Merge branch 'dev' into speedup-amax-kernel

Author: matthiasdiener

ci/pytorch.sh

@@ -93,6 +93,7 @@ run_test_config_mgpu(){
         run 3 distributed/test_fusible_ops.py
         run 3 distributed/test_numerics.py
         run 3 distributed/test_torch_fsdp2.py
+        run 3 distributed/test_torch_fsdp2_fp8.py
         run 3 fused_attn/test_fused_attn_with_cp.py
     fi
 }

tests/pytorch/distributed/run_fsdp2_fp8_model.py

@@ -0,0 +1,308 @@
+#!/usr/bin/python3
+# Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+# See LICENSE for license information.
+
+
+import os
+import sys
+import argparse
+
+import transformer_engine.pytorch as te
+from transformer_engine.common.recipe import Float8CurrentScaling, Format, DelayedScaling, MXFP8BlockScaling
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import nn, optim
+from torch.distributed import DeviceMesh
+from torch.distributed._composable.fsdp import fully_shard
+from torch.distributed.device_mesh import init_device_mesh
+from transformer_engine.pytorch import torch_version
+from transformer_engine.pytorch.fp8 import fp8_model_init
+from torch.nn.parallel import DistributedDataParallel as DDP
+from pathlib import Path
+
+class SimpleNet(nn.Module):  
+    def __init__(self, input_size, hidden_size, output_size, use_fsdp2=False):
+        super(SimpleNet, self).__init__()  
+          
+        # LayerNormLinear: fuses LayerNorm + Linear  
+        self.ln_linear = te.LayerNormLinear(  
+            in_features=input_size,  
+            out_features=hidden_size,  
+            eps=1e-5,
+            use_fsdp2=use_fsdp2,
+            keep_fp8_weight_transpose_cache=False
+        )  
+          
+        # LayerNormMLP: fuses LayerNorm + FC1 + Activation + FC2  
+        self.ln_mlp = te.LayerNormMLP(  
+            hidden_size=hidden_size,  
+            ffn_hidden_size=hidden_size * 4,  # Typical 4x expansion
+            use_fsdp2=use_fsdp2,
+            keep_fp8_weight_transpose_cache=False
+        )  
+          
+        # Regular Linear for final projection  
+        self.fc_out = te.Linear(  
+            hidden_size,   
+            output_size,  
+            use_fsdp2=use_fsdp2,
+            keep_fp8_weight_transpose_cache=False
+        )  
+  
+    def forward(self, x):  
+        # LayerNormLinear: applies LayerNorm then Linear  
+        x = self.ln_linear(x)  
+          
+        # LayerNormMLP: applies LayerNorm + FC1 + GELU + FC2  
+        x = self.ln_mlp(x)  
+          
+        # Final Linear projection  
+        x = self.fc_out(x)  
+          
+        return x
+
+def save_custom_attrs(module, _SKIP_KEYS = {"_data", "_module", "_transpose"}):
+    custom_attrs = {}
+    for name, param in module.named_parameters():
+        attrs = vars(param)
+        custom_attrs[name] = {k: v for k, v in attrs.items()}
+        for k in _SKIP_KEYS:
+            custom_attrs[name].pop(k, None)
+    return custom_attrs
+
+
+def restore_custom_attrs(module, custom_attrs):
+    for name, param in module.named_parameters():
+        if name in custom_attrs:
+            for attr_name, attr_value in custom_attrs[name].items():
+                setattr(param, attr_name, attr_value)
+
+
+def _parse_args(argv=None, namespace=None):
+    parser = argparse.ArgumentParser(description="Toy example for debugging fully_shard()")
+    parser.add_argument("--input-size", type=int, default=2048, help="Input size for the model")
+    parser.add_argument("--hidden-size", type=int, default=2048, help="Hidden layer size")
+    parser.add_argument("--output-size", type=int, default=2048, help="Output size for the model")
+    parser.add_argument("--batch-size", type=int, default=2048, help="Output size for the model")
+    parser.add_argument(
+        "--fp8-init", action="store_true", default=False, help="Initialize primary weights in FP8."
+    )
+    parser.add_argument(
+        "--iter", type=int, default=10, help="Number of iterations for forward pass"
+    )
+    parser.add_argument('--profile', action='store_true',
+                       help='Enable pytorch profiling.')
+    parser.add_argument('--profile-step-start', type=int, default=6,
+                       help='Global step to start profiling.')
+    parser.add_argument('--profile-step-end', type=int, default=7,
