Merge branch 'main' into research/ppl_refactor

src/deepsparse/transformers/helpers.py

@@ -73,7 +73,7 @@ def get_deployment_path(model_path: str) -> Tuple[str, str]:
 
     elif model_path.startswith("zoo:"):
         zoo_model = Model(model_path)
-        deployment_path = zoo_model.deployment_directory_path
+        deployment_path = zoo_model.deployment.path
         return deployment_path, os.path.join(deployment_path, _MODEL_DIR_ONNX_NAME)
     elif model_path.startswith("hf:"):
         from huggingface_hub import snapshot_download

src/deepsparse/transformers/pipelines/text_generation.py

@@ -670,6 +670,9 @@ def process_engine_outputs(
             input_tokens=input_tokens,
         )
 
+        if "session_ids" in kwargs:
+            outputs["session_ids"] = kwargs["session_ids"]
+
         if self._debug:
             debug_params = dict(
                 kv_cache_state=kv_cache_state,

src/deepsparse/transformers/utils/token_generator.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
+from typing import List, Optional
 
 import numpy
 
@@ -29,7 +29,7 @@ class TokenGenerator:
     def __init__(
         self,
         logits_shape: int,
-        tokens: List[int] = [],
+        tokens: Optional[List[int]] = None,
         deterministic: bool = True,
         sampling_temperature: float = 1.0,
         top_k: int = 0,
@@ -61,7 +61,7 @@ def __init__(
         self.top_p = top_p
         self.frequency_penalty = frequency_penalty
         self.presence_penalty = presence_penalty
-        self.tokens = tokens
+        self.tokens = [] if tokens is None else tokens
 
         self._initialize_token_frequencies()
 
@@ -168,5 +168,5 @@ def _update_frequencies(self, token: numpy.ndarray):
 
     def _initialize_token_frequencies(self):
         unique_tokens, frequencies = numpy.unique(self.tokens, return_counts=True)
-        for token, frequnecies in zip(unique_tokens, frequencies):
-            self.token_frequencies[token] += frequnecies
+        for token, freq in zip(unique_tokens, frequencies):
+            self.token_frequencies[token] += freq

tests/deepsparse/transformers/pipelines/integration_tests/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# 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.

tests/deepsparse/transformers/pipelines/integration_tests/configs/codegen.yaml

@@ -0,0 +1,8 @@
+cadence: "nightly"
+model_path: "zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none"
+torch_model_name: "salesforce/codegen-350m-mono"
+task: ["text-generation"]#, "chat"]
+prompt: "\ndef Fibonacci(n):\n    # Check if input is 0 then it will\n    # print incorrect input"
+has_bos_token: False
+precision: 0.0001
+internal_kv_cache: [True, False]

tests/deepsparse/transformers/pipelines/integration_tests/configs/opt.yaml

@@ -0,0 +1,8 @@
+cadence: "nightly"
+model_path: "zoo:nlg/text_generation/opt-1.3b/pytorch/huggingface/opt_pretrain/base-none"
+torch_model_name: "facebook/opt-1.3b"
+task: ["text-generation"]
+prompt: "Didn't know what time it was, the lights were low\n I leaned back on my radio"
+has_bos_token: True
+precision: 0.0001
+internal_kv_cache: [True, False]

tests/deepsparse/transformers/pipelines/helpers.py → tests/deepsparse/transformers/pipelines/integration_tests/helpers.py

@@ -12,11 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List, Tuple
+import logging
+import os
+from typing import Any, Dict, List, Tuple, Union
 
 import numpy
+import yaml
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
+import pytest
+
 
 class TorchGroundTruthSource:
     """
@@ -36,7 +41,6 @@ def __init__(self, num_tokens_to_generate: int, model_name: str):
         self.tokenizer = self._create_tokenizer(model_name)
 
         self.num_tokens_to_generate = num_tokens_to_generate
-        self.model_name = model_name
 
     def tokenize(self, prompt: str):
         return self.tokenizer(prompt, return_tensors="pt")
@@ -82,3 +86,61 @@ def _create_tokenizer(model_name):
             tokenizer.pad_token = tokenizer.eos_token
 
         return tokenizer
+
+
+def parse_params(configs_directory: str) -> List[Dict[str, Any]]:
+    # parses the config file provided
+    assert os.path.isdir(
+        configs_directory
+    ), f"Config_directory {configs_directory} is not a directory"
+
+    config_dicts = []
+    for file in os.listdir(configs_directory):
+        if file.endswith(".yaml"):
+            config_path = os.path.join(configs_directory, file)
+            # reads the yaml file
+            with open(config_path, "r") as f:
+                config = yaml.safe_load(f)
+
+            cadence = os.environ.get("CADENCE", "commit")
+            expected_cadence = config["cadence"]
+
+            if not isinstance(expected_cadence, list):
+                expected_cadence = [expected_cadence]
+            if cadence in expected_cadence:
+                config_dicts.append(config)
+            else:
+                logging.info(
+                    f"Skipping testing model: {config['model_path']} "
+                    f"for cadence: {config['cadence']}"
+                )
+        else:
+            raise FileNotFoundError(
+                f"Could not find a yaml file in {configs_directory}"
+            )
+    return config_dicts
+
+
+def validate_internal_kv_cache(
+    internal_kv_cache, available_kv_cache_types: Union[str, List[str]]
+) -> bool:
+    if internal_kv_cache and True not in available_kv_cache_types:
+        pytest.skip(
+            "The tests for running the pipeline with "
+            "internal kv cache management are disabled."
+        )
+    if not internal_kv_cache and False not in available_kv_cache_types:
+        pytest.skip(
+            "The tests for running the pipeline with "
+            "external kv cache management are disabled."
+        )
+    return internal_kv_cache
+
+
+def validate_task(task: str, available_tasks: Union[str, List[str]]) -> bool:
+    if task not in available_tasks:
+        pytest.skip(
+            f"The tests for running the pipeline with task: {task} are disabled. "
+            f"The available tasks, as specified in the config are: {available_tasks}"
+        )
+    return task