2].weight.data.clone()
+    output = forward_model(model, target, step=True)
+    error = reference_output.sub(output).max()
+    assert error < 1.0e-6
+    model[0].weight.data = saved_weight_0
+    model[2].weight.data = saved_weight_2
+
+    worker_map = {i: f"Test{i}" for i in range(torch.distributed.get_world_size())}
+
     if pipe_world_size == 2:
-        pipe_model = Pipe(model, [2, 1], devices=pipeline_devices, chunks=chunk_size)
+        print(f"actually doing pipe stuff now")
+        assert torch.equal(saved_weight_0, model[0].weight.data)
+        assert torch.equal(saved_weight_2, model[2].weight.data)
+        pipe_model = Pipe(
+            model,
+            [2, 1],
+            style=Pipe.MultiProcess,
+            group=pipeline_devices,
+            worker_map=worker_map,
+            input_device=torch.cuda.current_device(),
+            chunks=chunk_size,
+            pipelined_backward=True,
+        ).cuda()
         torch.distributed.barrier()
+        pipe_rank = torch.distributed.get_rank(group=mpu.get_pipeline_parallel_group())
+        print(f"pipe rank is {pipe_rank}")
+        if pipe_rank == 0:
+            assert torch.equal(saved_weight_0, pipe_model[0].weight.data)
+        else:
+            if not torch.equal(saved_weight_2, pipe_model[0].weight.data):
+                print(f"ne {pipe_rank}: left\n{saved_weight_2}\nright:\n{pipe_model[0].weight.data}")
+                assert torch.equal(saved_weight_2, pipe_model[0].weight.data)
+        optimizer = torch.optim.SGD(pipe_model.parameters(), lr=0.01, momentum=0.9)
+        optimizer.zero_grad()
+        if pipe_rank == 0:
+            assert torch.equal(saved_weight_0, pipe_model[0].weight.data)
+            print(f"runner {rank}:\n{pipe_model[0].weight.data}")
+        else:
+            assert torch.equal(saved_weight_2, pipe_model[0].weight.data)
+            print(f"runner {rank}:\n{pipe_model[0].weight.data}")
+
+        if torch.distributed.get_rank(mpu.get_pipeline_parallel_group()) == 1:
+            check_weights(model, reference, "pre-pipe", index=2)
+        else:
+            check_weights(model, reference, "pre-pipe", index=0)
+
         pipe_output = pipe_model(identity())
+        print(f"exited pipe for {rank}")
+        forward_model(reference, target, step=True)
+
+        print(f"pipe_output {rank} = {pipe_output}")
+        print(f"reference_output {rank} = {reference_output}")
 
-        error = reference_output.sub(pipe_output.cuda()).max()
         torch.distributed.barrier()
-        assert error < 1.0e-6
+
+        if torch.distributed.get_rank(mpu.get_pipeline_parallel_group()) == 1:
+            error = reference_output.sub(pipe_output.cuda()).max()
+            if error >= 1.0e-6:
+                print(f"error bad {error}")
+            assert error < 1.0e-6
+
+            loss = nn.MSELoss()
+            failed = False
+            pipe_output.retain_grad()
+            with torch.autograd.profiler.profile() as prof:
+                try:
+                    loss(pipe_output, target).backward()
+                except Exception as e:
+                    failed = True
+                    print(f"got {e} while doing backward, deadlock?")
+            if failed:
+                raise RuntimeError("failed somehow")
+            dump_opt_params(optimizer)
+            optimizer.step()
+
+            print(f"calling check_weights on master")
+            check_weights(model, reference, "pipe", index=2)
+            print(f"waiting for barrier on master, pid={os.getpid()}")
+        else:
+            print(f"calling backwards on slave, pid={os.getpid()}")
+            failed = False
+            with torch.autograd.profiler.profile() as prof:
+                try:
+                    pipe_model.back_helper(pipe_output)
+                except Exception as e:
+                    failed = True
+                    print(f"got {e} while doing backward, deadlock?")
+            if failed:
+                raise RuntimeError("failed somehow")
+            dump_opt_params(optimizer)
+            print(f"calling step on slave")
+            optimizer.step()
+            print(f"calling check_weights on slave")
+            check_weights(model, reference, "pipe", index=0)
+            print(f"waiting for barrier on slave")
+
+        pipe_model.zero_grad()
+        torch.distributed.barrier()
+
+        pipe_output = pipe_model(identity())
+        updated_ref_output = forward_model(reference, target)
+        if torch.distributed.get_rank(mpu.get_pipeline_parallel_group()) == 1:
+            error = updated_ref_output.sub(pipe_output.cuda()).max()
+            print(f"outputs are ref:\n{updated_ref_output}\npipe:\n{pipe_output}")
+            assert error < 1.0e-6
+        torch.distributed.barrier()
+
+        print(f"finished waiting for barrier on, pid={os.getpid()}")
+
+    print(f"really exited pipe for {rank}")
+
+    rpc.shutdown()
+    torch.distributed.destroy_process_group()
 