+                       help='Global step to stop profiling.')
+    parser.add_argument('--profile-ranks', nargs='+', type=int, default=[0],
+                       help='Global ranks to profile.')
+    parser.add_argument('--tensorboard-dir', type=str, default='./fsdp2_tensorboard',
+                       help='Write TensorBoard logs to this directory.')
+    parser.add_argument('--gradients-save-file', type=str, default='all_iters.pt',
+                       help='Write all the gradients across all the iterations to this file.')
+    parser.add_argument("--seed", type=int, default=42, help="RNG seed.")
+    parser.add_argument("--use-fsdp2", action='store_true',
+                       help='Enable New FSDP2 training.')
+    parser.add_argument("--memory-profile", action='store_true',
+                       help='profile memory traces')
+    parser.add_argument(
+        "--recipe",
+        type=str,
+        choices=["delayed", "mxfp8", "current"],
+        default="delayed",
+        help="Select the training recipe to use: 'delayed', 'mxfp8', or 'current'."
+    )
+
+    # Adding hsdp_dim as a list argument, comma-separated
+    parser.add_argument(
+        "--sharding-dims",
+        type=int,
+        nargs="+",
+        help='FSDP/HSDP sharding dimensions ("replicate", "shard")',
+    )
+    args = parser.parse_args(argv, namespace)
+    if args.sharding_dims:
+        assert len(args.sharding_dims) <= 2
+    return args
+
+
+sub_modules_to_wrap = [te.Linear, te.LayerNormLinear, te.LayerNormMLP]
+
+
+def _train(args):
+    assert "TORCHELASTIC_RUN_ID" in os.environ
+    WORLD_RANK = int(os.getenv("RANK", "0"))
+    WORLD_SIZE = int(os.getenv("WORLD_SIZE", "1"))
+    LOCAL_RANK = int(os.getenv("LOCAL_RANK", "0"))
+    LOCAL_SIZE = int(os.getenv("LOCAL_WORLD_SIZE", "1"))
+    assert LOCAL_SIZE == WORLD_SIZE
+
+    # Set device and initialize RNG states
+    torch.cuda.set_device(WORLD_RANK)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed(args.seed)
+
+    # Initialize torch.distributed global process group and get DP/TP groups
+    dist_init_kwargs = {
+        "backend": "nccl",
+        "rank": WORLD_RANK,
+        "world_size": WORLD_SIZE,
+    }
+    assert dist.is_nccl_available()
+    dist.init_process_group(**dist_init_kwargs)
+    nccl_world = dist.new_group(backend="nccl")
+    device = torch.device(f"cuda:{LOCAL_RANK}")
+
+    # FP8 Configuration
+    if args.recipe == "current":
+        fp8_recipe = Float8CurrentScaling()
+    elif args.recipe == "mxfp8":
+        fp8_recipe = MXFP8BlockScaling()
+    elif args.recipe == "delayed":
+        fp8_recipe = DelayedScaling()
+    else:
+        raise ValueError(f"Unsupported recipe: {args.recipe}")
+
+    if args.memory_profile:
+        torch.cuda.memory._record_memory_history(enabled='all', context='all', stacks='all')
+    if args.fp8_init:
+        # Build the model with the specified context
+        with fp8_model_init(enabled = True):
+            model = SimpleNet(args.input_size, args.hidden_size, args.output_size, use_fsdp2=args.use_fsdp2)
+    else:
+        model = SimpleNet(args.input_size, args.hidden_size, args.output_size, use_fsdp2=args.use_fsdp2)
+    # Move the model to the correct device
+    if not args.memory_profile:
+        model.load_state_dict(torch.load('fsdp_model.pth'))
+    model.to(device)
+
+    # Creating a DeviceMesh for fully_shard
+    world_size = int(WORLD_SIZE)
+    device_ids = list(range(world_size))
+
+    # Apply FSDP/HSDP
+    if args.use_fsdp2:
+        custom_attrs = save_custom_attrs(model)
+        if LOCAL_RANK == 0:
+            print(f"Rank {LOCAL_RANK}: Applying FSDP fully_shard() to the model...")
+            print(f"sharding-dims:{args.sharding_dims}")
+        # Setup the sharding mesh for FSDP/HSDP
+        if args.sharding_dims == None:  # FSDP
+            mesh = DeviceMesh("cuda", device_ids)
+        elif len(args.sharding_dims) == 1:
+            assert args.sharding_dims[0] == device_ids[-1] + 1
+            mesh = DeviceMesh("cuda", device_ids)
+        elif len(args.sharding_dims) == 2:  # HSDP
+            assert args.sharding_dims[0] * args.sharding_dims[1] == device_ids[-1] + 1
+            mesh = init_device_mesh(
+                "cuda",
+                (args.sharding_dims[0], args.sharding_dims[1]),
+                mesh_dim_names=("replicate", "shard"),
