Thread through the scaling type argument to float8 constructors

ghstack-source-id: a740bcf5ce2098160870ee8da085861e956c5254
Pull Request resolved: #301

.pre-commit-config.yaml

@@ -10,8 +10,6 @@ repos:
     -   id: trailing-whitespace
     -   id: check-ast
     -   id: check-merge-conflict
-    -   id: no-commit-to-branch
-        args: ['--branch=main']
     -   id: check-added-large-files
         args: ['--maxkb=500']
     -   id: end-of-file-fixer

float8_experimental/__init__.py

@@ -5,11 +5,15 @@
 # LICENSE file in the root directory of this source tree.
 # Lets define a few top level things here
 from float8_experimental.float8_linear import Float8Linear
-from float8_experimental.float8_tensor import Float8Tensor, ScaledMMConfig
+from float8_experimental.float8_tensor import (
+    Float8Tensor,
+    ScaledMMConfig,
+    ScalingStrategy,
+)
 
 # Needed to load Float8Tensor with weights_only = True
 from torch.serialization import add_safe_globals
 
-add_safe_globals([Float8Tensor, ScaledMMConfig])
+add_safe_globals([Float8Tensor, ScaledMMConfig, ScalingStrategy])
 
 __all__ = ["Float8Tensor", "Float8Linear"]

float8_experimental/float8_dynamic_linear.py

@@ -19,6 +19,7 @@
     Float8Tensor,
     merge_mm_configs,
     ScaledMMConfig,
+    ScalingStrategy,
     tensor_already_casted_to_fp8,
     to_fp8_no_autograd,
 )
@@ -36,21 +37,29 @@ class NoopFwToFloat8E5M2Bw(torch.autograd.Function):
     @staticmethod
     def forward(
         ctx,
-        tensor,
+        tensor: torch.Tensor,
         mm_config: ScaledMMConfig,
+        scaling_strategy: ScalingStrategy,
     ):
+        print(f"{mm_config=}")
+        print(f"{scaling_strategy=}")
         ctx.mm_config = mm_config
+        ctx.scaling_strategy = scaling_strategy
         return tensor
 
     @staticmethod
-    def backward(ctx, gradY):
+    def backward(ctx, gradY: torch.Tensor):
         if tensor_already_casted_to_fp8(gradY):
             return gradY, None
         gradY_scale = tensor_to_scale(gradY, e5m2_dtype)
         fp8_tensor = to_fp8_no_autograd(
-            gradY, gradY_scale, e5m2_dtype, mm_config=ctx.mm_config
+            gradY,
+            gradY_scale,
+            e5m2_dtype,
+            mm_config=ctx.mm_config,
+            scaling_strategy=ctx.scaling_strategy,
         )
-        return fp8_tensor, None
+        return fp8_tensor, None, None
 
 
 class Float8DynamicLinear(torch.nn.Linear):
@@ -63,13 +72,15 @@ def __init__(self, **super_kwargs):
         super().__init__(**super_kwargs)
 
     def forward(self, input: torch.Tensor) -> torch.Tensor:
-        x_fp8 = cast_to_float8_e4m3fn(input, self.forward_config)
+        x_fp8 = cast_to_float8_e4m3fn(input, self.forward_config, self.scaling_strategy)
         if isinstance(self.weight, Float8Tensor):  # cast by FSDP
             w_fp8 = self.weight
         else:
-            w_fp8 = cast_to_float8_e4m3fn(self.weight, self.forward_config)
+            w_fp8 = cast_to_float8_e4m3fn(
+                self.weight, self.forward_config, self.scaling_strategy
+            )
         y = torch.nn.functional.linear(x_fp8, w_fp8, self.bias)
-        y = cast_to_float8_e5m2_bw(y, self.backward_config)
+        y = cast_to_float8_e5m2_bw(y, self.backward_config, self.scaling_strategy)
         return y
 
     @classmethod
@@ -101,6 +112,9 @@ def from_float(cls, mod, emulate: bool = False) -> "Float8DynamicLinear":
             fp8_output=False,
             pad_inner_dim=config.pad_inner_dim,
         )
+        # TODO: For now hardcode TensorWise scaling
+        new_mod.scaling_strategy = ScalingStrategy.TensorWise
+
         if config.enable_fsdp_fp8_all_gather:
             new_mod.weight = nn.Parameter(
                 WeightWithDynamicFloat8CastTensor(mod.weight, new_mod.forward_config)
@@ -112,18 +126,27 @@ def from_float(cls, mod, emulate: bool = False) -> "Float8DynamicLinear":
 
