Updates with new scaled-mm api (#284)

Summary:
This updates the calls to _scaled_mm to the new signature from this PR: pytorch/pytorch#128683

This is needed to unblock inductor work on scaled_mm.

```Shell
❯ ./test/test_everything.sh
	.
	.
	.

test/test_fsdp2/test_fsdp2_eager.py .......                                   [100%]

================================ 7 passed in 27.66s =================================
all tests successful
```

Pull Request resolved: #284

Reviewed By: y-sq

Differential Revision: D58709092

Pulled By: drisspg

fbshipit-source-id: ab330506621e9240f495be965748066d494d7b50

benchmarks/bench_linear_float8.py

@@ -14,8 +14,6 @@
 
 import torch
 import torch.utils.benchmark as benchmark
-from float8_experimental.float8_dynamic_linear import Float8DynamicLinear
-from float8_experimental.float8_linear import Float8Linear
 from float8_experimental.float8_linear_utils import (
     get_float8_linear,
     LinearType,

benchmarks/bench_matmul.py

@@ -101,7 +101,11 @@ def run(n_limit: Optional[int] = None):
         B = torch.zeros(K, N, device=device, dtype=d2).t().contiguous().t()
 
         def do_matmul(A, B):
-            return torch._scaled_mm(A, B, out_dtype=d3, use_fast_accum=False)
+            scale_a = torch.tensor([1], device=device)
+            scale_b = torch.tensor([1], device=device)
+            return torch._scaled_mm(
+                A, B, scale_a, scale_b, out_dtype=d3, use_fast_accum=False
+            )
 
         fp8_time_sec, fp8_tops_sec, fp8_pct_top_peak = do_benchmarks(
             tops, dtype_to_peak_tops[d1], do_matmul, A, B

float8_experimental/float8_aten_api.py

@@ -10,7 +10,6 @@
 
 import torch
 
-from float8_experimental.float8_utils import tensor_to_amax
 from torch.library import Library
 
 
@@ -26,7 +25,7 @@ def mm_float8_emulated(
     m2_fp32 = m2.float() / s2
     m3_fp32 = torch.mm(m1_fp32, m2_fp32)
 
-    return m3_fp32.to(dtype3), tensor_to_amax(m3_fp32)
+    return m3_fp32.to(dtype3)
 
 
 #
@@ -38,7 +37,7 @@ def mm_float8_emulated(
 lib = Library("aten", "FRAGMENT")
 
 lib.define(
-    "mm_float8_emulated(Tensor m1, Tensor s1, Tensor m2, Tensor s2, ScalarType dtype3) -> (Tensor, Tensor)"
+    "mm_float8_emulated(Tensor m1, Tensor s1, Tensor m2, Tensor s2, ScalarType dtype3) -> Tensor"
 )
 lib.impl("mm_float8_emulated", mm_float8_emulated, "CPU")
 lib.impl("mm_float8_emulated", mm_float8_emulated, "CUDA")
@@ -47,4 +46,4 @@ def mm_float8_emulated(
 @torch.library.impl(lib, "mm_float8_emulated", "Meta")
 def _mm_float8_emulated_meta(m1, s1, m2, s2, dtype3):
     out = torch.mm(m1.float(), m2.float()).to(dtype3)
-    return out, torch.empty(1, device="meta")
+    return out

float8_experimental/float8_ops.py

@@ -147,8 +147,8 @@ def float8_mm(aten_op, args, kwargs=None):
     if mm_config.emulate:
         return torch.ops.aten.mm_float8_emulated(
             a._data, a._scale, b._data, b._scale, output_dtype
-        )[0]
-    tensor_out, amax = addmm_float8_unwrapped(
+        )
+    tensor_out = addmm_float8_unwrapped(
         a_data,
         a_scale,
         b_data,
@@ -180,9 +180,9 @@ def float8_addmm(aten_op, args, kwargs=None):
     if mm_config.emulate:
         out = torch.ops.aten.mm_float8_emulated(
             a._data, a._scale, b._data, b._scale, output_dtype
-        )[0]
+        )
         return out + bias
-    tensor_out, amax = addmm_float8_unwrapped(
+    tensor_out = addmm_float8_unwrapped(
         a_data,
         a_scale,
         b_data,

float8_experimental/float8_python_api.py

@@ -10,7 +10,7 @@
 """
 
