Allow for modifying the scaled_mm compute (#144)

Summary:
This does two things:
1.  Creates a new named_tuple type `ScaledMMConfig` that is used to control the behavior of the scaled_mm op. This includes, emulate, fast_accumulation, and fp8_out_dtype(the latter is not currently used).  It replaces the emulate arg and strings it through all the relevant infra, and updates test accordingly.
2. This adds the fp8 fast accum mode and enables it for the forward path and not the backward pass.

### Performance
With settings use_fast_accum in the forward using the linear_float8 benchmark:

![image](https://github.com/pytorch-labs/float8_experimental/assets/32754868/8510814e-88d0-402c-9676-d4afe8fef2a0)

|    | shape               |   Speedup_with_False |   Speedup_with_True |   Percentage_Gain |
|---:|:--------------------|---------------------:|--------------------:|------------------:|
|  0 | (16384, 1024, 8192) |             1.19086  |            1.26397  |           6.13912 |
|  1 | (16384, 3584, 8192) |             1.42227  |            1.48921  |           4.70629 |
|  2 | (16384, 8192, 1280) |             0.970685 |            0.986167 |           1.59497 |
|  3 | (16384, 8192, 7168) |             1.50755  |            1.54886  |           2.74022 |

Pull Request resolved: #144

Reviewed By: vkuzo

Differential Revision: D55906764

Pulled By: drisspg

fbshipit-source-id: c6c7f7d5f7831bc594c8e70c71d9ab0e0c90755c

Author: drisspg

README.md

@@ -90,13 +90,17 @@ for _ in range(N_ITER):
     optimizer.step()
 ```
 
-# code tips
+# 🧭 Code Organization
 
-* `float8_experimental/float8_linear.py` - `Float8Linear` (main user facing entry point for delayed scaling)
-* `float8_experimental/float8_dynamic_linear.py` - `Float8DynamicLinear` (main user facing entry point for dynamic scaling)
-* `float8_experimental/float8_tensor.py` - `Float8Tensor`, which allows `Float8Linear` to abide by the `x.dtype == x.grad.dtype` restriction
+* `float8_experimental/float8_linear.py`
+    - `Float8Linear` (main user facing entry point for delayed scaling)
+* `float8_experimental/float8_dynamic_linear.py`
+    - `Float8DynamicLinear` (main user facing entry point for dynamic scaling)
+* `float8_experimental/float8_tensor.py`
+    - `Float8Tensor`, which allows `Float8Linear` to abide by the `x.dtype == x.grad.dtype` restriction
+    - `ScaledMMConfig` defines the semantics for matmul in the forward and backwards pass
 
-# testing
+# Testing
 
 ```bash
 # run single-GPU unit tests
@@ -117,7 +121,7 @@ pytest test/test_compile.py
 ./test/run_everything.sh
 ```
 
-# benchmarking
+# Benchmarking
 
 ```bash
 # benchmark the torch._scaled_mm function on LLaMa 2 70B shapes
@@ -130,4 +134,3 @@ pytest test/test_compile.py
 
 # License
 PyTorch has a BSD 3-Clause License, as found in the LICENSE file.
-

benchmarks/bench_linear_float8.py

@@ -16,6 +16,7 @@
 import torch.utils.benchmark as benchmark
 from float8_experimental.float8_linear import Float8Linear
 from float8_experimental.float8_linear_utils import sync_float8_amax_and_scale_history
+from float8_experimental.float8_tensor import ScaledMMConfig
 from tqdm import tqdm
 
 # estimating TOPs for matmuls in fp32, fp16, fp8
@@ -54,8 +55,8 @@ class Experiment:
     ref_time_sec: float
     float8_time_sec: float
     dtype: torch.dtype
-    compiled: bool = False
-    float_8_dtype: Optional[torch.dtype] = torch.float8_e4m3fn
+    compiled: bool
+    use_fast_accum: bool
 
