DEBUG - asynctp - runs

autoparallel/api.py

@@ -180,6 +180,7 @@ def __init__(
         enable_ac: bool = True,
         # None means 'auto'
         ac_stage_size_in_GiB: Optional[Union[float, str]] = "auto",
+        enable_asynctp: bool = False,
         **kwargs,
     ):
         self.stack = ExitStack()
@@ -210,6 +211,8 @@ def __init__(
         self.enable_ac = enable_ac
         self.ac_stage_size_in_GiB = ac_stage_size_in_GiB
 
+        self.enable_asynctp = enable_asynctp
+
         # NB: rest of the construction happens in __enter__
         self.active = False
 
@@ -236,6 +239,7 @@ def __enter__(self):
             self.mesh,
             rescale_grad_comm_cost_for_mp,
             repeated_subgraphs=self.kwargs.get("repeated_subgraphs", False),
+            enable_asynctp=self.enable_asynctp,
         )
 
         # makes sharding of params and gradients the same

autoparallel/optimize_sharding.py

@@ -105,6 +105,8 @@
 from .propagation_rules import _create_all_options
 from .utils import get_local_map_placement_option, get_placement_options
 
+aten = torch.ops.aten
+
 
 def _debug_node(node):
     def my_print(x):
@@ -128,7 +130,12 @@ def _get_next_name(name):
 
 class ShardingOptimizer:
     def __init__(
-        self, gm, mesh, rescale_grad_comm_cost_for_mp=1.0, repeated_subgraphs=False
+        self,
+        gm,
+        mesh,
+        rescale_grad_comm_cost_for_mp=1.0,
+        repeated_subgraphs=False,
+        enable_asynctp=False,
     ):
         self.gm = gm
         self.graph = gm.graph
@@ -139,11 +146,13 @@ def __init__(
         self.strats = self.build_sharding_metadata()
 
         self.cluster_links = {}
+        repeated_subgraphs = True
         if repeated_subgraphs:
             t = time.time()
             clusters = get_identical_regions(self.gm.graph, self.strats)
             print(f"Found {len(clusters)} clusters in {time.time() - t:.2f}s")
             self.create_cluster_links(clusters)
+        self.enable_asynctp = enable_asynctp
 
         # ds: Decision variables dictionary mapping (s_i, argi, ss, ii) -> ILP variable data
         # Each key represents a choice of input placement ii and output placement ss
@@ -556,11 +565,11 @@ def print_old(self):
         print(self.get_violated_constraints_log())
 
     def get_log(self, colored=False):
-
         from torch.fx.graph import _color_fns, _identity
 
         opt = {}
         nodes = list(self.graph.nodes)
+        log_shapes = False
         for x in self.res:
             opt.setdefault(nodes[x[0]], []).append(self.ds[x])
 
@@ -600,10 +609,28 @@ def get_log(self, colored=False):
                 code.insert(l_id, line)
                 l_id += 1
                 continue
+            log_extra = ""
+            if log_shapes:
+                if (
+                    isinstance(node, torch.fx.Node)
+                    and "val" in node.meta
+                    and isinstance(node.meta["val"], torch.Tensor)
+                ):
+                    log_extra += "("
+                    for arg in node.args:
+                        if (
+                            isinstance(arg, torch.fx.Node)
+                            and "val" in arg.meta
+                            and isinstance(arg.meta["val"], torch.Tensor)
+                        ):
+                            log_extra += str(list(arg.meta["val"].shape))
+                    log_extra += ") -> "
+                    log_extra += str(list(node.meta["val"].shape))
+                log_extra += "\n"
             # LOL
             while not code[l_id].lstrip().startswith(repr(node)):
                 l_id += 1
-            code[l_id] += line
+            code[l_id] = log_extra + code[l_id] + line
             l_id += 1
         code = "\n".join(code)
         total_cost = sum(self.ds[x]["cost"] for x in self.res)
@@ -641,6 +668,11 @@ def get_solution(self, verbose=False):
         # add their costs
         for x in self.ds.values():
             opt_target[x["va"]] += x["cost"]
+
+        if self.enable_asynctp:
+            for va, cost in self.add_asynctp_scores().items():
+                opt_target[va] += cost
+
         self.prob += pulp.lpSum([va * cost for va, cost in opt_target.items()])
 
         # solver = pulp.HiGHS(msg=verbose)
@@ -885,6 +917,105 @@ def add_sharded_output_constraint(self, output_placements=None):
                 "them from the graph to avoid aliasing."
             )
 