 
 def cast_to_float8_e4m3fn(
-    inpt_tensor: torch.Tensor, mm_config: ScaledMMConfig, reduce_amax: bool = False
+    inpt_tensor: torch.Tensor,
+    mm_config: ScaledMMConfig,
+    scaling_strategy: ScalingStrategy,
+    reduce_amax: bool = False,
 ) -> Float8Tensor:
     if tensor_already_casted_to_fp8(inpt_tensor):
         return inpt_tensor
     scale = tensor_to_scale(inpt_tensor, e4m3_dtype, reduce_amax)
-    return Float8Tensor.to_float8(inpt_tensor, scale, e4m3_dtype, mm_config=mm_config)
+    return Float8Tensor.to_float8(
+        inpt_tensor,
+        scale,
+        e4m3_dtype,
+        mm_config=mm_config,
+        scaling_strategy=scaling_strategy,
+    )
 
 
 def cast_to_float8_e5m2_bw(
-    gradY: torch.Tensor, mm_config: ScaledMMConfig
+    gradY: torch.Tensor, mm_config: ScaledMMConfig, scaling_strategy: ScalingStrategy
 ) -> torch.Tensor:
-    return NoopFwToFloat8E5M2Bw.apply(gradY, mm_config)
+    return NoopFwToFloat8E5M2Bw.apply(gradY, mm_config, scaling_strategy)
 
 
 # FSDP pads its local tensor on dim-0. The subclass should be preserved such

float8_experimental/float8_linear.py

@@ -18,6 +18,7 @@
 from float8_experimental.float8_tensor import (
     Float8Tensor,
     ScaledMMConfig,
+    ScalingStrategy,
     to_fp8_no_autograd,
 )
 
@@ -75,11 +76,13 @@ def forward(
         scale_fn_name,
         is_amax_initialized,
         mm_config: ScaledMMConfig,
+        scaling_strategy: ScalingStrategy,
     ):
         ctx.save_for_backward(fp8_amax_dL_dY, fp8_amax_history_dL_dY, fp8_scale_dL_dY)
         ctx.scale_fn_name = scale_fn_name
         ctx.is_amax_initialized = is_amax_initialized
         ctx.mm_config = mm_config
+        ctx.scaling_strategy = scaling_strategy
         return tensor
 
     @staticmethod
@@ -102,9 +105,13 @@ def backward(ctx, go):
         fp8_amax_dL_dY.fill_(tensor_to_amax(go))
 
         res = to_fp8_no_autograd(
-            go, fp8_scale_dL_dY, e5m2_dtype, mm_config=ctx.mm_config
+            go,
+            fp8_scale_dL_dY,
+            e5m2_dtype,
+            mm_config=ctx.mm_config,
+            scaling_strategy=ctx.scaling_strategy,
         )
-        empty_grads = None, None, None, None, None, None
+        empty_grads = None, None, None, None, None, None, None
         return res, *empty_grads
 
 
@@ -150,6 +157,9 @@ def __init__(self, *args, **kwargs):
         self.forward_config = ScaledMMConfig()
         self.backward_config = ScaledMMConfig()
 
+        # Defines the scaling strategy for the forward and backwards pass
+        self.scaling_strategy = ScalingStrategy.TensorWise
+
         # Note: is_amax_initialized is not a buffer to avoid data dependent
         # control flow visible to dynamo
         # TODO(future PR): add serialization for this flag
@@ -288,6 +298,7 @@ def cast_y_to_float8_in_bw(self, y: torch.Tensor) -> torch.Tensor:
             scale_fn_name,
             self.is_amax_initialized,
             self.backward_config,
+            self.scaling_strategy,
         )
         return y
 
@@ -353,4 +364,6 @@ def from_float(cls, mod, emulate: bool = False):
         new_mod.backward_config = ScaledMMConfig(
             emulate, False, False, config.pad_inner_dim
         )
+        # TODO: For now hardcode TensorWise scaling
+        new_mod.scaling_strategy = ScalingStrategy.TensorWise
         return new_mod

float8_experimental/float8_ops.py

@@ -50,7 +50,11 @@ def decorator(func):
 def float8_desugar_op(aten_op, args, kwargs=None):
     new_data = aten_op(args[0]._data, *args[1:], **kwargs)
     return Float8Tensor(
-        new_data, args[0]._scale, args[0]._orig_dtype, args[0]._mm_config
+        new_data,
+        args[0]._scale,
+        args[0]._orig_dtype,
+        args[0]._mm_config,
+        args[0]._scaling_strategy,
     )
 
 
@@ -60,7 +64,11 @@ def float8_split(aten_op, args, kwargs=None):
 
     def make_float8(data):
         return Float8Tensor(
-            data, args[0]._scale, args[0]._orig_dtype, args[0]._mm_config
+            data,
+            args[0]._scale,
+            args[0]._orig_dtype,
+            args[0]._mm_config,
+            args[0]._scaling_strategy,
         )
 
