[5/x] make FSDP2 with float8 all-gather work for Float8Linear

float8_experimental/float8_linear.py

@@ -19,6 +19,7 @@
 from float8_experimental.float8_dynamic_linear import (
     cast_to_float8_e4m3_dynamic,
     cast_to_float8_e5m2_dynamic_bw,
+    WeightWithDynamicFloat8CastTensor,
 )
 
 from float8_experimental.float8_tensor import (
@@ -163,6 +164,7 @@ def __init__(self, *args, **kwargs):
         )
         # Amax scales should always be kept as float32.
         self.always_float32_buffers = set()
+        emulate = kwargs.pop("emulate", False)
         scaling_type_x = kwargs.pop("scaling_type_x", TensorScalingType.DELAYED)
         scaling_type_w = kwargs.pop("scaling_type_w", TensorScalingType.DELAYED)
         scaling_type_dL_dY = kwargs.pop("scaling_type_dL_dY", TensorScalingType.DELAYED)
@@ -187,8 +189,12 @@ def __init__(self, *args, **kwargs):
         self.create_buffers()
 
         # Defines the behavior of the matmul in the forward and backward pass
-        self.forward_config = ScaledMMConfig()
-        self.backward_config = ScaledMMConfig()
+        self.forward_config = ScaledMMConfig(
+            emulate, True if not emulate else False, False, config.pad_inner_dim
+        )
+        self.backward_config = ScaledMMConfig(
+            emulate, False, False, config.pad_inner_dim
+        )
 
         # Note: is_amax_initialized is not a buffer to avoid data dependent
         # control flow visible to dynamo
@@ -428,19 +434,20 @@ def from_float(
                 scaling_type_x=scaling_type_x,
                 scaling_type_w=scaling_type_w,
                 scaling_type_dL_dY=scaling_type_dL_dY,
+                emulate=emulate,
+            )
+        if (
+            scaling_type_w == TensorScalingType.DYNAMIC
+            and config.enable_fsdp_fp8_all_gather
+        ):
+            new_mod.weight = torch.nn.Parameter(
+                WeightWithDynamicFloat8CastTensor(mod.weight, new_mod.forward_config)
             )
-        new_mod.weight = mod.weight
+        else:
+            assert not config.enable_fsdp_fp8_all_gather, "unsupported"
+            new_mod.weight = mod.weight
         new_mod.bias = mod.bias
         # need to create buffers again when moving from meta device to
         # real device
         new_mod.create_buffers()
-        # Defines the behavior of the matmul in the forward and backward
-        # Forward we use fast_accum, backwards we do not
-        # TODO(future PR): move below to the constructor
-        new_mod.forward_config = ScaledMMConfig(
-            emulate, True if not emulate else False, False, config.pad_inner_dim
-        )
-        new_mod.backward_config = ScaledMMConfig(
-            emulate, False, False, config.pad_inner_dim
-        )
         return new_mod

test/test_fsdp2/test_fsdp2_eager.py

@@ -1,4 +1,5 @@
 import copy
+import itertools
 import threading
 import unittest
 from typing import Any, List
@@ -11,6 +12,7 @@
     Float8DynamicLinear,
     WeightWithDynamicFloat8CastTensor,
 )
+from float8_experimental.float8_linear import Float8Linear, TensorScalingType
 from float8_experimental.float8_linear_utils import swap_linear_with_float8_linear
 from test_fsdp2_common import (
     check_parity_bf16_mp,
@@ -74,8 +76,16 @@ def get_local_inp(self, dtype: torch.dtype = torch.float32):
         dist.broadcast(global_inp, src=0)
         return global_inp.view(self.world_size, -1)[self.rank].view(16, 16)
 
-    def swap_linear_with_dynamic(self, module: nn.Module, **kwargs: Any) -> nn.Module:
-        return swap_linear_with_float8_linear(module, Float8DynamicLinear, **kwargs)
+    def swap_linear_with_dynamic(
+        self, module: nn.Module, use_float8_linear=False, **kwargs: Any
+    ) -> nn.Module:
+        if use_float8_linear:
+            kwargs["scaling_type_x"] = TensorScalingType.DYNAMIC
+            kwargs["scaling_type_w"] = TensorScalingType.DYNAMIC
+            kwargs["scaling_type_dL_dY"] = TensorScalingType.DYNAMIC
+            return swap_linear_with_float8_linear(module, Float8Linear, **kwargs)
+        else:
+            return swap_linear_with_float8_linear(module, Float8DynamicLinear, **kwargs)
 
 
 class TestFloat8MultiProcess(FSDPTest, TestFloat8Common):
@@ -85,20 +95,26 @@ def world_size(self) -> int:
 