+    def add_asynctp_scores(self):
+        # Encourage placements that enable asyncTP fusions:
+        # -X % of comm_cost
+        # 1. ag + mm: S(d) -> R, d < mm.ndim - 1
+        # 2. mm + rs: P -> S(d)
+        # TODO1: Filter out FSDP ag/rs that will not be asyncTPed
+        # TODO2: With AsyncTP we should have perf wins,
+        # overlapping ((group_size - 1) / group_size) of communication
+        # minus cost of decomposition.
+        # For this we need to get group_size from the redistribution.
+        def _get_transformations(src_spec, tgt_spec):
+            # TODO: Use real transform preparation
+            # For now just checking left to right
+            src_pls = src_spec.placements
+            tgt_pls = tgt_spec.placements
+            transformations = []
+            for src_pl, tgt_pl in zip(src_pls, tgt_pls):
+                if src_pl == tgt_pl:
+                    continue
+                transformations.append((src_pl, tgt_pl))
+            return transformations
+
+        def _produces_asynctp_ag(src_spec, tgt_spec, mm_dim):
+            # Check that the last transition will be S(dim) -> Replicate
+
+            transformations = _get_transformations(src_spec, tgt_spec)
+            if len(transformations) == 0:
+                return False
+            last_t = transformations[-1]
+            return (
+                last_t[1].is_replicate()
+                and last_t[0].is_shard()
+                and last_t[0].dim < mm_dim - 1
+            )
+
+        def _produces_asynctp_rs(src_spec, tgt_spec, mm_dim):
+            # Check that the last transition will be P -> S(dim)
+            transformations = _get_transformations(src_spec, tgt_spec)
+            if len(transformations) == 0:
+                return False
+            last_t = transformations[-1]
+            return last_t[0].is_partial() and last_t[1].is_shard()
+
+        va_cost_delta = defaultdict(int)
+        strats = self.strats
+        for s_i, (node, s) in enumerate(strats.items()):
+            if not (node.op == "call_function" and node.target == aten.mm.default):
+                continue
+            mm_n = node
+            # Incentivize ag+mm
+            # ard0 of MM should be S(dim) -> R to have all_gather before mm
+            a_n = node.args[0]
+            mm_sts = s.strategies
+            for mm_st_i, mm_st in enumerate(mm_sts):
+                a_sts = strats[a_n].strategies
+                mm_tgt_spec = mm_st.input_specs[0]
+                for a_st_i, a_st in enumerate(a_sts):
+                    a_src_spec = a_st.output_spec
+                    # TODO: Is adding constraint to arg is enough or we need to follow the arg
+                    # ancestors and find the first sharding change?
+                    if _produces_asynctp_ag(
+                        a_src_spec, mm_tgt_spec, mm_n.meta["val"].ndim
+                    ):
+                        # TODO: We want to to calculate the cost of specific AG, as it will be pipelined,
+                        # for now using just redistribution cost
+                        cost = mm_st.redistribute_cost[0][a_st_i]
+                        if cost == float("inf"):
+                            continue
+                        va = self.ds[(s_i, 0, mm_st_i, a_st_i)]["va"]
+                        va_cost_delta[va] += -0.3 * cost
+                # mm+rs
+                src_spec = mm_st.output_spec
+                if len(mm_n.users) == 0:
+                    continue
+                mm_user = next(iter(mm_n.users))
+                mm_user_s_i = self.node_map[mm_user]
+                mm_u_arg_mm_i = -1
+                for i, arg in enumerate(mm_user.args):
+                    if arg == mm_n:
+                        mm_u_arg_mm_i = i
+                assert mm_u_arg_mm_i != -1
+                mm_user_sts = strats[mm_user].strategies
+                for mm_u_st_i, mm_u_st in enumerate(mm_user_sts):
+                    if _produces_asynctp_rs(
+                        src_spec,
+                        mm_u_st.input_specs[mm_u_arg_mm_i],
+                        mm_n.meta["val"].ndim,
+                    ):
+                        # TODO: We want to to calculate the cost of specific RS, as it will be pipelined,
+                        # for now using just redistribution cost
+                        cost = mm_u_st.redistribute_cost[mm_u_arg_mm_i][mm_u_st_i]
+                        if cost == float("inf"):
+                            continue
+                        key = (mm_user_s_i, mm_u_arg_mm_i, mm_u_st_i, mm_st_i)
+                        va = self.ds[key]["va"]
+                        va_cost_delta[va] += -0.3 * cost
+
+        return va_cost_delta
+
     def validate(self):
         for node in self.graph.nodes:
             if node.op != "call_function":

examples/example_llama3.py

@@ -211,7 +211,13 @@ def add_tp_constraints(autop):
 
