Add Benchmarking Compatibility to PaddingFree Plugin

plugins/attention-and-distributed-packing/src/fms_acceleration_aadp/flash_attn.py

@@ -138,9 +138,12 @@ def _flash_attention_forward_with_posids(
             and sliding_window is not None
             and key_states.shape[1] > sliding_window
         )
-    flash_kwargs = (
-        {"window_size": (sliding_window, sliding_window)} if use_sliding_windows else {}
-    )
+
+    # set flash_kwargs only if both use_sliding_window=true and sliding window exist
+    # otherwise, flash_attn takes window_size = -1 as the default
+    flash_kwargs = {}
+    if use_sliding_windows and sliding_window:
+        flash_kwargs = {"window_size": (sliding_window, sliding_window)}
 
     try:
         if is_flash_attn_greater_or_equal("2.4.1"):

sample-configurations/CONTENTS.yaml

@@ -36,4 +36,30 @@ framework_configs:
     - shortname: aadp-padding-free
       plugins:
         - attention-and-distributed-packing
-      filename: aadp-padding-free-sample-configuration.yaml
+      filename: aadp-padding-free-sample-configuration.yaml
+
+    - shortname: accelerated-peft-bnb-padding-free
+      plugins:
+        - accelerated-peft
+        - attention-and-distributed-packing
+      filename: accelerated-peft-bnb-nf4-padding-free-sample-configuration.yaml
+
+    - shortname: accelerated-peft-autogptq-padding-free
+      plugins:
+        - accelerated-peft
+        - attention-and-distributed-packing
+      filename: accelerated-peft-autogptq-padding-free-sample-configuration.yaml
+
+    - shortname: accelerated-peft-bnb-foak-padding-free
+      plugins:
+        - accelerated-peft
+        - attention-and-distributed-packing
+        - fused-ops-and-kernels
+      filename: accelerated-peft-bnb-nf4-foak-padding-free-sample-configuration.yaml
+
+    - shortname: accelerated-peft-autogptq-foak-padding-free
+      plugins:
+        - accelerated-peft
+        - attention-and-distributed-packing
+        - fused-ops-and-kernels
+      filename: accelerated-peft-autogptq-foak-padding-free-sample-configuration.yaml

sample-configurations/accelerated-peft-autogptq-foak-padding-free-sample-configuration.yaml

@@ -0,0 +1,58 @@
+# FMS Acceleration Plugin Configuration. 
+#
+# Each stanza incorporates various configurations for 
+# different fine-tuning / training tasks.
+plugins:
+  # Configurations to accelerate data packing/padding in training
+  training:
+
+    # attention module configurations
+    # e.g. padding-free modifications to attention layer
+    attention:
+
+      # this controls the confgurations for padding free computation of flash attention
+      padding_free:
+        method: huggingface
+  peft:
+
+    # quantization-releated acceleration
+    # e.g., kernels for quantized base weights
+    quantization:
+
+      # AutoGPTQ quantized base weights.
+      auto_gptq:
+
+        # Kernel to be used for GPTQ linear laeyer
+        # NOTE: Not all kernels are suitable for PEFT training; need to use 
+        # kernels that support autograd forward / backward. The best 
+        # recommendation at the moment is "triton_v2".
+        kernel: triton_v2
+
+        # If true, then will already expect quantized checkpoint 
+        # passed into TrainingArguments.model_name_or_path
+        from_quantized: true
+
+        # Setting to false, will create GPTQ-LORA using the local autogptq package.
+        # if true, will create legacy implementation of GPTQ-LORA using external 
+        # `auto_gptq`. Refer to README for installation instructions
+        use_external_lib: false
+      fused_ops_and_kernels:
+
+        # load unsloth optimizations for these 4bit base layer weights.
+        # currently only support "auto_gptq" and "bitsandbytes"
+        base_layer: auto_gptq
+
+        # activate various unsloth optimizations
+        # NOTE: currently supports only all-or-nothing.
+
+        # fused kernels for lora linear layers
+        fused_lora: true
+
+        # fast loss triton kernels
+        fast_loss: true
+
+        # fast rms norm triton kernels
+        fast_rsm_layernorm: true
+
+        # fast RoPE embedding triton kernels
+        fast_rope_embeddings: true

sample-configurations/accelerated-peft-autogptq-padding-free-sample-configuration.yaml