     # 3 Times since we are calculating forward backward
     @property
@@ -74,7 +75,7 @@ def float8_tops_sec(self):
 
     @property
     def float8_pct_top_peak(self):
-        return self.float8_tops_sec / dtype_to_peak_tops[self.float_8_dtype]
+        return self.float8_tops_sec / dtype_to_peak_tops[torch.float8_e4m3fn]
 
 
 def main(
@@ -95,9 +96,10 @@ def main(
     }
     input_bias = False
     ref_dtypes = [torch.bfloat16, torch.float16]
+    use_fast_accum = [True, False]
     experiment_list: List[Experiment] = []
-    for idx, (dtype, (name, (K, N))) in enumerate(
-        tqdm(list(product(ref_dtypes, name_to_shapes_70b.items())))
+    for idx, (dtype, fast_accum, (name, (K, N))) in enumerate(
+        tqdm(list(product(ref_dtypes, use_fast_accum, name_to_shapes_70b.items())))
     ):
         if n_limit is not None and idx >= n_limit:
             break
@@ -108,6 +110,10 @@ def main(
         linear_float8 = Float8Linear.from_float(
             copy.deepcopy(linear_ref), emulate=False
         )
+        if fast_accum:
+            linear_float8.forward_config = ScaledMMConfig(False, True, False)
+        else:
+            linear_float8.forward_config = ScaledMMConfig(False, False, False)
 
         bsz, seq_len = 4, 4096
         M = bsz * seq_len
@@ -155,6 +161,7 @@ def wrapper(*args, **kwargs):
             float8_time,
             dtype,
             compile,
+            use_fast_accum=fast_accum,
         )
         print(experiment)
         print("float8 speedup", experiment.ref_time_sec / experiment.float8_time_sec)
@@ -168,7 +175,7 @@ def wrapper(*args, **kwargs):
         "N",
         "ref_dtype",
         "compiled",
-        "fp8_dtype",
+        "use_fast_accum",
         "ref_time_sec",
         "pt_fp8_time_sec",
         "ref_tops_sec",
@@ -186,7 +193,7 @@ def wrapper(*args, **kwargs):
                 experiment.shape[2],
                 experiment.dtype,
                 experiment.compiled,
-                experiment.float_8_dtype,
+                experiment.use_fast_accum,
                 experiment.ref_time_sec,
                 experiment.float8_time_sec,
                 experiment.ref_tops_sec,
@@ -214,6 +221,7 @@ def wrapper(*args, **kwargs):
             "shape",
             "ref_dtype",
             "compiled",
+            "use_fast_accum",
             "ref_time_sec",
             "pt_fp8_time_sec",
             "pt_fp8_speedup",

float8_experimental/float8_dynamic_linear.py

@@ -10,6 +10,7 @@
 
 from float8_experimental.float8_tensor import (
     Float8Tensor,
+    ScaledMMConfig,
     tensor_already_casted_to_fp8,
     to_fp8_no_autograd,
 )
@@ -27,9 +28,9 @@ class NoopFwToFloat8E5M2Bw(torch.autograd.Function):
     def forward(
         ctx,
         tensor,
-        emulate: bool,
+        mm_config: ScaledMMConfig,
     ):
-        ctx.emulate = emulate
+        ctx.mm_config = mm_config
         return tensor
 
     @staticmethod
@@ -39,7 +40,7 @@ def backward(ctx, gradY):
             return gradY, None
         gradY_scale = tensor_to_scale(gradY, torch.float8_e5m2)
         fp8_tensor = to_fp8_no_autograd(
-            gradY, gradY_scale, torch.float8_e5m2, ctx.emulate
+            gradY, gradY_scale, torch.float8_e5m2, mm_config=ctx.mm_config
         )
         return fp8_tensor, None
 
@@ -73,11 +74,11 @@ def cast_to_float8_e4m3fn(self, inpt_tensor: torch.Tensor) -> Float8Tensor:
             return inpt_tensor
         scale = tensor_to_scale(inpt_tensor, torch.float8_e4m3fn)
         return Float8Tensor.to_float8(
-            inpt_tensor, scale, torch.float8_e4m3fn, emulate=self.emulate
+            inpt_tensor, scale, torch.float8_e4m3fn, mm_config=self.forward_config
         )
 
     def cast_to_float8_e5m2_bw(self, gradY: torch.Tensor) -> torch.Tensor:
-        return NoopFwToFloat8E5M2Bw.apply(gradY, self.emulate)
+        return NoopFwToFloat8E5M2Bw.apply(gradY, self.backward_config)
 
