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Source code for torch.distributed.pipelining.schedules

# Copyright (c) Meta Platforms, Inc. and affiliates import csv +import itertools import logging import re from abc import ABC, abstractmethod @@ -619,6 +620,144 @@

Source code for torch.distributed.pipelining.schedules

) +def _format_pipeline_order(pipeline_order: Dict[int, List[Optional[_Action]]]) -> str: + """ + Formats the pipeline order in a timestep (row) x rank (column) grid of actions + and returns the formatted string + """ + + # Calculate the maximum number of steps across all ranks + num_steps = max(len(actions) for actions in pipeline_order.values()) + step_labels = [ + "Step " + str(i).zfill(len(str(num_steps - 1))) for i in range(num_steps) + ] + # Sorting the dictionary by keys and retrieving values in that order + rank_actions = [ + pipeline_order.get(key, [""] * num_steps) for key in sorted(pipeline_order) + ] + # Transpose the list of lists (rows to columns) + transposed_actions = list(itertools.zip_longest(*rank_actions, fillvalue="")) + # Generate column labels for ranks + num_ranks = len(pipeline_order) + rank_labels = ["Rank " + str(i) for i in range(num_ranks)] + # Calculate the maximum length of each column, considering labels + max_lengths = [ + max(len(str(item)) if item is not None else 0 for item in col) + for col in zip(step_labels, *transposed_actions) + ] + # Format the header row with rank labels + header_row = " " * (len(step_labels[0]) + 2) + " ".join( + f"{label:<{max_lengths[i]}}" for i, label in enumerate(rank_labels) + ) + # Format each row with its corresponding label + formatted_rows = [ + f"{label}: " + + " ".join(f"{str(item):<{max_lengths[i]}}" for i, item in enumerate(row)) + for label, row in zip(step_labels, transposed_actions) + ] + # Join the rows into a single string + formatted_table = header_row + "\n" + "\n".join(formatted_rows) + "\n" + return formatted_table + + +def _validate_pipeline_order( + pipeline_order: Dict[int, List[Optional[_Action]]], + num_microbatches: int, + num_stages: int, +): + """ + pipeline_order[rank] = [(computation_type, microbatch_index, stage_index), ...] + Validating that the pipeline order follows the rules: + 1. Forward action for a microbatch must be before the Backward action for that microbatch + 2. Recv for a microbatch must be before the send for that microbatch + 3. Microbatch index is handled in sequential order for each stage + 4. A later stage cannot operate on a microbatch before any of the previous stages have operated on it + 5. Same microbatch cannot be handled in the same time step across ranks + """ + # microbatch_index: (current computation type, current stage) + microbatch_process_info: Dict[int, Tuple[_ComputationType, int]] = {} + max_timestep = max(len(rank_list) for rank_list in pipeline_order.values()) + for timestep in range(max_timestep): + error_msg: List[str] = [] + current_timestep_actions = [] + for rank in range(len(pipeline_order)): + action = ( + pipeline_order[rank][timestep] + if timestep < len(pipeline_order[rank]) + else None + ) + if action is not None: + current_timestep_actions.append(action) + + # TODO: enable this + # if len(current_timestep_actions) == 0: + # error_msg.append( + # "All actions were None, there is an unnecessary gap in the schedule" + # ) + + # Ensure that no microbatch is operated on twice in current_timestep_actions + unique_microbatch_indices = {action[1] for action in current_timestep_actions} + if len(unique_microbatch_indices) != len(current_timestep_actions): + error_msg.append( + "Duplicate microbatch index found in current_timestep_actions" + ) + + for action in current_timestep_actions: + computation_type, mb_index, stage_index = action + + if mb_index >= num_microbatches: + error_msg.append(f"Microbatch index {mb_index} out of range") + + # first microbatch + if mb_index not in microbatch_process_info: + if computation_type != _ComputationType.FORWARD or stage_index != 0: + error_msg.append(f"Incorrect start for microbatch {mb_index}") + microbatch_process_info[mb_index] = (computation_type, stage_index) + else: + # if the microbatch is included, check that the current stage is right after prev + prev_computation, prev_stage = microbatch_process_info[mb_index] + + if prev_computation == _ComputationType.FORWARD: + if prev_stage == num_stages - 1: + expected_stage = num_stages - 1 + expected_computation = _ComputationType.BACKWARD + else: + expected_stage = prev_stage + 1 + expected_computation = _ComputationType.FORWARD + elif prev_computation == _ComputationType.BACKWARD: + if prev_stage == 0: + error_msg.append( + f"[{mb_index=}] already finished backward computation" + ) + break + else: + expected_stage = prev_stage - 1 + expected_computation = _ComputationType.BACKWARD + else: + raise ValueError( + f"Computation type {prev_computation} not supported" + ) + + if expected_computation is not None: + if expected_computation != computation_type: + error_msg.append( + f"[{mb_index=}] {expected_computation=} VS. actual {computation_type=}" + ) + + if expected_stage != stage_index: + error_msg.append( + f"[{mb_index=}] {expected_stage=} VS. actual {stage_index=}" + ) + + microbatch_process_info[mb_index] = ( + expected_computation, + expected_stage, + ) + + if len(error_msg) != 0: + raise RuntimeError(f"Error at timestep {timestep}: " + ",".join(error_msg)) + + class _PipelineSchedule(ABC): def __init__( self, @@ -1331,90 +1470,106 @@

