fix arc ut test (#9736)

python/llm/test/inference_gpu/test_optimize_model.py

@@ -1,182 +0,0 @@
-#
-# Copyright 2016 The BigDL Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-import os
-import pytest
-
-import torch
-from transformers import LlamaTokenizer, AutoTokenizer
-from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel
-
-
-device = os.environ['DEVICE']
-print(f'Running on {device}')
-if device == 'xpu':
-    import intel_extension_for_pytorch as ipex
-
-prompt = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
-
-@pytest.mark.parametrize('Model, Tokenizer, model_path',[
-    (AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
-    (AutoModelForCausalLM, AutoTokenizer, os.environ.get('FALCON_7B_ORIGIN_PATH'))
-    ])
-def test_optimize_model(Model, Tokenizer, model_path):
-    tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
-    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
-
-    model = Model.from_pretrained(model_path,
-                                load_in_4bit=True,
-                                optimize_model=False,
-                                trust_remote_code=True)
-    model = model.to(device)
-    logits_base_model = (model(input_ids)).logits
-    model.to('cpu')  # deallocate gpu memory
-
-    model = Model.from_pretrained(model_path,
-                                load_in_4bit=True,
-                                optimize_model=True,
-                                trust_remote_code=True)
-    model = model.to(device)
-    logits_optimized_model = (model(input_ids)).logits
-    model.to('cpu')
-    
-    diff = abs(logits_base_model - logits_optimized_model).flatten()
-
-    assert any(diff) is False
-
-class Test_Optimize_Gpu_Model:
-    def setup(self):
-
-        self.layer_outputs = []
-        self.pre_layer_outputs = []
-
-    def run_optimize_gpu_model(self, Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound):
-        def forward_hook(module, input, output, layer_name):
-            self.layer_outputs.append(output)
-
-        def pre_forward_hook(module, input, output, layer_name):
-            self.pre_layer_outputs.append(output)
-
-        tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
-        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
-
-        model = Model.from_pretrained(model_path,
-                                      load_in_4bit=True,
-                                      optimize_model=False,
-                                      trust_remote_code=True)
-        model = model.to(device)
-
-        for layer_name, layer_module in model.named_modules():
-            if layer_name == layer_norm:
-                layer_module.register_forward_hook(
-                    lambda module, input, output, layer_name=layer_name: pre_forward_hook(module, input,
-                                                                                          output, layer_name))
-            if layer_name == self_attn:
-                layer_module.register_forward_hook(
-                    lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
-                                                                                      output, layer_name))
-        logits_base_model = (model(input_ids)).logits
-        # the list `layer_output` has only one element.
-        layer_tensor = self.layer_outputs.pop()
-        model.to('cpu')
-
-        opt_model = Model.from_pretrained(model_path,
-                                          load_in_4bit=True,
-                                          optimize_model=True,
-                                          trust_remote_code=True)
-        opt_model = opt_model.to(device)
-
-
-        def replace_forward_hook(module, input, output, layer_name):
-            output = self.pre_layer_outputs[0]
-            return output
-
-        for layer_name, layer_module in opt_model.named_modules():
-            if layer_name == layer_norm:
-                layer_module.register_forward_hook(
-                    lambda module, input, output, layer_name=layer_name: replace_forward_hook(module, input,
-                                                                                              output, layer_name))
-            if layer_name == self_attn:
-                layer_module.register_forward_hook(
-                    lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
-                                                                                      output, layer_name))
-        logits_optimized_model = (opt_model(input_ids)).logits
-        # the list `layer_output` has only one element.
-        opt_layer_tensor = self.layer_outputs[0]
-        opt_model.to('cpu')
-
-        attn_output_diff = []
-        for i, (t1, t2) in enumerate(zip(layer_tensor, opt_layer_tensor)):
-            if t1 is not None and t2 is not None:
-                if isinstance(t1, torch.Tensor) and isinstance(t2, torch.Tensor):
-                    # 'attn_output' is of type torch.Tensor.
-                    attn_output_diff.append(t1 - t2)
-                else:
-                    # 'past_key_value'is of type tuple as default.
-                    for i, (t3, t4) in enumerate(zip(t1, t2)):
-                        if model.config.architectures[0] == "ChatGLMModel" and \
-                                hasattr(model.config, 'padded_vocab_size') and \
-                                model.config.padded_vocab_size == 65024:
-                            # chatglm2's past_key_value is expanded 16x for some speedup.
-                            # We need to narrow it here.
-                            t4 = t4[:, :, 15:17, :]
-                        attn_output_diff.append(t3 - t4)
-
-        max_diff_tensor = [torch.max(item).item() for item in attn_output_diff]
-        print(max_diff_tensor)
-        assert all(max_diff <= lower_bound for max_diff in max_diff_tensor)
-
-
-    def test_falcon_gpu_model(self):
-
-        Model = AutoModelForCausalLM
-        Tokenizer = AutoTokenizer
-        model_path = os.environ.get('FALCON_7B_ORIGIN_PATH')
-        # currently only compare the output of the last self-attention layer.
-        layer_norm = "transformer.h.31.input_layernorm"
-        self_attn = "transformer.h.31.self_attention"
-        lower_bound = 0
-
-        self.run_optimize_gpu_model(Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
-
-
-    def test_llama_gpu_model(self):
-
-        Model = AutoModelForCausalLM
-        Tokenizer = AutoTokenizer
-        model_path = os.environ.get('LLAMA2_7B_ORIGIN_PATH')
-        # currently only compare the output of the last self-attention layer.
-        layer_norm = "model.layers.31.input_layernorm"
-        self_attn = "model.layers.31.self_attn"
-        lower_bound = 5e-2
-
-        self.run_optimize_gpu_model(Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
-
-    def test_chatglm2_gpu_model(self):
-
-        Model = AutoModel
-        Tokenizer = AutoTokenizer
-        model_path = os.environ.get('CHATGLM2_6B_ORIGIN_PATH')
-        # currently only need to compare the output of one self-attention layer.
-        layer_norm = "transformer.encoder.layers.27.input_layernorm"
-        self_attn = "transformer.encoder.layers.27.self_attention"
-        lower_bound = 5e-5
-
-        self.run_optimize_gpu_model(Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
-
-
-if __name__ == '__main__':
-    pytest.main([__file__])

python/llm/test/inference_gpu/test_transformers_api.py

@@ -18,6 +18,7 @@
 import os, time
 import pytest
 
+import torch
 from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel, AutoModelForSpeechSeq2Seq
 from transformers import LlamaTokenizer, AutoTokenizer
 
@@ -36,38 +37,192 @@ if device == 'xpu':
     (AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
     ])
 def test_completion(Model, Tokenizer, model_path, prompt, answer):
-    tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
+    with torch.inference_mode():
-    model = Model.from_pretrained(model_path,
+        tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
-                                load_in_4bit=True,
+        model = Model.from_pretrained(model_path,
-                                optimize_model=True,
+                                    load_in_4bit=True,
-                                trust_remote_code=True)
+                                    optimize_model=True,
-    model = model.to(device)
+                                    trust_remote_code=True)
+        model = model.to(device)
 
-    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
-    output = model.generate(input_ids, max_new_tokens=32)
+        output = model.generate(input_ids, max_new_tokens=32)
-    model.to('cpu')   # deallocate gpu memory
+        model.to('cpu')   # deallocate gpu memory
-    output_str = tokenizer.decode(output[0], skip_special_tokens=True)
+        output_str = tokenizer.decode(output[0], skip_special_tokens=True)
+
+        assert answer in output_str
+
+def test_transformers_auto_model_for_speech_seq2seq_int4():
+    with torch.inference_mode():
+        from transformers import WhisperProcessor
+        from datasets import load_from_disk
+        model_path = os.environ.get('WHISPER_TINY_ORIGIN_PATH')
+        dataset_path = os.environ.get('SPEECH_DATASET_PATH')
+        processor = WhisperProcessor.from_pretrained(model_path)
+        ds = load_from_disk(dataset_path)
+        sample = ds[0]["audio"]
+        input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
+        input_features = input_features.to(device)
+        model = AutoModelForSpeechSeq2Seq.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True, optimize_model=True)
+        model = model.to(device)
+        predicted_ids = model.generate(input_features)
+        # decode token ids to text
+        transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
+        model.to('cpu')
+        print('Output:', transcription)
+        assert 'Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.' in transcription[0]
+
+
+prompt = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
+
+@pytest.mark.parametrize('Model, Tokenizer, model_path',[
+    (AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
+    (AutoModelForCausalLM, AutoTokenizer, os.environ.get('LLAMA2_7B_ORIGIN_PATH'))
+    ])
+def test_optimize_model(Model, Tokenizer, model_path):
+    with torch.inference_mode():
+        tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
+
+        model = Model.from_pretrained(model_path,
+                                    load_in_4bit=True,
+                                    optimize_model=False,
+                                    trust_remote_code=True)
+        model = model.to(device)
+        logits_base_model = (model(input_ids)).logits
+        model.to('cpu')  # deallocate gpu memory
+
+        model = Model.from_pretrained(model_path,
+                                    load_in_4bit=True,
+                                    optimize_model=True,
+                                    trust_remote_code=True)
+        model = model.to(device)
+        logits_optimized_model = (model(input_ids)).logits
+        model.to('cpu')
+
+        assert all(torch.isclose(logits_optimized_model, logits_base_model).tolist())
+
+class Test_Optimize_Gpu_Model:
+    def setup(self):
+
+        self.layer_outputs = []
+        self.pre_layer_outputs = []
+
+    def run_optimize_gpu_model(self, Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound):
+        with torch.inference_mode():
+            def forward_hook(module, input, output, layer_name):
+                self.layer_outputs.append(output)
+
+            def pre_forward_hook(module, input, output, layer_name):
+                self.pre_layer_outputs.append(output)
+
+            tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
+            input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
+
+            model = Model.from_pretrained(model_path,
+                                        load_in_4bit=True,
+                                        optimize_model=False,
+                                        trust_remote_code=True)
+            model = model.to(device)
+
+            for layer_name, layer_module in model.named_modules():
+                if layer_name == layer_norm:
+                    layer_module.register_forward_hook(
+                        lambda module, input, output, layer_name=layer_name: pre_forward_hook(module, input,
+                                                                                            output, layer_name))
+                if layer_name == self_attn:
+                    layer_module.register_forward_hook(
+                        lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
+                                                                                        output, layer_name))
+            logits_base_model = (model(input_ids)).logits
+            # the list `layer_output` has only one element.
+            layer_tensor = self.layer_outputs.pop()
+            model.to('cpu')
+
+            opt_model = Model.from_pretrained(model_path,
+                                            load_in_4bit=True,
+                                            optimize_model=True,
+                                            trust_remote_code=True)
+            opt_model = opt_model.to(device)
+
+
+            def replace_forward_hook(module, input, output, layer_name):
+                output = self.pre_layer_outputs[0]
+                return output
+
+            for layer_name, layer_module in opt_model.named_modules():
+                if layer_name == layer_norm:
+                    layer_module.register_forward_hook(
+                        lambda module, input, output, layer_name=layer_name: replace_forward_hook(module, input,
+                                                                                                output, layer_name))
+                if layer_name == self_attn:
+                    layer_module.register_forward_hook(
+                        lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
+                                                                                        output, layer_name))
+            logits_optimized_model = (opt_model(input_ids)).logits
+            # the list `layer_output` has only one element.
+            opt_layer_tensor = self.layer_outputs[0]
+            opt_model.to('cpu')
+
+            attn_output_diff = []
+            for i, (t1, t2) in enumerate(zip(layer_tensor, opt_layer_tensor)):
+                if t1 is not None and t2 is not None:
+                    if isinstance(t1, torch.Tensor) and isinstance(t2, torch.Tensor):
+                        # 'attn_output' is of type torch.Tensor.
+                        attn_output_diff.append(t1 - t2)
+                    else:
+                        # 'past_key_value'is of type tuple as default.
+                        for i, (t3, t4) in enumerate(zip(t1, t2)):
+                            if model.config.architectures[0] == "ChatGLMModel" and \
+                                    hasattr(model.config, 'padded_vocab_size') and \
+                                    model.config.padded_vocab_size == 65024:
+                                # chatglm2's past_key_value is expanded 16x for some speedup.
+                                # We need to narrow it here.
+                                t4 = t4[:, :, 15:17, :]
+                            attn_output_diff.append(t3 - t4)
+
+            max_diff_tensor = [torch.max(item).item() for item in attn_output_diff]
+            print(max_diff_tensor)
+            assert all(max_diff <= lower_bound for max_diff in max_diff_tensor)
+
+
+    def test_falcon_gpu_model(self):
+
+        Model = AutoModelForCausalLM
+        Tokenizer = AutoTokenizer
+        model_path = os.environ.get('FALCON_7B_ORIGIN_PATH')
+        # currently only compare the output of the last self-attention layer.
+        layer_norm = "transformer.h.31.input_layernorm"
+        self_attn = "transformer.h.31.self_attention"
+        lower_bound = 0
+
+        self.run_optimize_gpu_model(Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
+
+
+    def test_llama_gpu_model(self):
+
+        Model = AutoModelForCausalLM
+        Tokenizer = AutoTokenizer
+        model_path = os.environ.get('LLAMA2_7B_ORIGIN_PATH')
+        # currently only compare the output of the last self-attention layer.
+        layer_norm = "model.layers.31.input_layernorm"
+        self_attn = "model.layers.31.self_attn"
+        lower_bound = 5e-2
+
+        self.run_optimize_gpu_model(Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
+
+    def test_chatglm2_gpu_model(self):
+
+        Model = AutoModel
+        Tokenizer = AutoTokenizer
+        model_path = os.environ.get('CHATGLM2_6B_ORIGIN_PATH')
+        # currently only need to compare the output of one self-attention layer.
+        layer_norm = "transformer.encoder.layers.27.input_layernorm"
+        self_attn = "transformer.encoder.layers.27.self_attention"
+        lower_bound = 1e-3
+
+        self.run_optimize_gpu_model(Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
 