 
 torch.backends.cudnn.deterministic = True
@@ -376,11 +559,29 @@ def test_column_parallel():
     spawn_for_all_world_sizes(run_test_column_parallel_linear)
 
 
+@pytest.mark.skipif("OMPI_COMM_WORLD_RANK" not in os.environ, reason="only works on mpi")
 def test_row_parallel():
     spawn_for_all_world_sizes(run_test_row_parallel_linear)
 
 
-def test_pipe():
+@torch_spawn([2])
+@pytest.mark.skipif("OMPI_COMM_WORLD_RANK" not in os.environ, reason="only works on mpi")
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="cuda required")
+def mpi_pipe():
+    mpu.destroy_model_parallel()
+    run_test_pipe(torch.distributed.get_rank(), torch.distributed.get_world_size(), skip_dist_init=True)
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="cuda required")
+def test_pipe_layer():
+    world_sizes = [x for x in get_world_sizes() if x <= torch.cuda.device_count() / 2]
+
+    spawn_for_all_world_sizes(run_test_pipe, args=[False])
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="cuda required")
+@pytest.mark.skip(reason="potential deadlock in nccl with multiple processes using the same gpu")
+def test_eight_pipe_layer():
     world_sizes = [x for x in get_world_sizes() if x <= torch.cuda.device_count() / 2]
 
-    spawn_for_all_world_sizes(run_test_pipe, world_sizes)
+    spawn_for_all_world_sizes(run_test_pipe, [8])

tests/nn/pipe/skip/test_portal.py

@@ -27,7 +27,7 @@ from fairscale.nn.pipe.stream import default_stream
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="cuda required")
 def test_copy_returns_on_next_device():
-    portal = Portal(torch.rand(1), tensor_life=1)
+    portal = Portal(torch.rand(1), tensor_life=1, index=0)
 
     prev_stream = default_stream(torch.device("cpu"))
     next_stream = default_stream(torch.device("cuda"))
@@ -52,7 +52,7 @@ def test_blue_orange():
     # tensor1 ------------ Join -- Fork --- Mul --- Add -- output
     #
     main = tensor1
-    portal = Portal(tensor2, tensor_life=2)
+    portal = Portal(tensor2, tensor_life=2, index=0)
     phony = portal.blue()
     main = join(main, phony)
     main, phony = fork(main)
@@ -78,7 +78,7 @@ def test_blue_orange_not_requires_grad():
     # tensor1 ------------ Join -- Fork --- Mul --- Add -- output
     #
     main = tensor1
-    portal = Portal(tensor2, tensor_life=2)
+    portal = Portal(tensor2, tensor_life=2, index=0)
     phony = portal.blue()
     main = join(main, phony)
     main, phony = fork(main)
@@ -93,7 +93,7 @@ def test_blue_orange_not_requires_grad():
 
 def test_use_grad():
     tensor = torch.rand(1, requires_grad=True)
-    portal = Portal(tensor, tensor_life=1)
+    portal = Portal(tensor, tensor_life=1, index=0)
 
     portal.put_grad(tensor)
     assert portal.use_grad() is tensor
@@ -111,7 +111,7 @@ class TestTensorLife:
         def new_portal(tensor_life):
             nonlocal portal
             tensor = torch.rand(1, requires_grad=True)
-            portal = Portal(tensor, tensor_life)
+            portal = Portal(tensor, tensor_life, 0)
             return portal, tensor
 
         yield new_portal

tests/nn/pipe/skip/test_tracker.py

@@ -72,9 +72,9 @@ def test_default_skip_tracker_by_data_parallel():
 
 def test_reuse_portal():
     skip_layout = SkipLayout(num_partitions=2, skip_routes={(None, "test"): (0, 1)})
-    skip_tracker = SkipTrackerThroughPotals(skip_layout)
+    skip_tracker = SkipTrackerThroughPotals(skip_layout, 0)
 