Source code for torch.distributed.pipelining.schedules

all_next_ranks.add(self.stage_index_to_group_rank[stage_index + 1]) for time_step, action in enumerate(self.pipeline_order[self.rank]): - ops: List[dist.P2POp] = [] - if action is not None: - computation_type, mb_index, stage_index = action - if computation_type == _ComputationType.FORWARD: - # perform forward computation - stage = stage_index_to_stage[stage_index] - output = stage.forward_one_chunk( - mb_index, arg_mbs[mb_index], kwarg_mbs[mb_index] - ) - self._maybe_compute_loss(stage, output, target_mbs, mb_index) - ops.extend(stage.get_fwd_send_ops(mb_index)) - elif computation_type == _ComputationType.BACKWARD: - # perform backward computation - stage = stage_index_to_stage[stage_index] - loss = self._maybe_get_loss(stage, mb_index) - stage.backward_one_chunk( - mb_index, loss=loss, full_backward=self.use_full_backward - ) - ops.extend(stage.get_bwd_send_ops(mb_index)) - elif computation_type == _ComputationType.WEIGHT: - # perform weight update - if self.use_full_backward: - raise ValueError( - f"We detected a weight update in the pipeline schedule, but \ - {self.use_full_backward=}" - ) - stage = stage_index_to_stage[stage_index] - stage.backward_weight_one_chunk(mb_index) - else: - raise ValueError(f"Unknown computation type {computation_type}") - - # Look at the neighboring ranks for this current timestep and determine whether - # this current rank needs to do any recv communication - for prev_rank in all_prev_ranks: - prev_rank_ops = self.pipeline_order[prev_rank] - prev_rank_action = None - if time_step < len(prev_rank_ops): - prev_rank_action = prev_rank_ops[time_step] - if prev_rank_action is not None: - computation_type, mb_index, stage_index = prev_rank_action - # Only handle sends for the forward from a previous rank + try: + ops: List[dist.P2POp] = [] + if action is not None: + computation_type, mb_index, stage_index = action if computation_type == _ComputationType.FORWARD: - # If not the last stage, then receive fwd activations - if stage_index + 1 in stage_index_to_stage: - # TODO: We are assuming that stage will always receive from stage-1 - # however that is not necessarily true of get_fwd_recv_ops - stage = stage_index_to_stage[stage_index + 1] - ops.extend(stage.get_fwd_recv_ops(mb_index)) - elif ( - computation_type == _ComputationType.BACKWARD - or computation_type == _ComputationType.WEIGHT - ): - # Previous rank doing backward or weight update has no influence for the current rank forward recv - pass - else: - raise ValueError(f"Unknown computation type {computation_type}") - - for next_rank in all_next_ranks: - next_rank_ops = self.pipeline_order[next_rank] - next_rank_action = None - if time_step < len(next_rank_ops): - next_rank_action = next_rank_ops[time_step] - if next_rank_action is not None: - computation_type, mb_index, stage_index = next_rank_action - # Only handle receives for the backwards from a next rank - if ( - computation_type == _ComputationType.FORWARD - or computation_type == _ComputationType.WEIGHT - ): - # Next rank doing forward or weight update has no influence for the current rank backward recv - pass + # perform forward computation + stage = stage_index_to_stage[stage_index] + output = stage.forward_one_chunk( + mb_index, arg_mbs[mb_index], kwarg_mbs[mb_index] + ) + self._maybe_compute_loss(stage, output, target_mbs, mb_index) + ops.extend(stage.get_fwd_send_ops(mb_index)) elif computation_type == _ComputationType.BACKWARD: - # If not the first stage, then receive bwd gradients - if stage_index - 1 in stage_index_to_stage: - # TODO: We are assuming that stage will always receive from stage+1 - # however that is not necessarily true of get_bwd_recv_ops - stage = stage_index_to_stage[stage_index - 1] - ops.extend(stage.get_bwd_recv_ops(mb_index)) + # perform backward computation + stage = stage_index_to_stage[stage_index] + loss = self._maybe_get_loss(stage, mb_index) + stage.backward_one_chunk( + mb_index, loss=loss, full_backward=self.use_full_backward + ) + ops.extend(stage.get_bwd_send_ops(mb_index)) + elif computation_type == _ComputationType.WEIGHT: + # perform weight update + if self.use_full_backward: + raise ValueError( + f"We detected a weight update in the pipeline schedule, but \ + {self.use_full_backward=}" + ) + stage = stage_index_to_stage[stage_index] + stage.backward_weight_one_chunk(mb_index) else: raise ValueError(f"Unknown computation type {computation_type}") - # do the communication - if ops: - _batch_p2p(ops).wait() + # Look at the neighboring ranks for this current timestep and determine whether + # this current rank needs to do any recv communication + for prev_rank in all_prev_ranks: + prev_rank_ops = self.pipeline_order[prev_rank] + prev_rank_action = None + if time_step < len(prev_rank_ops): + prev_rank_action = prev_rank_ops[time_step] + if prev_rank_action is not None: + computation_type, mb_index, stage_index = prev_rank_action + # Only handle sends for the forward from a previous rank + if computation_type == _ComputationType.FORWARD: + # If not the last stage, then receive fwd activations + if stage_index + 1 in stage_index_to_stage: + # TODO: We are assuming that stage will always receive from stage-1 + # however that is not necessarily true of get_fwd_recv_ops + stage = stage_index_to_stage[stage_index + 1] + ops.extend(stage.get_fwd_recv_ops(mb_index)) + elif ( + computation_type == _ComputationType.BACKWARD + or computation_type == _ComputationType.WEIGHT + ): + # Previous rank doing backward or weight update has no influence for the current rank forward recv + pass + else: + raise ValueError( + f"Unknown computation type {computation_type}" + ) + + for next_rank in all_next_ranks: + next_rank_ops = self.pipeline_order[next_rank] + next_rank_action = None + if time_step < len(next_rank_ops): + next_rank_action = next_rank_ops[time_step] + if next_rank_action is not None: + computation_type, mb_index, stage_index = next_rank_action + # Only handle receives for the backwards from a next rank + if ( + computation_type == _ComputationType.FORWARD + or computation_type == _ComputationType.WEIGHT + ): + # Next rank doing forward or weight update has no influence for the current rank backward recv + pass + elif computation_type == _ComputationType.BACKWARD: + # If not the first stage, then receive bwd gradients + if stage_index - 1 in stage_index_to_stage: + # TODO: We are assuming that stage will always receive from stage+1 + # however that is not necessarily true of get_bwd_recv_ops + stage = stage_index_to_stage[stage_index - 1] + ops.extend(stage.get_bwd_recv_ops(mb_index)) + else: + raise ValueError( + f"Unknown computation type {computation_type}" + ) + + # do the communication + if ops: + _batch_p2p(ops).wait() + except Exception as e: + logger.error( + "[Rank %s] pipeline schedule %s caught the following exception \ + at time_step %s when running action %s", + self.rank, + self.__class__.__name__, + time_step, + action, + ) + logger.error("%s", _format_pipeline_order(self.pipeline_order)) + raise e # Return losses if there is a container passed in self._update_losses(self._stages, losses)
diff --git a/main/_modules/torch/distributed/pipelining/stage.html b/main/_modules/torch/distributed/pipelining/stage.html index 1189bb2ae6..f43e81cdbd 100644 --- a/main/_modules/torch/distributed/pipelining/stage.html +++ b/main/_modules/torch/distributed/pipelining/stage.html @@ -280,7 +280,7 @@
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@@ -912,7 +912,7 @@

Source code for torch.distributed.pipelining.stage

else: if not (grad is None and grad_recv_stage is None): raise RuntimeError( - f"[{self.stage_index}] for chunk {bwd_chunk_id - 1} has gradients {grad} " + f"[{self.stage_index}] for chunk {bwd_chunk_id} has gradients {grad} " f"and is expecting to send gradients to stage {grad_recv_stage}" ) return ops diff --git a/main/_modules/torch/distributed/rpc.html b/main/_modules/torch/distributed/rpc.html index 016b3b406d..1543e8f364 100644 --- a/main/_modules/torch/distributed/rpc.html +++ b/main/_modules/torch/distributed/rpc.html @@ -280,7 +280,7 @@
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Source code for torch.nn.modules.module

 
             mod = getattr(mod, item)
 
