Remove TE v1 executor

.lightning/workflows/transformer-engine.yaml

@@ -12,7 +12,6 @@ parametrize:
   matrix:
     test_file:
       - test_transformer_engine_executor.py
-      - test_transformer_engine_v1_executor.py
 
 run: |
   whereis nvidia

thunder/benchmarks/benchmark_litgpt.py

@@ -686,11 +686,6 @@ def setup_compile(self, model):
 
                 executors.insert(0, torch_compile_ex)
 
-            if "transformerengine_v1" in self.compile:
-                from thunder.executors.transformer_engine_v1ex import transformer_engine_v1_ex
-
-                executors.insert(0, transformer_engine_v1_ex)
-
             elif "transformerengine" in self.compile:
                 from thunder.executors.transformer_engineex import (
                     transformer_engine_ex,

thunder/core/trace.py

@@ -406,18 +406,6 @@ def keyfn(class_or_module: type | ModuleType) -> str:
                 program.append("")
 
             if include_decorators:
-                # NOTE: For TransformerEngine executor, we want to wrap the generated
-                # forward function in fp8_autocast ctx manager.
-                # In the future, if other executor has similar requirements, we should
-                # add a new extension point for executors
-                # NOTE: For TE v1.6 onwards, `fp8_autocast` checks if `torch.is_grad_enabled` for updating
-                # the FP8 scales/inverses. So this decorator should be applied before `torch.no_grad` (so that
-                # it is in grad enabled part).
-                from thunder.executors.transformer_engine_v1ex import _is_te_linear_enabled, _get_te_wrapper_string
-
-                if TraceTag.AUGMENTED_FORWARD and _is_te_linear_enabled(import_ctx, object_ctx):
-                    program.append(_get_te_wrapper_string())
-
                 # Disable gradients since Thunder takes care of this (for when calling torch operations)
                 program.append("@torch.no_grad()")
                 # Disable autocast since we already generated the trace with it in consideration (for when calling torch