     @skip_if_lt_x_gpu(2)
     def test_transformer_parity_dynamic(self):
-        for enable_fsdp_fp8_all_gather in [False, True]:
-            self._test_transformer_parity_dynamic(enable_fsdp_fp8_all_gather)
+        for enable_fsdp_fp8_all_gather, use_float8_linear in itertools.product(
+            [False, True], [False, True]
+        ):
+            self._test_transformer_parity_dynamic(
+                enable_fsdp_fp8_all_gather, use_float8_linear
+            )
 
-    def _test_transformer_parity_dynamic(self, enable_fsdp_fp8_all_gather: bool):
+    def _test_transformer_parity_dynamic(
+        self, enable_fsdp_fp8_all_gather: bool, use_float8_linear: bool
+    ):
         # NOTE: Weight-tying does not compose with fp8 all-gather because the
         # embedding weight and output linear weight are tied but only the
         # latter uses fp8 compute. With fp8 all-gather, FSDP would pre-cast to
         # fp8 for that tied weight, incorrectly using fp8 for the embedding.
         weight_tying = not enable_fsdp_fp8_all_gather
         module = self.init_transformer(weight_tying=weight_tying)
         ref_module = copy.deepcopy(module)
-        ref_module = self.swap_linear_with_dynamic(ref_module).cuda()
+        ref_module = self.swap_linear_with_dynamic(ref_module, use_float8_linear).cuda()
         with set_enable_fsdp_fp8_all_gather(enable_fsdp_fp8_all_gather):
-            module = self.swap_linear_with_dynamic(module)
+            module = self.swap_linear_with_dynamic(module, use_float8_linear)
         for submodule in module.modules():
             if isinstance(submodule, TransformerBlock):
                 fully_shard(submodule)
@@ -108,17 +124,24 @@ def _test_transformer_parity_dynamic(self, enable_fsdp_fp8_all_gather: bool):
         local_inp = torch.randint(
             0, ref_module.tok_embeddings.weight.size(0), (16, 16), device="cuda"
         )
+        # TODO(future): change Float8DynamicLinear to module_cls below, and
+        # ensure there is no amax syncing for all-dynamic
         check_parity_no_mp(
             self, ref_module, ref_optim, module, optim, local_inp, Float8DynamicLinear
         )
 
     @skip_if_lt_x_gpu(2)
     def test_transformer_memory(self):
         """Tests peak active memory in the forward and backward passes."""
-        for enable_fsdp_fp8_all_gather in [False, True]:
-            self._test_transformer_memory(enable_fsdp_fp8_all_gather)
-
-    def _test_transformer_memory(self, enable_fsdp_fp8_all_gather: bool):
+        # for enable_fsdp_fp8_all_gather in [False, True]:
+        for enable_fsdp_fp8_all_gather, use_float8_linear in itertools.product(
+            [False, True], [False, True]
+        ):
+            self._test_transformer_memory(enable_fsdp_fp8_all_gather, use_float8_linear)
+
+    def _test_transformer_memory(
+        self, enable_fsdp_fp8_all_gather: bool, use_float8_linear: bool
+    ):
         torch.manual_seed(42)
         # Pre-run a linear forward (gemm and bias) and backward (gemm) to
         # allocate the cuBLAS workspaces before measuring the memory usage
@@ -141,7 +164,9 @@ def _test_transformer_memory(self, enable_fsdp_fp8_all_gather: bool):
         # Emulate the fp8 matmul to bypass the scaled matmul op's divisibility
         # requirement to use a smaller activation size
         with set_enable_fsdp_fp8_all_gather(enable_fsdp_fp8_all_gather):
-            model = self.swap_linear_with_dynamic(model, emulate=True)
+            model = self.swap_linear_with_dynamic(
+                model, emulate=True, use_float8_linear=use_float8_linear
+            )
         model_unsharded_numel = sum(p.numel() for p in model.parameters())
         model_sharded_numel = (model_unsharded_numel + 1) // 2
         block_lin_weight_numel = 0
@@ -242,16 +267,23 @@ class TestFloat8MultiThread(FSDPTestMultiThread, TestFloat8Common):
     def world_size(self) -> int:
         return 2
 
-    @unittest.skipIf(not TEST_CUDA, "no cuda")
-    def test_weight_subclass_dynamic(self):
+    def _test_weight_subclass_dynamic(self, use_float8_linear):
+        float8_cls = Float8Linear if use_float8_linear else Float8DynamicLinear
+        extra_kwargs = {}
+        if use_float8_linear:
+            extra_kwargs["scaling_type_x"] = TensorScalingType.DYNAMIC
+            extra_kwargs["scaling_type_w"] = TensorScalingType.DYNAMIC
+            extra_kwargs["scaling_type_dL_dY"] = TensorScalingType.DYNAMIC
+            pass
         tensor_cls = WeightWithDynamicFloat8CastTensor
         # Check for a single FSDP paramter group
         module_fp32 = self.init_single_module()
         with set_enable_fsdp_fp8_all_gather(True):
             module = swap_linear_with_float8_linear(
                 module_fp32,
-                Float8DynamicLinear,
+                float8_cls,
                 emulate=True,
+                **extra_kwargs,
             )
         self.assertIsInstance(module.weight, tensor_cls)
         fully_shard(module)
@@ -265,8 +297,9 @@ def test_weight_subclass_dynamic(self):
         with set_enable_fsdp_fp8_all_gather(True):
             module = swap_linear_with_float8_linear(
                 module,
-                Float8DynamicLinear,
+                float8_cls,
                 emulate=True,
+                **extra_kwargs,
             )
         for param_name, param in module.named_parameters():
             if "weight" in param_name:
@@ -280,7 +313,14 @@ def test_weight_subclass_dynamic(self):
                 self.assertIsInstance(param.to_local(), tensor_cls)
 