-            if type(mod) is not torch.nn.Module:
+            # Use isinstance instead of type here to also handle subclass of nn.Module
+            if not isinstance(mod, torch.nn.Module):
                 raise AttributeError("`" + item + "` is not an nn.Module")
+
         setattr(mod, name, module)
[docs] def get_parameter(self, target: str) -> "Parameter": diff --git a/main/_modules/torch/nn/modules/normalization.html b/main/_modules/torch/nn/modules/normalization.html index 409f5d39ec..9b145a0e24 100644 --- a/main/_modules/torch/nn/modules/normalization.html +++ b/main/_modules/torch/nn/modules/normalization.html @@ -280,7 +280,7 @@
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diff --git a/main/_modules/typing.html b/main/_modules/typing.html index 28490c4705..5afc969ebf 100644 --- a/main/_modules/typing.html +++ b/main/_modules/typing.html @@ -280,7 +280,7 @@
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diff --git a/main/_sources/generated/exportdb/index.rst.txt b/main/_sources/generated/exportdb/index.rst.txt index 77ed2af021..1b804063f8 100644 --- a/main/_sources/generated/exportdb/index.rst.txt +++ b/main/_sources/generated/exportdb/index.rst.txt @@ -19,8 +19,8 @@ support in export please create an issue in the pytorch/pytorch repo wih a modul :caption: Tags torch.escape-hatch - torch.dynamic-shape torch.cond + torch.dynamic-shape python.closure torch.dynamic-value python.data-structure @@ -219,7 +219,7 @@ cond_branch_class_method .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -309,7 +309,7 @@ cond_branch_nested_function .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -396,7 +396,7 @@ cond_branch_nonlocal_variables .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -510,7 +510,7 @@ cond_closed_over_variable .. note:: - Tags: :doc:`python.closure `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`python.closure ` Support Level: SUPPORTED @@ -578,7 +578,7 @@ cond_operands .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -662,7 +662,7 @@ cond_predicate .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -1136,7 +1136,7 @@ dynamic_shape_map .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.map ` + Tags: :doc:`torch.map `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -1372,7 +1372,7 @@ list_contains .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`python.data-structure `, :doc:`python.assert ` + Tags: :doc:`python.data-structure `, :doc:`python.assert `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -1422,7 +1422,7 @@ list_unpack .. note:: - Tags: :doc:`python.control-flow `, :doc:`python.data-structure ` + Tags: :doc:`python.data-structure `, :doc:`python.control-flow ` Support Level: SUPPORTED @@ -2211,6 +2211,6 @@ Result: .. code-block:: - Unsupported: torch.* op returned non-Tensor int call_function + Unsupported: torch.* op returned non-Tensor int call_function diff --git a/main/_sources/generated/exportdb/python.assert.rst.txt b/main/_sources/generated/exportdb/python.assert.rst.txt index 403a74f6f5..d2e7276097 100644 --- a/main/_sources/generated/exportdb/python.assert.rst.txt +++ b/main/_sources/generated/exportdb/python.assert.rst.txt @@ -54,7 +54,7 @@ list_contains .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`python.data-structure `, :doc:`python.assert ` + Tags: :doc:`python.data-structure `, :doc:`python.assert `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED diff --git a/main/_sources/generated/exportdb/python.closure.rst.txt b/main/_sources/generated/exportdb/python.closure.rst.txt index a0b6799a5e..801570f84f 100644 --- a/main/_sources/generated/exportdb/python.closure.rst.txt +++ b/main/_sources/generated/exportdb/python.closure.rst.txt @@ -5,7 +5,7 @@ cond_closed_over_variable .. note:: - Tags: :doc:`python.closure `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`python.closure ` Support Level: SUPPORTED diff --git a/main/_sources/generated/exportdb/python.control-flow.rst.txt b/main/_sources/generated/exportdb/python.control-flow.rst.txt index 8e1b37feba..0e957b1630 100644 --- a/main/_sources/generated/exportdb/python.control-flow.rst.txt +++ b/main/_sources/generated/exportdb/python.control-flow.rst.txt @@ -57,7 +57,7 @@ list_unpack .. note:: - Tags: :doc:`python.control-flow `, :doc:`python.data-structure ` + Tags: :doc:`python.data-structure `, :doc:`python.control-flow ` Support Level: SUPPORTED diff --git a/main/_sources/generated/exportdb/python.data-structure.rst.txt b/main/_sources/generated/exportdb/python.data-structure.rst.txt index c54923e4e8..562430d8a0 100644 --- a/main/_sources/generated/exportdb/python.data-structure.rst.txt +++ b/main/_sources/generated/exportdb/python.data-structure.rst.txt @@ -133,7 +133,7 @@ list_contains .