Revert "add CPU autotp UT (#4263)"

This reverts commit 388c848.
microsoft · Sep 29, 2023 · 0dcaa94 · 0dcaa94
1 parent 7220e7f
commit 0dcaa94
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Showing 5 changed files with 5 additions and 28 deletions.
diff --git a/.github/workflows/cpu-inference.yml b/.github/workflows/cpu-inference.yml
@@ -76,5 +76,4 @@ jobs:
           source oneCCL/build/_install/env/setvars.sh
           unset TORCH_CUDA_ARCH_LIST # only jit compile for current arch
           cd tests
-          TRANSFORMERS_CACHE=~/tmp/transformers_cache/ TORCH_EXTENSIONS_DIR=./torch-extensions pytest -m 'seq_inference' unit/
-          TRANSFORMERS_CACHE=~/tmp/transformers_cache/ TORCH_EXTENSIONS_DIR=./torch-extensions pytest -m 'inference_ops' -m 'inference' unit/
+          TRANSFORMERS_CACHE=~/tmp/transformers_cache/ TORCH_EXTENSIONS_DIR=./torch-extensions pytest -m 'seq_inference' -m 'inference_ops' -m 'inference' unit/
diff --git a/tests/unit/hybrid_engine/test_he_all.py b/tests/unit/hybrid_engine/test_he_all.py
@@ -12,10 +12,6 @@
 from deepspeed.accelerator import get_accelerator
 
 from transformers import (AutoConfig, AutoTokenizer, AutoModelForCausalLM)
-from deepspeed.ops.op_builder import InferenceBuilder
-
-if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]:
-    pytest.skip("This op had not been implemented on this system.", allow_module_level=True)
 
 rocm_version = OpBuilder.installed_rocm_version()
 if rocm_version != (0, 0):

diff --git a/tests/unit/hybrid_engine/test_he_llama.py b/tests/unit/hybrid_engine/test_he_llama.py
@@ -12,10 +12,6 @@
 from deepspeed.accelerator import get_accelerator
 
 from transformers import (AutoConfig, AutoTokenizer, AutoModelForCausalLM)
-from deepspeed.ops.op_builder import InferenceBuilder
-
-if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]:
-    pytest.skip("This op had not been implemented on this system.", allow_module_level=True)
 
 rocm_version = OpBuilder.installed_rocm_version()
 if rocm_version != (0, 0):

diff --git a/tests/unit/hybrid_engine/test_he_lora.py b/tests/unit/hybrid_engine/test_he_lora.py
@@ -14,10 +14,6 @@
 from deepspeed.utils import safe_get_full_grad
 import numpy.testing as npt
 from unit.common import DistributedTest
-from deepspeed.ops.op_builder import InferenceBuilder
-
-if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]:
-    pytest.skip("This op had not been implemented on this system.", allow_module_level=True)
 
 from transformers import (AutoConfig, AutoTokenizer, AutoModelForCausalLM)
 

diff --git a/tests/unit/inference/test_inference.py b/tests/unit/inference/test_inference.py
@@ -22,6 +22,9 @@
 from deepspeed.accelerator import get_accelerator
 from deepspeed.ops.op_builder import InferenceBuilder
 
+if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]:
+    pytest.skip("This op had not been implemented on this system.", allow_module_level=True)
+
 rocm_version = OpBuilder.installed_rocm_version()
 if rocm_version != (0, 0):
     pytest.skip("skip inference tests on rocm for now", allow_module_level=True)
@@ -362,9 +365,6 @@ def test(
         if invalid_test_msg:
             pytest.skip(invalid_test_msg)
 
-        if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]:
-            pytest.skip("This op had not been implemented on this system.", allow_module_level=True)
-
         model, task = model_w_task
         local_rank = int(os.getenv("LOCAL_RANK", "0"))
 
@@ -401,9 +401,6 @@ def test(
     ):
         model, task = model_w_task
         dtype = torch.float16
-        if dtype not in get_accelerator().supported_dtypes():
-            pytest.skip(f"Acceleraor {get_accelerator().device_name()} does not support {dtype}.")
-
         local_rank = int(os.getenv("LOCAL_RANK", "0"))
 
         pipe = pipeline(task, model=model, model_kwargs={"low_cpu_mem_usage": True}, device=local_rank, framework="pt")
@@ -517,7 +514,7 @@ def test(
     [("Helsinki-NLP/opus-mt-en-de", "translation"), ("Salesforce/codegen-350M-mono", "text-generation")],
     ids=["marian", "codegen"],  #codegen has fusedqkv weight.
 )
-@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16], ids=["fp16", "bf16"])
+@pytest.mark.parametrize("dtype", [torch.float16], ids=["fp16"])
 class TestAutoTensorParallelism(DistributedTest):
     world_size = [2]
 
@@ -533,13 +530,6 @@ def test(
         if invalid_test_msg:
             pytest.skip(invalid_test_msg)
 
-        if dtype not in get_accelerator().supported_dtypes():
-            pytest.skip(f"Acceleraor {get_accelerator().device_name()} does not support {dtype}.")
-
-        # TODO: enable this test after torch 2.1 stable release
-        if dtype == torch.bfloat16 and model_w_task[0] == "Salesforce/codegen-350M-mono":
-            pytest.skip("Codegen model(bf16) need to use torch version > 2.0.")
-
         model, task = model_w_task
         local_rank = int(os.getenv("LOCAL_RANK", "0"))
         world_size = int(os.getenv("WORLD_SIZE", "2"))