 # parallelize the model
 with AutoParallel(
-    model, input_fn, mesh, mp_policy, compile=True, repeated_subgraphs=True
+    model,
+    input_fn,
+    mesh,
+    mp_policy,
+    compile=True,
+    repeated_subgraphs=True,
+    enable_asynctp=enable_asynctp,
 ) as autop:
     autop.add_parameter_memory_constraint(low=None, high=None)
 
@@ -229,22 +235,55 @@ def add_tp_constraints(autop):
     if enable_manual_constraint and not use_1d_mesh:
         add_tp_constraints(autop)
 
-    if enable_asynctp:
-        from torch.distributed._symmetric_memory import enable_symm_mem_for_group
+    enable_overlap_scheduling = True
+    enable_overlap_scheduling_bucketing = True
+    if enable_overlap_scheduling_bucketing:
+        assert (
+            enable_overlap_scheduling
+        ), "bucketing can not be used without overlap scheduling"
+    enable_asynctp = True
+    from autoparallel.asynctp import _micro_pipeline_tp_ag_transpose_mm_enabled, _micro_pipeline_tp_ag_mm_last_dim_enabled
+    _micro_pipeline_tp_ag_transpose_mm_enabled = True
+    _micro_pipeline_tp_ag_mm_last_dim_enabled = True
+    if (
+        enable_overlap_scheduling
+        or enable_overlap_scheduling_bucketing
+        or enable_asynctp
+    ):
+        torch._inductor.config.reorder_for_peak_memory = False
+        torch._inductor.config.reorder_for_compute_comm_overlap = False
+        torch._inductor.config.allow_buffer_reuse = False
+        torch._inductor.config.test_configs.aten_fx_overlap_preserving_bucketing = (
+            enable_overlap_scheduling_bucketing
+        )
+
+        if enable_asynctp:
+            from torch.distributed._symmetric_memory import enable_symm_mem_for_group
 
-        enable_symm_mem_for_group(mesh["dp"].get_group().group_name)
-        enable_symm_mem_for_group(mesh["tp"].get_group().group_name)
-        torch._inductor.config._micro_pipeline_tp = False
-        from autoparallel.asynctp import micro_pipeline_tp_pass
+            enable_symm_mem_for_group(mesh["tp"].get_group().group_name)
+            enable_symm_mem_for_group(mesh["dp"].get_group().group_name)
+            torch._inductor.config._micro_pipeline_tp = False
+            # Disable inductor AsyncTP passes, in favor of using Autoparallel passes fork.
+            # TODO: Switch to Inductor AsyncTP passes, when all additions landed.
+            from autoparallel.asynctp import micro_pipeline_tp_pass
 
         existing_post_grad_custom_post_pass = (
             torch._inductor.config.post_grad_custom_post_pass
         )
+        from torch._inductor.fx_passes.overlap_scheduling import OverlapScheduler
 
         def _pass(graph):
             if existing_post_grad_custom_post_pass is not None:
                 existing_post_grad_custom_post_pass(graph)
-            micro_pipeline_tp_pass(graph)
+
+            collective_info = None
+            if enable_overlap_scheduling:
+                overlap_scheduler = OverlapScheduler(graph.owning_module)
+                overlap_scheduler.run()
+                collective_info = overlap_scheduler.collective_info
+
+            if enable_asynctp:
+                micro_pipeline_tp_pass(graph, collective_info)
 
         torch._inductor.config.post_grad_custom_post_pass = _pass
 

mast/sweep.py

@@ -123,6 +123,12 @@ def maybe_find_pulp(maybe_path: Optional[str] = None) -> Optional[str]:
         "--model.name=llama3",
         "--compile.enable",
     ],
+    "llama3_FSDP_tp_async_tp_compile": llama3_2d_common_opts
+    + [
+        "--model.name=llama3",
+        "--compile.enable",
+        "--parallelism.enable_async_tensor_parallel",
+    ],
     "llama3_autop_2d_compile": llama3_2d_common_opts
     + [
         "--model.name=llama3_auto_parallel",