-    batch = Batch(torch.tensor([1.0]))
+    batch = Batch(torch.tensor([1.0]), 0)
     a = torch.tensor([2.0])
     b = torch.tensor([2.0])
 
@@ -87,9 +87,9 @@ def test_reuse_portal():
 
 def test_no_copy_no_portal():
     skip_layout = SkipLayout(num_partitions=2, skip_routes={(None, "copy"): (0, 1), (None, "not_copy"): (0, 0)})
-    skip_tracker = SkipTrackerThroughPotals(skip_layout)
+    skip_tracker = SkipTrackerThroughPotals(skip_layout, 0)
 
-    batch = Batch(torch.tensor([1.0]))
+    batch = Batch(torch.tensor([1.0]), 0)
     a = torch.tensor([2.0])
     b = torch.tensor([2.0])
 
@@ -104,9 +104,9 @@ def test_no_copy_no_portal():
 
 def test_tensor_life_without_checkpointing():
     skip_layout = SkipLayout(num_partitions=2, skip_routes={(None, "test"): (0, 1)})
-    skip_tracker = SkipTrackerThroughPotals(skip_layout)
+    skip_tracker = SkipTrackerThroughPotals(skip_layout, 0)
 
-    batch = Batch(torch.tensor([1.0]))
+    batch = Batch(torch.tensor([1.0]), 0)
     tensor = torch.tensor([2.0])
 
     skip_tracker.save(batch, None, "test", tensor)
@@ -118,9 +118,9 @@ def test_tensor_life_without_checkpointing():
 
 def test_tensor_life_with_checkpointing():
     skip_layout = SkipLayout(num_partitions=2, skip_routes={(None, "test"): (0, 1)})
-    skip_tracker = SkipTrackerThroughPotals(skip_layout)
+    skip_tracker = SkipTrackerThroughPotals(skip_layout, 0)
 
-    batch = Batch(torch.tensor([1.0]))
+    batch = Batch(torch.tensor([1.0]), 0)
     tensor = torch.tensor([2.0])
 
     with enable_checkpointing():

tests/nn/pipe/test_checkpoint.py

@@ -24,7 +24,7 @@ import torch
 from torch import nn
 import torch.cuda
 
-from fairscale.nn.pipe.checkpoint import Checkpointing, checkpoint, is_checkpointing, is_recomputing
+from fairscale.nn.pipe.checkpoint import Checkpointing, Function, TensorOrTensors, is_checkpointing, is_recomputing
 from fairscale.nn.pipe.dependency import fork, join
 from fairscale.nn.pipe.microbatch import Batch
 
@@ -33,6 +33,20 @@ if torch.cuda.is_available():
     devices.append("cuda")
 
 
+def make_checkpoint(function: Function, input: TensorOrTensors, index: int) -> TensorOrTensors:
+    """Makes a checkpoint with a simple interface like
+    :func:`torch.utils.checkpoint.checkpoint`. It's only used to test or debug
+    :class:`Checkpoint` and :class:`Recompute` without boilerplate.
+    """
+    batch = Batch(input, index)
+
+    chk = Checkpointing(function, batch)
+    batch = chk.checkpoint()
+    chk.recompute(batch)
+
+    return batch.tensor_or_tensors
+
+
 @pytest.mark.parametrize("device", devices)
 def test_serial_checkpoints(device):
     # Copied from https://github.com/pytorch/pytorch/pull/18568.
@@ -57,12 +71,12 @@ def test_serial_checkpoints(device):
     # Increase the next function sequence number.
     _ = a + 1 + 2 + 3 + 4 + 5
 
-    a = checkpoint(partial(Log.apply, "a"), a)
+    a = make_checkpoint(partial(Log.apply, "a"), a, 0)
 
     a, phony = fork(a)
     b = join(b, phony)
 
-    b = checkpoint(partial(Log.apply, "b"), b)
+    b = make_checkpoint(partial(Log.apply, "b"), b, 0)
 
     c = torch.cat((a, b))
 