@@ -0,0 +1,38 @@
+# FMS Acceleration Plugin Configuration. 
+#
+# Each stanza incorporates various configurations for 
+# different fine-tuning / training tasks.
+plugins:
+  # Configurations to accelerate data packing/padding in training
+  training:
+
+    # attention module configurations
+    # e.g. padding-free modifications to attention layer
+    attention:
+
+      # this controls the confgurations for padding free computation of flash attention
+      padding_free:
+        method: huggingface
+  peft:
+
+    # quantization-releated acceleration
+    # e.g., kernels for quantized base weights
+    quantization:
+
+      # AutoGPTQ quantized base weights.
+      auto_gptq:
+
+        # Kernel to be used for GPTQ linear laeyer
+        # NOTE: Not all kernels are suitable for PEFT training; need to use 
+        # kernels that support autograd forward / backward. The best 
+        # recommendation at the moment is "triton_v2".
+        kernel: triton_v2
+
+        # If true, then will already expect quantized checkpoint 
+        # passed into TrainingArguments.model_name_or_path
+        from_quantized: true
+
+        # Setting to false, will create GPTQ-LORA using the local autogptq package.
+        # if true, will create legacy implementation of GPTQ-LORA using external 
+        # `auto_gptq`. Refer to README for installation instructions
+        use_external_lib: false

sample-configurations/accelerated-peft-bnb-nf4-foak-padding-free-sample-configuration.yaml

@@ -0,0 +1,53 @@
+# FMS Acceleration Plugin Configuration. 
+#
+# Each stanza incorporates various configurations for 
+# different fine-tuning / training tasks.
+plugins:
+  # Configurations to accelerate data packing/padding in training
+  training:
+
+    # attention module configurations
+    # e.g. padding-free modifications to attention layer
+    attention:
+
+      # this controls the confgurations for padding free computation of flash attention
+      padding_free:
+        method: huggingface
+  peft:
+
+    # quantization-releated acceleration
+    # e.g., kernels for quantized base weights
+    quantization:
+
+      # For loading BitsAndBytes quantized layers
+      # to serve as 4bit base-weights for LoRA PEFT-tuning.
+      # NOTE: currently AutoGPTQ is not properly integrated into huggingface /
+      # bitsandbytes, thus recommended quant_type to be either "nf4"
+      # or "fp4".
+      # bitsandbytes:
+      bitsandbytes:
+        quant_type: nf4
+
+        # If True, then no get_peft_model and prepare_model_for_kbit_training
+        # will be called. 
+        no_peft_model: false
+      fused_ops_and_kernels:
+
+        # load unsloth optimizations for these 4bit base layer weights.
+        # currently only support "auto_gptq" and "bitsandbytes"
+        base_layer: bitsandbytes
+
+        # activate various unsloth optimizations
+        # NOTE: currently supports only all-or-nothing.
+
+        # fused kernels for lora linear layers
+        fused_lora: true
+
+        # fast loss triton kernels
+        fast_loss: true
+
+        # fast rms norm triton kernels
+        fast_rsm_layernorm: true
+
+        # fast RoPE embedding triton kernels
+        fast_rope_embeddings: true

sample-configurations/accelerated-peft-bnb-nf4-padding-free-sample-configuration.yaml

@@ -0,0 +1,33 @@
+# FMS Acceleration Plugin Configuration. 
+#
+# Each stanza incorporates various configurations for 
+# different fine-tuning / training tasks.
+plugins:
+  # Configurations to accelerate data packing/padding in training
+  training:
+
+    # attention module configurations
+    # e.g. padding-free modifications to attention layer
+    attention:
+
+      # this controls the confgurations for padding free computation of flash attention
+      padding_free:
+        method: huggingface
+  peft:
+
+    # quantization-releated acceleration
+    # e.g., kernels for quantized base weights
+    quantization:
+
+      # For loading BitsAndBytes quantized layers
+      # to serve as 4bit base-weights for LoRA PEFT-tuning.
+      # NOTE: currently AutoGPTQ is not properly integrated into huggingface /
+      # bitsandbytes, thus recommended quant_type to be either "nf4"
+      # or "fp4".
+      # bitsandbytes:
+      bitsandbytes:
+        quant_type: nf4
+
+        # If True, then no get_peft_model and prepare_model_for_kbit_training
+        # will be called. 
+        no_peft_model: false