     out = map(make_float8, new_data_tensors)
@@ -75,6 +83,7 @@ def float8_cat(aten_op, args, kwargs=None):
     orig_dtype = chunked_tensors[0]._orig_dtype
     scale = chunked_tensors[0]._scale
     mm_config = chunked_tensors[0]._mm_config
+    scaling_strategy = chunked_tensors[0]._scaling_strategy
     fp8_dtype = chunked_tensors[0]._data.dtype
     chunk_data = []
     for chunk in chunked_tensors:
@@ -93,11 +102,14 @@ def float8_cat(aten_op, args, kwargs=None):
         assert (
             chunk._data.dtype == fp8_dtype
         ), "Expecting all chunks to be of the same dtype as a result of a split"
+        assert (
+            chunk._scaling_strategy is scaling_strategy
+        ), "Expecting all chunks to have thee same scaling strategy as a result of a split"
         chunk_data.append(chunk._data.view(torch.uint8))
 
     new_data = aten_op(chunk_data, *args[1:], **kwargs)
     new_data = new_data.view(fp8_dtype)
-    return Float8Tensor(new_data, scale, orig_dtype, mm_config)
+    return Float8Tensor(new_data, scale, orig_dtype, mm_config, scaling_strategy)
 
 
 @implements([aten.sum.dim_IntList])
@@ -162,6 +174,11 @@ def float8_mm(aten_op, args, kwargs=None):
         return torch.ops.aten.mm_float8_emulated(
             a._data, a._scale, b._data, b._scale, output_dtype
         )
+    scaling_strategy = a._scaling_strategy
+    # TODO We can enable this by broadcasting to the more generic form
+    assert (
+        scaling_strategy == b._scaling_strategy
+    ), "Scaling strategy are currently required to be the same"
     tensor_out = addmm_float8_unwrapped(
         a_data,
         a_scale,
@@ -191,6 +208,11 @@ def float8_addmm(aten_op, args, kwargs=None):
     a_mm_config: ScaledMMConfig = a._mm_config
     b_mm_config: ScaledMMConfig = b._mm_config
     mm_config: ScaledMMConfig = merge_mm_configs(a_mm_config, b_mm_config)
+    scaling_strategy = a._scaling_strategy
+    # TODO We can enable this by broadcasting to the more generic form
+    assert (
+        scaling_strategy == b._scaling_strategy
+    ), "Scaling strategy are currently required to be the same"
     if mm_config.emulate:
         out = torch.ops.aten.mm_float8_emulated(
             a._data, a._scale, b._data, b._scale, output_dtype
@@ -229,7 +251,11 @@ def autocast_to_copy(aten_op, args, kwargs=None):
         torch.bfloat16,
     }, "Only support floating point conversion for autocast w/ Float8Tensor"
     return Float8Tensor(
-        args[0]._data, args[0]._scale, kwargs["dtype"], args[0]._mm_config
+        args[0]._data,
+        args[0]._scale,
+        kwargs["dtype"],
+        args[0]._mm_config,
+        args[0]._scaling_strategy,
     )
 
 
@@ -252,7 +278,11 @@ def allgather_fp8(aten_op, args, kwargs=None):
     fp8_data = fp8_data.contiguous()
     fp8_out = aten_op(fp8_data, *args[1:], **kwargs)
     return Float8Tensor(
-        fp8_out, fp8_input._scale, fp8_input._orig_dtype, fp8_input._mm_config
+        fp8_out,
+        fp8_input._scale,
+        fp8_input._orig_dtype,
+        fp8_input._mm_config,
+        fp8_input._scaling_strategy,
     )
 
 
@@ -264,7 +294,11 @@ def wait_tensor_fp8(aten_op, args, kwargs=None):
     fp8_data = fp8_input._data
     fp8_out = aten_op(fp8_data, *args[1:], **kwargs)
     return Float8Tensor(
-        fp8_out, fp8_input._scale, fp8_input._orig_dtype, fp8_input._mm_config
+        fp8_out,
+        fp8_input._scale,
+        fp8_input._orig_dtype,
+        fp8_input._mm_config,
+        fp8_input._scaling_strategy,
     )
 
 
@@ -282,7 +316,11 @@ def index_put_fp8(aten_op, args, kwargs=None):
     fp8_values_data = fp8_values._data
     fp8_out = aten_op(fp8_data, args[1], fp8_values_data, *args[3:], **kwargs)
     return Float8Tensor(
-        fp8_out, fp8_self._scale, fp8_self._orig_dtype, fp8_self._mm_config
+        fp8_out,
+        fp8_self._scale,
+        fp8_self._orig_dtype,
+        fp8_self._mm_config,
+        fp8_self._scaling_strategy,
     )
 
 
@@ -315,6 +353,12 @@ def copy_fp8(aten_op, args, kwargs=None):
             self._data.dtype == src._data.dtype
         ), "Expecting both Float8Tensors to be of the same dtypet"
         fp8_out = aten_op(self._data, src._data, *args[2:], **kwargs)
-        return Float8Tensor(fp8_out, self._scale, self._orig_dtype, self._mm_config)
+        return Float8Tensor(
+            fp8_out,
+            self._scale,
+            self._orig_dtype,
+            self._mm_config,
+            self._scaling_strategy,
+        )
     else:
         raise RuntimeError("Unsupported semantics for copy_ in Float8Tensor")