@@ -79,7 +93,7 @@ def test_serial_checkpoints(device):
 
 
 def test_not_requires_grad():
-    x = Batch(torch.rand(1, requires_grad=False))
+    x = Batch(torch.rand(1, requires_grad=False), 0)
     assert not x[0].requires_grad
 
     def f(x):
@@ -102,7 +116,7 @@ def test_not_requires_grad_with_parameter():
     def f(x):
         return x * a
 
-    y = checkpoint(f, x)
+    y = make_checkpoint(f, x, 0)
     y.backward()
 
     assert a.grad is not None
@@ -119,7 +133,7 @@ def test_random_in_checkpoint(device):
 
     torch.manual_seed(0)
     chk_x = torch.randn(3, 3, device=device, requires_grad=True)
-    chk_y = checkpoint(dropout, chk_x)
+    chk_y = make_checkpoint(dropout, chk_x, 0)
     chk_y.norm().backward()
 
     assert torch.allclose(x.grad, chk_x.grad)
@@ -136,7 +150,7 @@ def test_detect_checkpointing_recomputing():
     model = Detect()
     input = torch.rand(1, requires_grad=True)
 
-    output = checkpoint(model, input)
+    output = make_checkpoint(model, input, 0)
     output.backward()
 
     assert logs == [(True, False), (False, True)]
@@ -167,5 +181,5 @@ def test_non_grad_output():
     model = ForkNonGrad()
     input = torch.rand(1, requires_grad=True)
 
-    output = checkpoint(model, input)
+    output = make_checkpoint(model, input, 0)
     output[0].backward()

tests/nn/pipe/test_microbatch.py

@@ -26,7 +26,7 @@ from fairscale.nn.pipe.microbatch import Batch, check, gather, scatter
 
 def test_batch_atomic():
     x = torch.tensor(42)
-    b = Batch(x)
+    b = Batch(x, 0)
 
     assert b.atomic
 
@@ -41,7 +41,7 @@ def test_batch_atomic():
 
 def test_batch_non_atomic():
     x, y = torch.tensor(42), torch.tensor(21)
-    b = Batch((x, y))
+    b = Batch((x, y), 0)
 
     assert not b.atomic
 
@@ -56,8 +56,8 @@ def test_batch_non_atomic():
 
 
 def test_batch_call():
-    a = Batch(torch.tensor(42))
-    b = Batch((torch.tensor(42), torch.tensor(21)))
+    a = Batch(torch.tensor(42), 0)
+    b = Batch((torch.tensor(42), torch.tensor(21)), 0)
 
     def f(x):
         return x
@@ -67,8 +67,8 @@ def test_batch_call():
 
 
 def test_batch_setitem_by_index():
-    a = Batch(torch.tensor(42))
-    b = Batch((torch.tensor(42), torch.tensor(21)))
+    a = Batch(torch.tensor(42), 0)
+    b = Batch((torch.tensor(42), torch.tensor(21)), 0)
 
     a[0] = torch.tensor(0)
     b[0] = torch.tensor(0)
@@ -83,8 +83,8 @@ def test_batch_setitem_by_index():
 
 
 def test_batch_setitem_by_slice():
-    a = Batch(torch.tensor(42))
-    b = Batch((torch.tensor(42), torch.tensor(21)))
+    a = Batch(torch.tensor(42), 0)
+    b = Batch((torch.tensor(42), torch.tensor(21)), 0)
 
     a[:] = (torch.tensor(0),)
     b[:] = (torch.tensor(0),)
@@ -115,7 +115,7 @@ def test_gather_tensors():
     a = torch.zeros(1, 1)
     b = torch.zeros(1, 1)
 
-    ab = gather([Batch(a), Batch(b)])
+    ab = gather([Batch(a, 0), Batch(b, 0)])
 
     assert ab.size() == (2, 1)
 
@@ -124,7 +124,7 @@ def test_gather_tuples():
     a = (torch.zeros(1, 1), torch.zeros(2, 2))
     b = (torch.zeros(1, 1), torch.zeros(2, 2))
 