     @classmethod
     def from_float(cls, mod, emulate: bool = False) -> "Float8DynamicLinear":
@@ -97,5 +98,6 @@ def from_float(cls, mod, emulate: bool = False) -> "Float8DynamicLinear":
             new_mod = cls(**super_kwargs)
         new_mod.weight = mod.weight
         new_mod.bias = mod.bias
-        new_mod.emulate = emulate
+        new_mod.forward_config = ScaledMMConfig(emulate, True if not emulate else False)
+        new_mod.backward_config = ScaledMMConfig(emulate, False)
         return new_mod

float8_experimental/float8_linear.py

@@ -20,7 +20,11 @@
 
 import torch
 
-from float8_experimental.float8_tensor import Float8Tensor, to_fp8_no_autograd
+from float8_experimental.float8_tensor import (
+    Float8Tensor,
+    ScaledMMConfig,
+    to_fp8_no_autograd,
+)
 
 from float8_experimental.float8_utils import (
     amax_history_to_scale,
@@ -73,12 +77,12 @@ def forward(
         fp8_scale_dL_dY,
         scale_fn_name,
         is_amax_initialized,
-        emulate: bool,
+        mm_config: ScaledMMConfig,
     ):
         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.emulate = emulate
+        ctx.mm_config = mm_config
         return tensor
 
     @staticmethod
@@ -99,7 +103,9 @@ def backward(ctx, go):
 
         fp8_amax_dL_dY.fill_(tensor_to_amax(go))
 
-        res = to_fp8_no_autograd(go, fp8_scale_dL_dY, torch.float8_e5m2, ctx.emulate)
+        res = to_fp8_no_autograd(
+            go, fp8_scale_dL_dY, torch.float8_e5m2, mm_config=ctx.mm_config
+        )
         empty_grads = None, None, None, None, None, None
         return res, *empty_grads
 
@@ -154,8 +160,9 @@ def __init__(self, *args, **kwargs):
         )
         self.register_always_float32_buffer("fp8_scale_dL_dY", torch.tensor([1.0]))
 
-        # Whether to emulate the fp8 matmul logic in float32
-        self.emulate = False
+        # Defines the behavior of the matmul in the forward and backward pass
+        self.forward_config = ScaledMMConfig()
+        self.backward_config = ScaledMMConfig()
 
         # Note: is_amax_initialized is not a buffer to avoid data dependent
         # control flow visible to dynamo
@@ -216,7 +223,11 @@ def cast_x_to_float8(
             is_amax_initialized,
         )
         x_fp8 = Float8Tensor.to_float8(
-            x, self.fp8_scale_x, torch.float8_e4m3fn, self.fp8_amax_x, self.emulate
+            x,
+            self.fp8_scale_x,
+            torch.float8_e4m3fn,
+            self.fp8_amax_x,
+            self.forward_config,
         )
         return x_fp8
 
@@ -239,13 +250,11 @@ def cast_w_to_float8(
             self.fp8_scale_w,
             torch.float8_e4m3fn,
             self.fp8_amax_w,
-            self.emulate,
+            self.forward_config,
         )
         return w_fp8
 
-    def cast_y_to_float8_in_bw(
-        self, y: torch.Tensor, emulate: bool = False
-    ) -> torch.Tensor:
+    def cast_y_to_float8_in_bw(self, y: torch.Tensor) -> torch.Tensor:
         scale_fn_name = self.recipe.scale_fn_name
         y = NoopFwToFloat8E5M2Bw.apply(
             y,
@@ -254,7 +263,7 @@ def cast_y_to_float8_in_bw(
             self.fp8_scale_dL_dY,
             scale_fn_name,
             self.is_amax_initialized,
-            emulate,
+            self.backward_config,
         )
         return y
 