     @unittest.skipIf(not TEST_CUDA, "no cuda")
-    def test_fp8_fp32_all_gather_dynamic_comm_size(self):
+    def test_weight_subclass_float8_dynamic_linear(self):
+        self._test_weight_subclass_dynamic(use_float8_linear=False)
+
+    @unittest.skipIf(not TEST_CUDA, "no cuda")
+    def test_weight_subclass_float8_linear(self):
+        self._test_weight_subclass_dynamic(use_float8_linear=True)
+
+    def _test_fp8_fp32_all_gather_dynamic_comm_size(self, use_float8_linear):
         """
         Tests that fp8 all-gather with dynamic scaling communicates the
         expected number of bytes.
@@ -314,7 +354,7 @@ def get_expected_all_gather_size(module: nn.Module):
         module_fp32 = self.init_single_module()
         ref_module = copy.deepcopy(module_fp32)
         with set_enable_fsdp_fp8_all_gather(True):
-            module = self.swap_linear_with_dynamic(module_fp32)
+            module = self.swap_linear_with_dynamic(module_fp32, use_float8_linear)
         fully_shard(module)
         local_inp = self.get_local_inp()
         expected_all_gather_size = get_expected_all_gather_size(ref_module)
@@ -358,18 +398,30 @@ def get_expected_all_gather_size(module: nn.Module):
                 [s for s in expected_all_gather_sizes for _ in range(self.world_size)],
             )
 
+    @unittest.skipIf(not TEST_CUDA, "no cuda")
+    def test_fp8_fp32_all_gather_float8_dynamic_linear_comm_size(self):
+        self._test_fp8_fp32_all_gather_dynamic_comm_size(use_float8_linear=False)
+
+    @unittest.skipIf(not TEST_CUDA, "no cuda")
+    def test_fp8_fp32_all_gather_float8_linear_comm_size(self):
+        self._test_fp8_fp32_all_gather_dynamic_comm_size(use_float8_linear=True)
+
     @unittest.skipIf(not TEST_CUDA, "no cuda")
     def test_fp32_fp8_single_module_parity(self):
         """
         Tests numeric parity for fp32 parameters with fp8 computation with a
         single module/FSDP communication group.
         """
-        for enable_fsdp_fp8_all_gather in [False, True]:
+        for enable_fsdp_fp8_all_gather, use_float8_linear in itertools.product(
+            [False, True], [False, True]
+        ):
             module_fp32 = self.init_single_module()
-            ref_module = self.swap_linear_with_dynamic(copy.deepcopy(module_fp32))
+            ref_module = self.swap_linear_with_dynamic(
+                copy.deepcopy(module_fp32), use_float8_linear
+            )
             ref_module = ref_module.cuda()
             with set_enable_fsdp_fp8_all_gather(enable_fsdp_fp8_all_gather):
-                module = self.swap_linear_with_dynamic(module_fp32)
+                module = self.swap_linear_with_dynamic(module_fp32, use_float8_linear)
             fully_shard(module)
             ref_optim = torch.optim.Adam(ref_module.parameters(), lr=1e-2)
             optim = torch.optim.Adam(module.parameters(), lr=1e-2, foreach=True)
@@ -390,12 +442,16 @@ def test_fp32_fp8_multi_module_parity(self):
         Tests numeric parity for fp32 parameters with fp8 computation with
         multiple modules/FSDP communication groups.
         """
-        for enable_fsdp_fp8_all_gather in [False, True]:
+        for enable_fsdp_fp8_all_gather, use_float8_linear in itertools.product(
+            [False, True], [False, True]
+        ):
             module = self.init_multi_module()
             ref_module = copy.deepcopy(module)
-            ref_module = self.swap_linear_with_dynamic(ref_module).cuda()
+            ref_module = self.swap_linear_with_dynamic(
+                ref_module, use_float8_linear
+            ).cuda()
             with set_enable_fsdp_fp8_all_gather(enable_fsdp_fp8_all_gather):
-                module = self.swap_linear_with_dynamic(module)
+                module = self.swap_linear_with_dynamic(module, use_float8_linear)
             for submodule in module:
                 fully_shard(submodule)
             fully_shard(module)