-    ab = gather([Batch(a), Batch(b)])
+    ab = gather([Batch(a, 0), Batch(b, 0)])
 
     assert isinstance(ab, tuple)
     assert ab[0].size() == (2, 1)

tests/nn/pipe/test_worker.py

@@ -44,7 +44,7 @@ def test_join_running_workers():
         nonlocal count
         time.sleep(0.1)
         count += 1
-        return Batch(())
+        return Batch((), 0)
 
     with spawn_workers([fake_device() for _ in range(10)]) as (in_queues, out_queues):
 
@@ -70,7 +70,7 @@ def test_join_running_workers_with_exception():
         nonlocal count
         time.sleep(0.1)
         count += 1
-        return Batch(())
+        return Batch((), 0)
 
     with pytest.raises(ExpectedException):
         with spawn_workers([fake_device() for _ in range(10)]) as (in_queues, out_queues):
@@ -96,7 +96,7 @@ def test_compute_multithreading():
     def log_thread_id():
         thread_id = threading.current_thread().ident
         thread_ids.add(thread_id)
-        return Batch(())
+        return Batch((), 0)
 
     with spawn_workers([fake_device() for _ in range(2)]) as (in_queues, out_queues):
         for i in range(2):
@@ -112,7 +112,7 @@ def test_compute_success():
     """Task.compute returns (True, (task, batch)) on success."""
 
     def _42():
-        return Batch(torch.tensor(42))
+        return Batch(torch.tensor(42), 0)
 
     with spawn_workers([torch.device("cpu")]) as (in_queues, out_queues):
         t = Task(CPUStream, compute=_42, finalize=None)
@@ -145,7 +145,7 @@ def test_compute_exception():
 def test_grad_mode(grad_mode):
     def detect_grad_enabled():
         x = torch.rand(1, requires_grad=torch.is_grad_enabled())
-        return Batch(x)
+        return Batch(x, 0)
 
     with torch.set_grad_enabled(grad_mode):
         with spawn_workers([torch.device("cpu")]) as (in_queues, out_queues):

tests/nn/pipe_process/__init__.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Copyright 2019 Kakao Brain
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# tests/__init__.py makes pytest can import the application without custom sys.path or PYTHONPATH.
+# See also: https://docs.pytest.org/en/latest/goodpractices.html

tests/nn/pipe_process/conftest.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Copyright 2019 Kakao Brain
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import functools
+import os
+
+import pytest
+import torch
+
+from fairscale.nn.model_parallel import destroy_model_parallel
+
+
+@pytest.fixture(autouse=True)
+def manual_seed_zero():
+    torch.manual_seed(0)
+
+
+def cuda_sleep_impl(seconds, cycles_per_ms):
+    torch.cuda._sleep(int(seconds * cycles_per_ms * 1000))
+
+
+@pytest.fixture(scope="session")
+def cuda_sleep():
+    # Warm-up CUDA.
+    torch.empty(1, device="cuda")
+
+    # From test/test_cuda.py in PyTorch.
+    start = torch.cuda.Event(enable_timing=True)
+    end = torch.cuda.Event(enable_timing=True)
+    start.record()
+    torch.cuda._sleep(1000000)
+    end.record()
+    end.synchronize()
+    cycles_per_ms = 1000000 / start.elapsed_time(end)
+
+    return functools.partial(cuda_sleep_impl, cycles_per_ms=cycles_per_ms)
+
+
+def pytest_report_header():
+    return f"torch: {torch.__version__}"
+
+
+def pytest_runtest_setup(item):
+    print(f"setup mpi function called")
+
+
+def pytest_runtest_teardown(item):
+    if "OMPI_COMM_WORLD_RANK" in os.environ:
+        destroy_model_parallel()
+        if torch.distributed.is_initialized():
+            torch.distributed.destroy_process_group()

tests/nn/pipe_process/skip/__init__.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Copyright 2019 Kakao Brain
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

