updating license for toolchain

llama_toolchain/inference/quantization/fp8_impls.py

@@ -1,3 +1,9 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described found in the
+# LICENSE file in the root directory of this source tree.
+
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
 
@@ -8,7 +14,7 @@ try:
     import fbgemm_gpu.experimental.gen_ai  # noqa: F401
 
     print("Using efficient FP8 operators in FBGEMM.")
-except (ImportError, ModuleNotFoundError):
+except ImportError:
     print("No efficient FP8 operators. Please install FBGEMM in fp8_requirements.txt.")
     raise
 
@@ -57,8 +63,8 @@ def ffn_swiglu(
             x, w1, w3, w2, w1.activation_scale_ub, num_tokens, is_memory_bounded
         )
 
-    (B, T, D) = x.shape
-    (HD_L, D_) = w1.shape
+    (B, T, D) = x.shape  # noqa: N806
+    (HD_L, D_) = w1.shape  # noqa: N806
     assert D_ == D
 
     assert isinstance(w1, Tensor)
@@ -153,8 +159,8 @@ def ffn_swiglu_fp8_dynamic(
     num_tokens: Optional[Tensor] = None,
     is_memory_bounded: bool = False,
 ) -> Tensor:
-    (B, T, D) = x.shape
-    HD_L = w1.shape[0]
+    (B, T, D) = x.shape  # noqa: N806
+    HD_L = w1.shape[0]  # noqa: N806
     assert HD_L == w3.shape[0]
     x1 = fc_fp8_dynamic(
         x.view(B * T, D),

llama_toolchain/inference/quantization/loader.py

@@ -1,3 +1,9 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described found in the
+# LICENSE file in the root directory of this source tree.
+
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
 
@@ -9,13 +15,13 @@ import torch
 from fairscale.nn.model_parallel.mappings import reduce_from_model_parallel_region
 from llama_models.llama3_1.api.model import Transformer, TransformerBlock
 
-from termcolor import cprint
-
 from llama_toolchain.inference.api.config import (
     CheckpointQuantizationFormat,
     InlineImplConfig,
 )
 from llama_toolchain.inference.api.datatypes import QuantizationType
+
+from termcolor import cprint
 from torch import Tensor
 
 
@@ -24,7 +30,7 @@ def is_fbgemm_available() -> bool:
         import fbgemm_gpu.experimental.gen_ai  # noqa: F401
 
         return True
-    except (ImportError, ModuleNotFoundError):
+    except ImportError:
         return False
 
 

llama_toolchain/inference/quantization/scripts/quantize_checkpoint.py

@@ -1,3 +1,9 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described found in the
+# LICENSE file in the root directory of this source tree.
+
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
 

llama_toolchain/inference/quantization/scripts/run_quantize_checkpoint.sh

@@ -1,5 +1,11 @@
 #!/bin/bash
 
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described found in the
+# LICENSE file in the root directory of this source tree.
+
 set -euo pipefail
 set -x
 

llama_toolchain/inference/quantization/test_fp8.py

@@ -1,3 +1,9 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described found in the
+# LICENSE file in the root directory of this source tree.
+
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
 
@@ -5,7 +11,7 @@ import unittest
 
 import torch
 
-from fp8_impls import ffn_swiglu_fp8_dynamic, quantize_fp8, FfnQuantizeMode
+from fp8_impls import ffn_swiglu_fp8_dynamic, FfnQuantizeMode, quantize_fp8
 from hypothesis import given, settings, strategies as st
 from torch import Tensor
 
@@ -26,29 +32,25 @@ class FP8Tests(unittest.TestCase):
     )
     def test_fp8_ffn(
         self,
-        D: int,
+        D: int,  # noqa
         HD_L: int,
         B: int,
         T: int,
         UB: float,
     ) -> None:
         x = torch.randn(size=(B, T, D), dtype=torch.bfloat16, device="cuda") * 0.1
-        w1 = (
-            torch.randn(size=(HD_L, D), dtype=torch.bfloat16, device="cuda") * 0.01
-        )
-        w3 = (
-            torch.randn(size=(HD_L, D), dtype=torch.bfloat16, device="cuda") * 0.01
-        )
+        w1 = torch.randn(size=(HD_L, D), dtype=torch.bfloat16, device="cuda") * 0.01
+        w3 = torch.randn(size=(HD_L, D), dtype=torch.bfloat16, device="cuda") * 0.01
         w2 = torch.randn(size=(D, HD_L), dtype=torch.bfloat16, device="cuda") * 0.1
 
-        x_q = quantize_fp8(x, UB, mode = FfnQuantizeMode.FP8_ROWWISE)
-        w1_q = quantize_fp8(w1, UB, mode = FfnQuantizeMode.FP8_ROWWISE)
-        w3_q = quantize_fp8(w3, UB, mode = FfnQuantizeMode.FP8_ROWWISE)
-        w2_q = quantize_fp8(w2, UB, mode = FfnQuantizeMode.FP8_ROWWISE)
+        x_q = quantize_fp8(x, UB, mode=FfnQuantizeMode.FP8_ROWWISE)
+        w1_q = quantize_fp8(w1, UB, mode=FfnQuantizeMode.FP8_ROWWISE)
+        w3_q = quantize_fp8(w3, UB, mode=FfnQuantizeMode.FP8_ROWWISE)
+        w2_q = quantize_fp8(w2, UB, mode=FfnQuantizeMode.FP8_ROWWISE)
 
         def ref_ffn(x: Tensor, w1: Tensor, w3: Tensor, w2: Tensor) -> Tensor:
-            (B, T, D) = x.shape
-            (HD_L, D_) = w1.shape
+            (B, T, D) = x.shape  # noqa: N806
+            (HD_L, D_) = w1.shape  # noqa: N806
             assert D_ == D
 
             x1 = x.view(B * T, D) @ w1.T