[bc breaking] change x, w, dL_dY variable names to input, weight, gra…

…d_output

Summary:

The following naming scheme matches the rest of PyTorch better:

```
// forward
output = input @ weight_t
// backward
grad_input = grad_output @ weight
grad_weight = input_t @ grad_output
```

This PR changes all the previous references to `x`, `w`, `dL_dY` to
match the naming scheme above.

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

ghstack-source-id: 52683b06c816b8ab1cdef5142d5a7eaea1a9e0f2
Pull Request resolved: #323

.github/workflows/ufmt.yml

@@ -23,4 +23,7 @@ jobs:
         pip install black==23.3.0 usort==1.0.6 ufmt==2.1.0 libcst==1.0.1
     - name: Analyzing the code with ufmt
       run: |
+        ufmt format .
+        git diff
+        git restore .
         ufmt check .

README.md

@@ -28,9 +28,9 @@ pip install -e ".[dev]"
 
 # Single GPU User API
 
-We provide two per-tensor scaling strategies: dynamic and delayed.  See https://arxiv.org/pdf/2209.05433.pdf, Section 4.3 for more details. These strategies are configurable separately for activations (`x`), weights (`w`) and gradients (`dL_dY`).
+We provide two per-tensor scaling strategies: dynamic and delayed.  See https://arxiv.org/pdf/2209.05433.pdf, Section 4.3 for more details. These strategies are configurable separately for activations (`input`), weights (`weight`) and gradients (`grad_output`).
 
-## float8 linear with dynamic scaling for `x`, `w` and `dL_dY`
+## float8 linear with dynamic scaling for `input`, `weight` and `grad_output`
 
 This is the most accurate recipe as every tensor is scaled dynamically.
 
@@ -95,9 +95,9 @@ m = Model(...)
 # type
 swap_linear_with_float8_linear(
     m,
-    scaling_type_x=TensorScalingType.DELAYED,
-    scaling_type_w=TensorScalingType.DELAYED,
-    scaling_type_dL_dY=TensorScalingType.DELAYED,
+    scaling_type_input=TensorScalingType.DELAYED,
+    scaling_type_weight=TensorScalingType.DELAYED,
+    scaling_type_grad_output=TensorScalingType.DELAYED,
 )
 
 # optional: use FSDP. Note that workarounds gated with config.enable_amax_init and

benchmarks/bench_linear_float8.py

@@ -95,16 +95,16 @@ def main(
     n_limit: Optional[int] = None,
     fast_accum_filter: Optional[bool] = None,
     shape_name_filter: Optional[str] = None,
-    scaling_type_x: str = "dynamic",
-    scaling_type_w: str = "dynamic",
-    scaling_type_dL_dY: str = "dynamic",
+    scaling_type_input: str = "dynamic",
+    scaling_type_weight: str = "dynamic",
+    scaling_type_grad_output: str = "dynamic",
 ):
     device = "cuda"
     print(f"Compile is set to             | {compile}")
 
-    scaling_type_x = TensorScalingType(scaling_type_x)
-    scaling_type_w = TensorScalingType(scaling_type_w)
-    scaling_type_dL_dY = TensorScalingType(scaling_type_dL_dY)
+    scaling_type_input = TensorScalingType(scaling_type_input)
+    scaling_type_weight = TensorScalingType(scaling_type_weight)
+    scaling_type_grad_output = TensorScalingType(scaling_type_grad_output)
 
     # LLaMa 2 70B single-node weight shapes
     # assumes fused attn.wqkv and ffn.w13
@@ -136,9 +136,9 @@ def main(
         linear_float8 = Float8Linear.from_float(
             copy.deepcopy(linear_ref),
             emulate=False,
-            scaling_type_x=scaling_type_x,
-            scaling_type_w=scaling_type_w,
-            scaling_type_dL_dY=scaling_type_dL_dY,
+            scaling_type_input=scaling_type_input,
+            scaling_type_weight=scaling_type_weight,
+            scaling_type_grad_output=scaling_type_grad_output,
         )
         scaling_repr = linear_float8.scaling_repr()
 
@@ -153,7 +153,9 @@ def main(
         ref_forw_backward = lambda: linear_ref(input_tensor).sum().backward()
 
         def float8_forw_backward():
-            if linear_requires_sync(scaling_type_x, scaling_type_w, scaling_type_dL_dY):
+            if linear_requires_sync(
+                scaling_type_input, scaling_type_weight, scaling_type_grad_output
+            ):
                 sync_float8_amax_and_scale_history(linear_float8)
             linear_float8(input_tensor).sum().backward()
 