@@ -295,7 +304,7 @@ def forward(self, x):
         y = torch.matmul(x_fp8, w_fp8.t())
 
         # Cast gradY to float8_e5m2 during backward
-        y = self.cast_y_to_float8_in_bw(y, self.emulate)
+        y = self.cast_y_to_float8_in_bw(y)
 
         if self.bias is not None:
             y = y + self.bias.to(y.dtype)
@@ -318,7 +327,12 @@ def from_float(cls, mod, emulate: bool = False):
         new_mod = cls(mod.in_features, mod.out_features, bias=False)
         new_mod.weight = mod.weight
         new_mod.bias = mod.bias
-        new_mod.emulate = emulate
+
+        # Defines the behavior of the matmul in the forward and backward
+        # Forward we use fast_accum, backwards we do not
+        new_mod.forward_config = ScaledMMConfig(emulate, True if not emulate else False)
+        new_mod.backward_config = ScaledMMConfig(emulate, False)
+
         # I think its okay to send all params and buffers to device
         new_mod.to(mod.weight.device)
         return new_mod

float8_experimental/float8_ops.py

@@ -8,7 +8,11 @@
 import torch
 
 from float8_experimental.float8_python_api import addmm_float8_unwrapped
-from float8_experimental.float8_tensor import Float8Tensor
+from float8_experimental.float8_tensor import (
+    Float8Tensor,
+    merge_mm_configs,
+    ScaledMMConfig,
+)
 from float8_experimental.float8_utils import is_row_major
 from torch.utils._pytree import tree_map
 
@@ -41,7 +45,9 @@ 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]._emulate)
+    return Float8Tensor(
+        new_data, args[0]._scale, args[0]._orig_dtype, args[0]._mm_config
+    )
 
 
 @implements([aten.sum.dim_IntList])
@@ -89,13 +95,22 @@ def float8_mm(aten_op, args, kwargs=None):
     )
     a_data, a_scale, b_data, b_scale = preprocess_addmm(a, b)
     output_dtype = a._orig_dtype
-    if a._emulate:
-        assert a._emulate == b._emulate
+    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)
+    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(
-        a_data, a_scale, b_data, b_scale, output_dtype, output_scale=None, bias=None
+        a_data,
+        a_scale,
+        b_data,
+        b_scale,
+        output_dtype,
+        output_scale=None,
+        bias=None,
+        use_fast_accum=mm_config.use_fast_accum,
     )
     return tensor_out
 
@@ -113,14 +128,23 @@ def float8_addmm(aten_op, args, kwargs=None):
     a_data, a_scale, b_data, b_scale = preprocess_addmm(a, b)
     output_dtype = a._orig_dtype
     assert bias.dtype == output_dtype, "bias dtype must match output dtype"
-    if a._emulate:
-        assert a._emulate == b._emulate
+    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)
+    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(
-        a_data, a_scale, b_data, b_scale, output_dtype, output_scale=None, bias=bias
+        a_data,
+        a_scale,
+        b_data,
+        b_scale,
+        output_dtype,
+        output_scale=None,
+        bias=bias,
+        use_fast_accum=mm_config.use_fast_accum,
     )
     return tensor_out
 
@@ -145,7 +169,7 @@ 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]._emulate
+        args[0]._data, args[0]._scale, kwargs["dtype"], args[0]._mm_config
     )
 
 
@@ -170,7 +194,7 @@ def allgather_fp8(aten_op, args, kwargs=None):
     fp8_out = aten_op(fp8_data, *args[1:], **kwargs)
     fp8_out = fp8_out.view(fp8_input._data.dtype)
     return Float8Tensor(
-        fp8_out, fp8_input._scale, fp8_input._orig_dtype, fp8_input._emulate
+        fp8_out, fp8_input._scale, fp8_input._orig_dtype, fp8_input._mm_config
     )
 
 
@@ -182,5 +206,5 @@ 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._emulate
+        fp8_out, fp8_input._scale, fp8_input._orig_dtype, fp8_input._mm_config
     )