+            )
+        else:
+            assert False
+        for sub_module in model.modules():
+            if any(
+                isinstance(sub_module, sub_module_to_wrap) for sub_module_to_wrap in sub_modules_to_wrap
+            ):
+                fully_shard(sub_module, mesh=mesh)
+        fully_shard(model, mesh=mesh, reshard_after_forward=True)
+        restore_custom_attrs(model, custom_attrs)
+    else:
+        model = DDP(model, device_ids=[LOCAL_RANK])
+
+    optimizer =  te.optimizers.FusedAdam(model.parameters(), lr=1e-3)
+
+    input_path = Path("shared_input.pt")
+    if input_path.exists():
+        input_data = torch.load(input_path).to(device)
+    else:
+        input_data = torch.randn(args.batch_size, args.input_size, requires_grad=True).to(device)
+        torch.save(input_data.cpu(), input_path)
+        print("Generated and saved shared input tensor.")
+    
+    out_tensors = []
+    prof = None
+    if (
+        args.profile
+        and torch.distributed.get_rank() in args.profile_ranks
+    ):
+        prof = torch.profiler.profile(
+            activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
+            schedule=torch.profiler.schedule(
+                wait=max(args.profile_step_start - 1, 0),
+                warmup=1 if args.profile_step_start > 0 else 0,
+                active=args.profile_step_end - args.profile_step_start,
+                repeat=1,
+            ),
+            on_trace_ready=torch.profiler.tensorboard_trace_handler(args.tensorboard_dir),
+            record_shapes=True,
+            profile_memory=True,
+            with_stack=True,
+        )
+        prof.start()
+    for iteration in range(args.iter):
+        if LOCAL_RANK == 0:
+            print(f"Starting iteration...{iteration}")
+        if args.profile and torch.distributed.get_rank() in args.profile_ranks:
+            prof.step()
+
+        # Zero the parameter gradients
+        optimizer.zero_grad()
+        with te.fp8_autocast(enabled=True, fp8_recipe=fp8_recipe):
+            output = model(input_data)
+        target = torch.randn(args.batch_size, args.output_size).to(device)
+        loss = F.mse_loss(output, target)
+        loss.backward()
+        optimizer.step()
+        if LOCAL_RANK == 0:
+            print(f"Rank {LOCAL_RANK}: Iteration {iteration} completed.")
+        
+        if not args.profile and not args.memory_profile:
+            with torch.no_grad():
+                for name, p in model.named_parameters():
+                    full_grad = None
+                    if p.grad is not None and hasattr(p.grad, 'full_tensor'):
+                        # This call is required to be executed on ALL ranks
+                        # to complete the collective communication.
+                        full_grad = p.grad.full_tensor().detach().clone()
+                    elif p.grad is not None:
+                        full_grad = p.grad.detach().clone()
+                    # 2. Only Rank 0 stores the result
+                    if LOCAL_RANK == 0 and p.requires_grad:
+                        out_tensors.append((name, full_grad))
+    if (
+        args.profile
+        and iteration == args.profile_step_end
+        and torch.distributed.get_rank() in args.profile_ranks
+    ):
+        prof.stop()
+
+    if (not args.profile and not args.memory_profile) and LOCAL_RANK == 0:
+        torch.save(out_tensors, args.gradients_save_file)
+
+    if args.memory_profile:
+        snapshot = torch.cuda.memory._snapshot()
+        import pickle
+        with open('memory_snapshot.pickle', 'wb') as f:
+            pickle.dump(snapshot, f)
+        # To disable memory history recording when no longer needed
+        torch.cuda.memory._record_memory_history(enabled=None)
+
+    # NOTE: In PyTorch < 2.6 there’s a teardown race where one rank may call
+    # destroy_process_group() while other ranks still have in-flight NCCL ops,
+    # which can trigger a NCCL/RCCL comm error. Newer releases (>= 2.6) fixed
+    # this, but we kept a version-guarded barrier on older Torch for stability.
+    if torch_version() < (2, 6, 0):
+        dist.barrier(device_ids=[torch.cuda.current_device()])
+    dist.destroy_process_group()
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(_train(_parse_args()))