scripts/benchmarks/benchmark.py

@@ -79,6 +79,7 @@
 KEYWORD_ALLOC_DELTA = "alloc_delta"
 HF_ARG_TRAINING_DATA_PATH = "training_data_path"
 HF_ARG_RESPONSE_TEMPLATE = "response_template"
+HF_ARG_DATASET_TEXT_FIELD = "dataset_text_field"
 HF_ARG_SKIP_MEMORY_METRIC = "skip_memory_metrics"
 RESULT_FIELD_ALLOCATED_GPU_MEM = "mem_torch_mem_alloc_in_bytes"
 RESULT_FIELD_PEAK_ALLOCATED_GPU_MEM = "mem_peak_torch_mem_alloc_in_bytes"
@@ -164,9 +165,15 @@ def __init__(
         input_field: str = "input",
         dataset_text_field: str = "output",
         chat_template: str = None,
+        response_template: str = None,
+        additional_dataset_kwargs: Dict = {},
     ) -> None:
 
-        self.dataset_split = datasets.load_dataset(dataset_name, split=dataset_split)
+        self.dataset_split = datasets.load_dataset(
+            dataset_name,
+            split=dataset_split,
+            **additional_dataset_kwargs
+        )
 
         self.kwargs = {
             "formatting": formatting,
@@ -177,6 +184,7 @@ def __init__(
         }
         self.training_paths = {}  # cache to store the training paths
         self.data_save_path = data_save_path
+        self.response_template = response_template
 
     def prepare_dataset(
         self,
@@ -186,6 +194,16 @@ def prepare_dataset(
         if model_name in self.training_paths:
             return self.training_paths[model_name]
 
+        if self.response_template:
+            if response_template is not None:
+                warnings.warn(
+                    "Response Template detected in data processing field, "
+                    "overriding response template. "
+                    "*** Old ***\n{response_template}\n"
+                    "*** New ***\n{self.response_template}"
+                )
+            response_template = self.response_template
+
         if self.kwargs["tokenize"]:
             tokenizer = AutoTokenizer.from_pretrained(model_name)
 
@@ -257,8 +275,8 @@ def convert_keyvalue_arguments_to_list(args_dict: Dict):
             # otherwise if a regular argument
             if val is None:
                 warnings.warn(
-                    f"Argument '{arg}' is not a true/false argument andhad a 'None' value ",
-                    "and thus will be ignored.",
+                    f"Argument '{arg}' is not a true/false argument and "
+                    "had a 'None' value and thus will be ignored.",
                 )
                 continue
 
@@ -668,8 +686,10 @@ def prepare_arguments(args, benchmark_dataset: BenchmarkDataset):
             print(f"Scenario '{_scn_name}' has matrix '{k}' of len {len(v)}")
             scn_factor *= len(v)
 
+        # scenario-specific constants should overwrite any similar values in defaults
+        defaults = {k:v for k, v in defaults.items() if k not in scenario_constants}
         # update defaults with scenario constants
-        constants = {**scenario_constants, **defaults}
+        constants = {**defaults, **scenario_constants}
         # Remove any empty variables and combine matrices to dictionary to cartesian product on
         combined_matrices = {**scenario_matrices, **experiment_matrices}
         products = ConfigUtils.cartesian_product_on_dict(combined_matrices)
@@ -684,12 +704,9 @@ def prepare_arguments(args, benchmark_dataset: BenchmarkDataset):
             # prepare the dataset
             training_path = benchmark_dataset.prepare_dataset(
                 x["model_name_or_path"],
-                (
-                    x[HF_ARG_RESPONSE_TEMPLATE]
-                    if HF_ARG_RESPONSE_TEMPLATE in x
-                    else constants.get(HF_ARG_RESPONSE_TEMPLATE)
-                ),
+                constants.get(HF_ARG_RESPONSE_TEMPLATE),
             )
+
             # update
             x[HF_ARG_TRAINING_DATA_PATH] = training_path
 

scripts/benchmarks/compare_with_reference.py

@@ -172,9 +172,19 @@ def main(
         help="the acceptable relative difference from the reference value.",
     )
 
-    parser.add_argument("--indices", default=DEFAULT_INDICES, nargs="+")
+    parser.add_argument(
+        "--indices",
+        default=DEFAULT_INDICES,
+        nargs="+",
+        help="list of column names to use as index for merging between old and new benchmark results",
+    )
 
-    parser.add_argument("--plot_columns", default=DEFAULT_PLOT_COLUMNS, nargs="+")
+    parser.add_argument(
+        "--plot_columns", 
+        default=DEFAULT_PLOT_COLUMNS, 
+        nargs="+"
+        help="list of metric names in benchmark results to analyze visually",
+    )
 
     args = parser.parse_args()
     main(