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`python.data-structure `, :doc:`python.assert ` + Tags: :doc:`python.data-structure `, :doc:`python.assert `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -183,7 +183,7 @@ list_unpack .. note:: - Tags: :doc:`python.control-flow `, :doc:`python.data-structure ` + Tags: :doc:`python.data-structure `, :doc:`python.control-flow ` Support Level: SUPPORTED diff --git a/main/_sources/generated/exportdb/torch.cond.rst.txt b/main/_sources/generated/exportdb/torch.cond.rst.txt index d024584fc7..7907a66f92 100644 --- a/main/_sources/generated/exportdb/torch.cond.rst.txt +++ b/main/_sources/generated/exportdb/torch.cond.rst.txt @@ -5,7 +5,7 @@ cond_branch_class_method .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -95,7 +95,7 @@ cond_branch_nested_function .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -182,7 +182,7 @@ cond_branch_nonlocal_variables .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -296,7 +296,7 @@ cond_closed_over_variable .. note:: - Tags: :doc:`python.closure `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`python.closure ` Support Level: SUPPORTED @@ -364,7 +364,7 @@ cond_operands .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -448,7 +448,7 @@ cond_predicate .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED diff --git a/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt b/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt index cdd8f9d14d..2ad8cfd528 100644 --- a/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt +++ b/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt @@ -5,7 +5,7 @@ cond_branch_class_method .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -95,7 +95,7 @@ cond_branch_nested_function .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -182,7 +182,7 @@ cond_branch_nonlocal_variables .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -296,7 +296,7 @@ cond_operands .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -380,7 +380,7 @@ cond_predicate .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.cond ` + Tags: :doc:`torch.cond `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -551,7 +551,7 @@ dynamic_shape_map .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.map ` + Tags: :doc:`torch.map `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED @@ -757,7 +757,7 @@ list_contains .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`python.data-structure `, :doc:`python.assert ` + Tags: :doc:`python.data-structure `, :doc:`python.assert `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED diff --git a/main/_sources/generated/exportdb/torch.map.rst.txt b/main/_sources/generated/exportdb/torch.map.rst.txt index 6a13593e81..c6ba5f67b7 100644 --- a/main/_sources/generated/exportdb/torch.map.rst.txt +++ b/main/_sources/generated/exportdb/torch.map.rst.txt @@ -5,7 +5,7 @@ dynamic_shape_map .. note:: - Tags: :doc:`torch.dynamic-shape `, :doc:`torch.map ` + Tags: :doc:`torch.map `, :doc:`torch.dynamic-shape ` Support Level: SUPPORTED diff --git a/main/_sources/generated/exportdb/torch.operator.rst.txt b/main/_sources/generated/exportdb/torch.operator.rst.txt index 144f064b05..d0e47d84ff 100644 --- a/main/_sources/generated/exportdb/torch.operator.rst.txt +++ b/main/_sources/generated/exportdb/torch.operator.rst.txt @@ -40,4 +40,4 @@ Result: .. code-block:: - Unsupported: torch.* op returned non-Tensor int call_function + Unsupported: torch.* op returned non-Tensor int call_function diff --git a/main/_sources/mps_environment_variables.rst.txt b/main/_sources/mps_environment_variables.rst.txt index d382140e85..34c3ab75ce 100644 --- a/main/_sources/mps_environment_variables.rst.txt +++ b/main/_sources/mps_environment_variables.rst.txt @@ -12,6 +12,10 @@ MPS Environment Variables - Description * - ``PYTORCH_DEBUG_MPS_ALLOCATOR`` - If set to ``1``, set allocator logging level to verbose. + * - ``PYTORCH_MPS_LOG_PROFILE_INFO`` + - Set log options bitmask to ``MPSProfiler``. See ``LogOptions`` enum in `aten/src/ATen/mps/MPSProfiler.h` for available options. + * - ``PYTORCH_MPS_TRACE_SIGNPOSTS`` + - Set profile and signpost bitmasks to ``MPSProfiler``. See ``ProfileOptions`` and ``SignpostTypes`` enums in `aten/src/ATen/mps/MPSProfiler.h` for available options. * - ``PYTORCH_MPS_HIGH_WATERMARK_RATIO`` - High watermark ratio for MPS allocator. By default, it is set to 1.7. * - ``PYTORCH_MPS_LOW_WATERMARK_RATIO`` diff --git a/main/amp.html b/main/amp.html index 2a10489475..d97cf63201 100644 --- a/main/amp.html +++ b/main/amp.html @@ -281,7 +281,7 @@
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