@@ -278,18 +280,18 @@ def invoke_main() -> None:
     parser.add_argument("-n", "--n_limit", type=int, required=False)
     parser.add_argument("--fast_accum_filter", type=bool, required=False)
     parser.add_argument("--shape_name_filter", type=str, required=False)
-    parser.add_argument("--scaling_type_x", type=str, required=False)
-    parser.add_argument("--scaling_type_w", type=str, required=False)
-    parser.add_argument("--scaling_type_dL_dY", type=str, required=False)
+    parser.add_argument("--scaling_type_input", type=str, required=False)
+    parser.add_argument("--scaling_type_weight", type=str, required=False)
+    parser.add_argument("--scaling_type_grad_output", type=str, required=False)
     args = parser.parse_args()
     output_path = Path(args.output_path) if args.output_path is not None else None
     kwargs = {}
-    if args.scaling_type_x is not None:
-        kwargs["scaling_type_x"] = args.scaling_type_x
-    if args.scaling_type_w is not None:
-        kwargs["scaling_type_w"] = args.scaling_type_w
-    if args.scaling_type_dL_dY is not None:
-        kwargs["scaling_type_dL_dY"] = args.scaling_type_dL_dY
+    if args.scaling_type_input is not None:
+        kwargs["scaling_type_input"] = args.scaling_type_input
+    if args.scaling_type_weight is not None:
+        kwargs["scaling_type_weight"] = args.scaling_type_weight
+    if args.scaling_type_grad_output is not None:
+        kwargs["scaling_type_grad_output"] = args.scaling_type_grad_output
     main(
         output_path,
         not args.disable_compile,

benchmarks/bench_multi_gpu.py

@@ -68,9 +68,9 @@ def get_model(K, N, is_fp8, base_dtype=torch.float32):
         swap_linear_with_float8_linear(
             m,
             emulate=False,
-            scaling_type_x=TensorScalingType.DELAYED,
-            scaling_type_w=TensorScalingType.DELAYED,
-            scaling_type_dL_dY=TensorScalingType.DELAYED,
+            scaling_type_input=TensorScalingType.DELAYED,
+            scaling_type_weight=TensorScalingType.DELAYED,
+            scaling_type_grad_output=TensorScalingType.DELAYED,
         )
     return m
 

benchmarks/profile_linear_float8.py

@@ -204,20 +204,23 @@ def profile_function(
 def main(
     profile_path_prefix: Path,
     compile: bool = True,
-    scaling_type_x: str = "dynamic",
-    scaling_type_w: str = "dynamic",
-    scaling_type_dL_dY: str = "dynamic",
+    scaling_type_input: str = "dynamic",
+    scaling_type_weight: str = "dynamic",
+    scaling_type_grad_output: str = "dynamic",
     model_type: str = "linear",
     dtype_filter: str = "both",
 ):
     assert model_type in ("linear", "ln_linear", "norm_ffn_norm"), "unsupported"
     assert dtype_filter in ("both", "float8", "bfloat16")
 
-    scaling_type_x = TensorScalingType(scaling_type_x)
-    scaling_type_w = TensorScalingType(scaling_type_w)
-    scaling_type_dL_dY = TensorScalingType(scaling_type_dL_dY)
+    scaling_type_input = TensorScalingType(scaling_type_input)
+    scaling_type_weight = TensorScalingType(scaling_type_weight)
+    scaling_type_grad_output = TensorScalingType(scaling_type_grad_output)
     scaling_repr = "_".join(
-        [s.short_str() for s in (scaling_type_x, scaling_type_w, scaling_type_dL_dY)]
+        [
+            s.short_str()
+            for s in (scaling_type_input, scaling_type_weight, scaling_type_grad_output)
+        ]
     )
 
     print(f"Compile is set to       | {compile}")
@@ -254,9 +257,9 @@ def main(
     m_ref = m_ref.to(device).to(ref_dtype)
 
     extra_kwargs = {
-        "scaling_type_x": scaling_type_x,
-        "scaling_type_w": scaling_type_w,
-        "scaling_type_dL_dY": scaling_type_dL_dY,
+        "scaling_type_input": scaling_type_input,
+        "scaling_type_weight": scaling_type_weight,
+        "scaling_type_grad_output": scaling_type_grad_output,
     }
 
     m_float8 = copy.deepcopy(m_ref)
@@ -278,7 +281,9 @@ def float8_forw_backward_wrapper(x):
         # inspection of the fw+bw torch.compile without the scale
         # syncing code
         # TODO(future): make this better
-        if linear_requires_sync(scaling_type_x, scaling_type_w, scaling_type_dL_dY):
+        if linear_requires_sync(
+            scaling_type_input, scaling_type_weight, scaling_type_grad_output
+        ):
             with record_function("scale_amax_and_scales"):
                 sync_amax_history(m_float8)
         out = float8_forw(x)