 
-from typing import Optional, Tuple
+from typing import Optional
 
 import float8_experimental.float8_aten_api  # noqa
 
@@ -31,7 +31,7 @@ def addmm_float8_unwrapped(
     output_scale: Optional[torch.Tensor] = None,
     bias: Optional[torch.Tensor] = None,
     use_fast_accum: bool = False,
-) -> Tuple[torch.Tensor, torch.Tensor]:
+) -> torch.Tensor:
     """
     This is the unwrapped version of addmm_float8, which does not take in Float8Tensors
     as inputs. This is used to standardize the logic between subclassed and non subclassed
@@ -41,25 +41,25 @@ def addmm_float8_unwrapped(
     b_inverse_scale = b_scale.reciprocal()
     if output_dtype == torch.float32 and bias is not None:
         # Bias is not supported by _scaled_mm when output is fp32
-        output, output_amax = torch._scaled_mm(
+        output = torch._scaled_mm(
             a_data,
             b_data,
-            out_dtype=output_dtype,
             scale_a=a_inverse_scale,
             scale_b=b_inverse_scale,
             scale_result=output_scale,
+            out_dtype=output_dtype,
             use_fast_accum=use_fast_accum,
         )
         output += bias
-        return output, output_amax
-    output, output_amax = torch._scaled_mm(
+        return output
+    output = torch._scaled_mm(
         a_data,
         b_data,
-        bias=bias,
-        out_dtype=output_dtype,
         scale_a=a_inverse_scale,
         scale_b=b_inverse_scale,
+        bias=bias,
         scale_result=output_scale,
+        out_dtype=output_dtype,
         use_fast_accum=use_fast_accum,
     )
-    return output, output_amax
+    return output

test/test_base.py

@@ -29,7 +29,6 @@
     ScaledMMConfig,
 )
 from float8_experimental.float8_utils import (
-    amax_to_scale,
     compute_error,
     fp8_tensor_statistics,
     FP8_TYPES,
@@ -327,29 +326,22 @@ def test_scaled_mm_vs_emulated(self, base_dtype, use_fast_accum):
         a_fp8 = Float8Tensor.to_float8(a, a_scale, input_dtype)
         b_fp8 = Float8Tensor.to_float8(b, b_scale, input_dtype)
 
-        out_scaled_mm, output_amax_scaled = addmm_float8_unwrapped(
+        out_scaled_mm = addmm_float8_unwrapped(
             a_fp8._data,
             a_fp8._scale,
             b_fp8._data,
             b_fp8._scale,
             output_dtype=output_dtype,
             use_fast_accum=use_fast_accum,
         )
-        out_emulated, output_amax_emulated = torch.ops.aten.mm_float8_emulated(
+        out_emulated = torch.ops.aten.mm_float8_emulated(
             a_fp8._data, a_fp8._scale, b_fp8._data, b_fp8._scale, output_dtype
         )
 
         if output_dtype != base_dtype:
             out_scaled_mm = out_scaled_mm.to(compare_type)
             out_emulated = out_emulated.to(compare_type)
 
-            out_scaled_mm = out_scaled_mm / amax_to_scale(
-                output_amax_scaled, input_dtype
-            )
-            out_emulated = out_emulated / amax_to_scale(
-                output_amax_emulated, input_dtype
-            )
-
         if base_dtype in {torch.bfloat16, torch.float16}:
             atol, rtol = 7e-2, 7e-2
         else: