QOL improvements to benchmarks/profile_linear_float8.py (#281)

Summary:
Pull Request resolved: #281

Cleaning up this script in preparation of adding some more comprehensive
benchmarking to target commonly occuring fusions.

Reviewed By: drisspg

Differential Revision: D59163494

fbshipit-source-id: 4b4bd21a5b65e0704d360e6f79d24142a9e35ad4

Author: vkuzo

benchmarks/profile_linear_float8.py

@@ -3,6 +3,8 @@
 #
 # This source code is licensed under the BSD 3-Clause license found in the
 # LICENSE file in the root directory of this source tree.
+
+import copy
 import random
 from contextlib import nullcontext
 from dataclasses import dataclass, field
@@ -12,15 +14,30 @@
 import fire
 
 import torch
+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,
     linear_requires_sync,
     LinearType,
+    swap_linear_with_float8_linear,
     sync_float8_amax_and_scale_history,
 )
 from torch.profiler import profile, ProfilerActivity, record_function
 
 
+class LNLinear(torch.nn.Module):
+    def __init__(self, fc_dim1, fc_dim2):
+        super().__init__()
+        self.ln = torch.nn.LayerNorm(fc_dim1, elementwise_affine=False)
+        self.fc = torch.nn.Linear(fc_dim1, fc_dim2, bias=False)
+
+    def forward(self, x):
+        x = self.ln(x)
+        x = self.fc(x)
+        return x
+
+
 @dataclass
 class ProfileConfig:
     file_path: Optional[str] = None
@@ -77,65 +94,58 @@ def profile_function(
 
 
 @dataclass(frozen=True)
-class LinearParams:
+class ModelParams:
     M: int
     K: int
     N: int
-    input_bias: bool
     ref_dtype: torch.dtype
     layer_norm: bool = True
-    torch_compile: Optional[bool] = False
 
 
-def main(profile_path: Path, compile: bool, linear_type: str):
-    profile_path = Path(profile_path)
-    assert profile_path.is_dir(), f"Path {profile_path} must be a directory"
-    params = LinearParams(
+def main(
+    profile_path_prefix: Path,
+    compile: bool = True,
+    linear_type: str = "dynamic",
+    use_layer_norm: bool = False,
+):
+    params = ModelParams(
         M=4 * 4096,
         K=8192,
         N=7168,
-        input_bias=False,
         ref_dtype=torch.bfloat16,
-        layer_norm=True,
-        torch_compile=compile,
+        layer_norm=use_layer_norm,
     )
     print(f"Compile is set to          | {compile}")
     print(f"Using Linear type:         | {linear_type}")
     print(f"Use layer norm is set to   | {params.layer_norm}")
-    linear_ref = torch.nn.Linear(
-        params.K,
-        params.N,
-        bias=params.input_bias,
-        device="cuda",
-        dtype=params.ref_dtype,
-    )
+
+    device = "cuda"
+    if params.layer_norm:
+        m_ref = LNLinear(params.K, params.N)
+    else:
+        m_ref = torch.nn.Sequential(
+            torch.nn.Linear(params.K, params.N, bias=False),
+        )
+    m_ref = m_ref.to(device).to(params.ref_dtype)
+
     linear_type = LinearType[linear_type.upper()]
-    linear_float8 = get_float8_linear(linear_type, linear_ref)
+    linear_cls = (
+        Float8Linear if linear_type is LinearType.DELAYED else Float8DynamicLinear
+    )
+
+    m_float8 = copy.deepcopy(m_ref)
+    swap_linear_with_float8_linear(m_float8, linear_cls)
 
     input_tensor = torch.randn(
         params.M, params.K, device="cuda", dtype=params.ref_dtype, requires_grad=True
     )
 
-    if params.layer_norm:
-        ln = torch.nn.LayerNorm(
-            params.K, elementwise_affine=False, device="cuda", dtype=params.ref_dtype
-        )
-
     def ref_forw_backward(x):
-        if params.layer_norm:
-            with record_function("layer_norm"):
-                x = ln(x)
-        with record_function("forward"):
-            out = linear_ref(x)
-        with record_function("backward"):
-            out.sum().backward()
+        out = m_ref(x)
+        out.sum().backward()
 
-    def float8_forw_backward(x):
-        if params.layer_norm:
-            with record_function("layer_norm"):
-                x = ln(x)
-        with record_function("forward"):
-            out = linear_float8(x)
+    def float8_forw(x):
+        out = m_float8(x)
         return out
 
     def float8_forw_backward_wrapper(x):
@@ -146,34 +156,34 @@ def float8_forw_backward_wrapper(x):
         # TODO(future): make this better
         if linear_requires_sync(linear_type):
             with record_function("scale_amax_and_scales"):
-                sync_float8_amax_and_scale_history(linear_float8)
-        out = float8_forw_backward(x)
+                sync_float8_amax_and_scale_history(m_float8)
+        out = float8_forw(x)
 
         # out.sum().backward() is also not torch.compile fullgraph
         # friendly
         with record_function("backward"):
             out.sum().backward()
 
-    if params.torch_compile:
+    if compile:
         ref_forw_backward = torch.compile(ref_forw_backward)
-        float8_forw_backward = torch.compile(float8_forw_backward, fullgraph=True)
+        float8_forw = torch.compile(float8_forw, fullgraph=True)
 
     for _ in range(5):
         ref_forw_backward(input_tensor)
         float8_forw_backward_wrapper(input_tensor)
 
-    # Profile Reference Linear
-    ref_string = f"linear_ref_dtype_{params.ref_dtype}_M_{params.M}_K_{params.K}_N_{params.N}_input_bias_{params.input_bias}_compile_{params.torch_compile}.json"
+    # Profile Reference Model
+    ref_suffix = f"_ref_compile_{compile}.json"
     profile_config = ProfileConfig(
-        str(profile_path / ref_string), ref_string, iters=5, warmup_iters=5, sync=True
+        profile_path_prefix + ref_suffix, ref_suffix, iters=5, warmup_iters=5, sync=True
     )
     profile_function(profile_config, ref_forw_backward, input_tensor)
 
-    # # Profile Float8 Linear
-    float8_string = f"linear_float8_M_{params.M}_K_{params.K}_N_{params.N}_input_bias_{params.input_bias}_compile_{params.torch_compile}_{linear_type}.json"
+    # Profile Float8 Model
+    float8_suffix = f"_float8_compile_{compile}_{linear_type}.json"
     profile_config = ProfileConfig(
-        str(profile_path / float8_string),
-        float8_string,
+        profile_path_prefix + float8_suffix,
+        float8_suffix,
         iters=5,
         warmup_iters=5,
         sync=True,
@@ -182,7 +192,7 @@ def float8_forw_backward_wrapper(x):
 
 
 def invoke_main() -> None:
-    # Example usage: python benchmarks/profile_linear_float8.py benchmarks/data/profiles --compile=True --linear_type="dynamic"
+    # Example usage: python benchmarks/profile_linear_float8.py benchmarks/data/profiles/current_profile --compile=True --linear_type="dynamic"
     fire.Fire(main)