Nano: update ipex_bf16_inference_model (intel-analytics#5470)

* rollback requirement-doc

* Update

* Update

* Update

* Update

* Fix ipex with amp error

* Fix unit test

* Update

* Remove redundant method

* Fix error

* remove redundant import

* Update ut to conver cases

python/nano/src/bigdl/nano/deps/ipex/ipex_inference_bf16_model.py

@@ -14,6 +14,12 @@
 # limitations under the License.
 #
 
+import contextlib
+import subprocess
+from logging import info, warning
+
+from ...utils.log4Error import invalidInputError
+
 from .ipex_inference_model import PytorchIPEXJITModel
 from bigdl.nano.pytorch.amp.bfloat16 import autocast
 import torch
@@ -38,13 +44,35 @@ def __init__(self, model, input_sample=None, use_ipex=False,
                the parameter will be ignored if use_ipex is False.
         :param from_load: this will only be set by _load method.
         '''
+        if use_ipex:
+            invalidInputError(
+                self._check_cpu_isa,
+                errMsg="Applying IPEX BF16 optimization needs the cpu support avx512.",
+                fixMsg="Please set use_ipex to False or not set precision to bf16."
+            )
         PytorchIPEXJITModel.__init__(self, model, input_sample=input_sample, use_ipex=use_ipex,
                                      dtype=torch.bfloat16, use_jit=use_jit,
                                      channels_last=channels_last, from_load=from_load)
 
-    @autocast()
+    @property
+    def _check_cpu_isa(self):
+        """Indicator to verify if cpu supports avx512"""
+        msg = subprocess.check_output(["lscpu"]).decode("utf-8")
+        return 'avx512' in msg or 'amx' in msg
+
+    def autocast_context_manager(self):
+        """Create autocast context"""
+        return autocast(enabled=self._check_cpu_isa)
+
+    @contextlib.contextmanager
+    def forward_context(self):
+        """Enable autocast context"""
+        with self.autocast_context_manager():
+            yield
+
     def forward_step(self, *inputs):
-        return super().forward_step(*inputs)
+        with self.forward_context():
+            return super().forward_step(*inputs)
 
     @property
     def status(self):

python/nano/src/bigdl/nano/pytorch/amp/bfloat16.py

@@ -17,6 +17,8 @@
 import contextlib
 import io
 from logging import info, warning
+import sys
+import fcntl
 
 from pytorch_lightning import LightningModule
 import torch
@@ -46,6 +48,40 @@ def __exit__(self, exc_type, exc_val, exc_tb):
         return super().__exit__(exc_type, exc_val, exc_tb)
 
 
+class RedirectStream(object):
+    """Context manager to capture output of shared library"""
+    def __init__(self, stream=sys.stdout, target=None):
+        self.origin_stream = stream
+        self.origin_stream_fileno = stream.fileno()
+        self.target = io.StringIO() if target is None else target
+        # Create a pipe to capture the stream
+        self.pipe_out, self.pipe_in = os.pipe()
+
+    def __enter__(self):
+        # Save a copy of the original stream
+        self.origin_stream_fileno_dup = os.dup(self.origin_stream_fileno)
+        # Replace the original stream with the write pipe
+        os.dup2(self.pipe_in, self.origin_stream_fileno)
+        os.close(self.pipe_in)
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.origin_stream.flush()
+        # Make pipe_out non-blocking
+        fcntl.fcntl(self.pipe_out, fcntl.F_SETFL, os.O_NONBLOCK)
+        while True:
+            try:
+                buf = os.read(self.pipe_out, 1024)
+                if not buf:
+                    break
+                self.target.write(buf.decode('utf-8'))
+            except OSError as e:
+                break
+        os.close(self.pipe_out)
+        os.dup2(self.origin_stream_fileno_dup, self.origin_stream_fileno)
+        os.close(self.origin_stream_fileno_dup)
+
+
 class BF16Model(LightningModule):
     """Model of BFloat16 with auto mixed precision."""
 

python/nano/test/pytorch/tests/test_bf16_ipex.py

@@ -19,7 +19,8 @@
 from bigdl.nano.pytorch import Trainer
 from torchvision.models.resnet import resnet18
 from unittest.mock import MagicMock, Mock, PropertyMock, patch
-from bigdl.nano.pytorch.utils import TORCH_VERSION_LESS_1_10, TORCH_VERSION_LESS_1_12
+from bigdl.nano.pytorch.utils import TORCH_VERSION_LESS_1_10, TORCH_VERSION_LESS_1_11
+from bigdl.nano.common import check_avx512
 
 
 class Pytorch1_9:
@@ -35,30 +36,51 @@ def test_bf16_pytorch_less_1_10(self):
             trainer.quantize(model, precision='bf16', use_ipex=True)
 
 
-class Pytorch1_12:
-    def test_bf16_common(self):
-        """
-        Debug mode. Allow run bf16 forward without bf16 instruction support.
-        """
+class CaseWithoutAVX512:
+    def test_unsupported_HW_or_OS(self):
+        trainer = Trainer(max_epochs=1)
+        model = resnet18(num_classes=10)
+
+        with pytest.raises(RuntimeError,
+                           match="Applying IPEX BF16 optimization needs the cpu support avx512."):
+            bf16_model = trainer.quantize(model, precision='bf16', use_ipex=True)
+
+
+class Pytorch1_11:
+    @patch('bigdl.nano.deps.ipex.ipex_inference_bf16_model.PytorchIPEXJITBF16Model._check_cpu_isa', new_callable=PropertyMock)
+    def test_unsupported_HW_or_OS(self, mocked_check_cpu_isa):
+        mocked_check_cpu_isa.return_value = False
+        trainer = Trainer(max_epochs=1)
+        model = resnet18(num_classes=10)
+
+        with pytest.raises(RuntimeError,
+                           match="Applying IPEX BF16 optimization needs the cpu support avx512."):
+            bf16_model = trainer.quantize(model, precision='bf16', use_ipex=True)
+
+    def test_bf16_with_avx512_core(self):
         trainer = Trainer(max_epochs=1)
         model = resnet18(num_classes=10)
 
         x = torch.rand((10, 3, 256, 256))
         y = torch.ones((10,), dtype=torch.long)
 
         bf16_model = trainer.quantize(model, precision='bf16', use_ipex=True)
-        # Debug mode to test functionality, make sure forward is called sucessfully
         y_hat = bf16_model(x)
+
         assert y_hat.shape == (10, 10) and y_hat.dtype == torch.bfloat16
 
 
-TORCH_VERSION_CLS = Pytorch1_12
+TORCH_VERSION_CLS = Pytorch1_11
 
 if TORCH_VERSION_LESS_1_10:
+    print("ipex 1.9")
     TORCH_VERSION_CLS = Pytorch1_9
+elif not check_avx512():
+    print("IPEX Inference Model Without AVX512")
+    TORCH_VERSION_CLS = CaseWithoutAVX512
 
 
-class TestBF16(TORCH_VERSION_CLS, TestCase):
+class TestIPEXBF16(TORCH_VERSION_CLS, TestCase):
     pass