tests/nn/pipe_process/skip/test_gpipe.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Copyright 2019 Kakao Brain
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+import pytest
+import torch
+from torch import nn
+
+from fairscale.nn.pipe import Pipe
+from fairscale.nn.pipe.skip import pop, skippable, stash
+from fairscale.nn.pipe.skip.portal import PortalBlue, PortalCopy, PortalOrange
+from tests.nn.model_parallel.commons import get_worker_map, torch_spawn
+
+
+@torch_spawn([3])
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="cuda required")
+@pytest.mark.parametrize("balance", [[3], [1, 2], [2, 1], [1, 1, 1]], ids=["3", "1:2", "2:1", "1:1:1"])
+@pytest.mark.parametrize("checkpoint", ["never", "always", "except_last"])
+@pytest.mark.skipif("OMPI_COMM_WORLD_RANK" in os.environ, reason="broken on mpi")
+def x1to3(balance, checkpoint):
+    torch.manual_seed(0)
+
+    @skippable(stash=["1to3"])
+    class Layer1(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.conv = nn.Conv2d(3, 3, 1)
+
+        def forward(self, input):
+            yield stash("1to3", input)
+            output = self.conv(input)
+            return output
+
+    class Layer2(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.conv = nn.Conv2d(3, 3, 1)
+
+        def forward(self, input):
+            output = self.conv(input)
+            return output
+
+    @skippable(pop=["1to3"])
+    class Layer3(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.conv = nn.Conv2d(3, 3, 1)
+
+        def forward(self, input):
+            skip_1to3 = yield pop("1to3")
+            output = self.conv(input) + skip_1to3
+            return output
+
+    model = nn.Sequential(Layer1(), Layer2(), Layer3())
+    model = Pipe(
+        model,
+        balance,
+        chunks=3,
+        checkpoint=checkpoint,
+        input_device=torch.cuda.current_device(),
+        style=Pipe.MultiProcess,
+        worker_map=get_worker_map(),
+        pipelined_backward=False,
+    ).cuda()
+
+    input = torch.rand(30, 3, 224, 224, requires_grad=True).cuda()
+    input.retain_grad()
+    output = model(input)
+    if model.group.rank() == len(balance) - 1:
+        loss = output.mean()
+        loss.backward()
+    elif model.group.rank() < len(balance) - 1:
+        model.back_helper(output)
+    if model.group.rank() == len(balance) - 1:
+        # TODO(tom) the single-process test uses 2e-1 but for some reason
+        # mutli-process is more noisy, need to investigate why
+        assert torch.allclose(output.norm(), torch.tensor(1039.0).cuda(), atol=4e-1)
+    if model.group.rank() == 0:
+        assert torch.allclose(input.grad.norm(), torch.tensor(0.0004533053).cuda())
+
+    torch.distributed.barrier()
+
+
+@torch_spawn([2])
+@pytest.mark.skipif("OMPI_COMM_WORLD_RANK" in os.environ, reason="broken on mpi")
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="cuda required")
+def none_skip():
+    @skippable(stash=["none"])
+    class Stash(nn.Module):
+        def forward(self, input):
+            yield stash("none", None)
+            return input
+
+    @skippable(pop=["none"])
+    class Pop(nn.Module):
+        def forward(self, input):
+            none = yield pop("none")
+            assert none is None
+            return input
+
+    model = nn.Sequential(Stash(), Pop())
+    model = Pipe(
+        model,
+        [1, 1],
+        style=Pipe.MultiProcess,
+        worker_map=get_worker_map(),
+        input_device=torch.cuda.current_device(),
+        chunks=5,
+    ).cuda()
+
+    input = torch.rand(10, requires_grad=True).cuda()
+    input.retain_grad()
+    output = model(input)
+
+    def assert_grad_fn_is_not_portal(grad_fn, visited=set()):
+        if grad_fn in visited or grad_fn is None:
+            return
+
+        assert not isinstance(grad_fn, PortalBlue._backward_cls)
+        assert not isinstance(grad_fn, PortalCopy._backward_cls)
+        assert not isinstance(grad_fn, PortalOrange._backward_cls)
+
+        visited.add(grad_fn)
+        for next_grad_fn, _ in grad_fn.next_functions:
+            assert_grad_fn_is_not_portal(next_grad_fn, visited)
+
+    if model.group.rank() == 1:
+        assert_grad_fn_is_not_portal(output.grad_fn)
+
+        output.sum().backward()
+    else:
+        model.back_helper(output)
+        assert input.grad.mean().item() == 1
+
+
+@torch_spawn([2])
+def lazy_skippable_error():
+    """Using skippable layers in combination with lazy construction is currently
+    not supported, check that it raises an Exception"""
+
+    @skippable(stash=["1to3"])
+    class Layer1(nn.Linear):
+        pass
+
+    @skippable(pop=["1to3"])
+    class Layer3(nn.Linear):
+        pass
+
+    model = [lambda: Layer1(10, 10), lambda: nn.Linear(10, 10), lambda: Layer3(10, 10)]
+
+    with pytest.raises(ValueError, match="Can't use Skippable layers with multi-process pipe and lazy construction"):
+        Pipe(
+            model, [2, 1], style=Pipe.MultiProcess, worker_map=get_worker_map(),
+        )