-    assert answer in output_str
 
-#def test_transformers_auto_model_for_speech_seq2seq_int4():
-#    from transformers import WhisperProcessor
-#    from datasets import load_from_disk
-#    model_path = os.environ.get('WHISPER_TINY_ORIGIN_PATH')
-#    dataset_path = os.environ.get('SPEECH_DATASET_PATH')
-#    processor = WhisperProcessor.from_pretrained(model_path)
-#    ds = load_from_disk(dataset_path)
-#    sample = ds[0]["audio"]
-#    input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
-#    input_features = input_features.to(device)
-#    model = AutoModelForSpeechSeq2Seq.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True, optimize_model=True)
-#    model = model.to(device)
-#    predicted_ids = model.generate(input_features)
-#    # decode token ids to text
-#    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
-#    model.to('cpu')
-#    print('Output:', transcription)
-#    assert 'Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.' in transcription[0]
-        
 if __name__ == '__main__':
     pytest.main([__file__])

python/llm/test/run-llm-inference-tests-gpu.sh

@@ -5,6 +5,7 @@ export LLM_HOME=${ANALYTICS_ZOO_ROOT}/python/llm/src
 export LLM_INFERENCE_TEST_DIR=${ANALYTICS_ZOO_ROOT}/python/llm/test/inference_gpu
 
 export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
 export DEVICE='xpu'
 
 set -e