tests/nn/pipe_process/skip/test_leak.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Copyright 2019 Kakao Brain
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+import pytest
+import torch
+from torch import nn
+
+from fairscale.nn.pipe import Pipe, is_checkpointing, is_recomputing
+from fairscale.nn.pipe.skip import pop, skippable, stash
+from fairscale.nn.pipe.skip.tracker import current_skip_tracker
+from tests.nn.model_parallel.commons import get_worker_map, torch_spawn
+
+
+@skippable(stash=["skip"])
+class Stash(nn.Module):
+    def forward(self, input):
+        yield stash("skip", input)
+        return input
+
+
+@skippable(pop=["skip"])
+class Pop(nn.Module):
+    def forward(self, input):
+        skip = yield pop("skip")
+        return input + skip
+
+
+@torch_spawn([2])
+@pytest.mark.parametrize("train", [True, False], ids=["train", "eval"])
+@pytest.mark.parametrize("checkpoint", ["always", "except_last", "never"])
+@pytest.mark.skipif("OMPI_COMM_WORLD_RANK" in os.environ, reason="broken on mpi")
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="cuda required")
+def delete_portal_tensor(train, checkpoint):
+    # Without checkpointing:
+    # +- Stash --+  +--- Pop ----+ - - - layers
+    # | 2,blue,1 |--| 1,orange,0 | - - - tensor_life and portal function
+    # +----------+  +------------+
+    #
+    # With checkpointing:
+    # +- Stash --+  +--- Pop ----+  +--- Pop'----+  +- Stash'--+
+    # | 3,blue,2 |--| 2,orange,1 |--| 1,orange,0 |--| 1,blue,0 |
+    # +----------+  +------------+  +------------+  +----------+
+
+    def portal_tensor_life_is(tensor_life, skip_tracker=None):
+        if skip_tracker is None:
+            skip_tracker = current_skip_tracker()
+
+        # Get the current portal.
+        portal = list(skip_tracker.portals.values())[0]
+
+        if tensor_life == 0:
+            return portal.tensor_life == 0 and portal.tensor is None
+        else:
+            return portal.tensor_life == tensor_life and portal.tensor is not None
+
+    # Check the portal tensor after 'Stash'.
+    stash_ = Stash()
+
+    @stash_.register_forward_hook
+    def check_portal_tensor_after_stash(*_):
+        if is_checkpointing():
+            assert portal_tensor_life_is(2)
+        elif is_recomputing():
+            assert portal_tensor_life_is(0)
+        else:
+            assert portal_tensor_life_is(1)
+
+    pop_ = Pop()
+
+    @pop_.register_forward_hook
+    def check_portal_tensor_after_pop(*_):
+        if is_checkpointing():
+            assert portal_tensor_life_is(1)
+        elif is_recomputing():
+            assert portal_tensor_life_is(0)
+        else:
+            assert portal_tensor_life_is(0)
+
+    class NoPortalTensorAtBackward(nn.Module):
+        class F(torch.autograd.Function):
+            @staticmethod
+            def forward(ctx, input):
+                ctx.skip_tracker = current_skip_tracker()
+                return input.detach()
+
+            @staticmethod
+            def backward(ctx, grad):
+                assert portal_tensor_life_is(0, skip_tracker=ctx.skip_tracker)
+                return grad
+
+        def forward(self, input):
+            return self.F.apply(input)
+
+    model = nn.Sequential(NoPortalTensorAtBackward(), stash_, pop_)
+    model = Pipe(
+        model, balance=[2, 1], style=Pipe.MultiProcess, worker_map=get_worker_map(), chunks=2, checkpoint=checkpoint,
+    )
+
+    input = torch.rand(10, requires_grad=True)
+
+    if train:
+        model.train()
+        output = model(input)
+        if model.group.rank() == 1:
+            output.norm().backward()
+        else:
+            model.back_helper(output)
+    else:
+        model.eval()
+        with torch.no_grad():
+            model(input)
+
+    torch.distributed.barrier()