Add Cache Miss/Hit Test

aiu_fms_testing_utils/testing/utils.py

@@ -0,0 +1,24 @@
+from collections.abc import Iterable
+
+
+def format_kwargs_to_string(**kwargs):
+    """
+    Turns kwargs into a str with variable names using `-`, variables separated by `_` and iterable separated by `,`
+    """
+    formatted_pairs = []
+    for key, value in sorted(kwargs.items()):
+        formatted_value = None
+        if isinstance(value, str):
+            formatted_value = value
+        elif isinstance(value, Iterable):
+            formatted_value = ",".join(map(str, value))
+        elif value:
+            formatted_value = str(value)
+        # only append if formatted_value exists
+        if formatted_value:
+            # Keep previous convention of variable names with `-` instead of `_`
+            formatted_pairs.append(
+                f"{key.replace('_', '-')}-{formatted_value.replace('/', '--')}"
+            )
+
+    return "_".join(formatted_pairs)

aiu_fms_testing_utils/testing/validation.py

@@ -5,6 +5,9 @@
 from aiu_fms_testing_utils.utils.aiu_setup import dprint
 from aiu_fms_testing_utils._version import version_tuple
 import os
+from aiu_fms_testing_utils.testing.utils import format_kwargs_to_string
+
+import hashlib
 
 
 class LogitsExtractorHook(
@@ -125,13 +128,7 @@ def __len__(self):
 
 
 def get_default_validation_prefix(
-    model_id: str,
-    max_new_tokens: int,
-    batch_size: int,
-    seq_length: int,
-    dtype: str,
-    attn_type: str,
-    aftu_version: str,
+    **kwargs,
 ):
     """
     Args:
@@ -144,9 +141,17 @@ def get_default_validation_prefix(
         aftu_version (str): introduced in v0.3.0 to track changed in log
 
     Returns:
-        str: A prefix that will be prepended to the file name
+        str: A hashed prefix that will be prepended to the file name
     """
-    return f"{model_id.replace('/', '--')}_max-new-tokens-{max_new_tokens}_batch-size-{batch_size}_seq-length-{seq_length}_dtype-{dtype}_attn-type-{attn_type}.{aftu_version}"
+    aftu_version = kwargs.pop(
+        "aftu_version", ".".join([str(_) for _ in version_tuple[:3]])
+    )
+    kwargs_str = format_kwargs_to_string(**kwargs)
+
+    filename = f"{kwargs_str}"
+    hash_object = hashlib.sha256(filename.encode("utf-8"))
+    hex_digest = hash_object.hexdigest()
+    return f"{hex_digest}_{aftu_version}"
 
 
 def load_validation_information(
@@ -416,11 +421,14 @@ def get_validation_info_path(
     aftu_version: Optional[Tuple[int, int, int]] = None,
     device_type: str = "cpu",
     dtype: str = "fp16",
+    **kwargs,
 ):
     if aftu_version is None:
         aftu_version = version_tuple
 
-    validation_file_name = f"{get_default_validation_prefix(model_variant, max_new_tokens, batch_size, seq_length, dtype, attn_type, '.'.join([str(_) for _ in aftu_version[:3]]))}.{device_type}_validation_info.{seed}.out"
+    sample_key = kwargs.get("sample_key", None)
+
+    validation_file_name = f"{get_default_validation_prefix(aftu_version='.'.join([str(_) for _ in aftu_version[:3]]), model_id=model_variant, max_new_tokens=max_new_tokens, batch_size=batch_size, seq_length=seq_length, dtype=dtype, attn_type=attn_type, sample_key=sample_key)}.{device_type}_validation_info.{seed}.out"
     full_path = os.path.join(validation_info_dir, validation_file_name)
     return full_path
 
@@ -452,10 +460,12 @@ def find_validation_info_path(
     version_allow_decrement: bool = False,
     device_type: str = "cpu",
     dtype: str = "fp16",
+    **kwargs,
 ):
     """
     Find the validation info path if it exists, otherwise return None
     """
+    sample_key = kwargs.get("sample_key", None)
 
     if aftu_version is None:
         loc_version_tuple = version_tuple[:3]
@@ -476,6 +486,7 @@ def find_validation_info_path(
             loc_version_tuple,
             device_type,
             dtype,
+            sample_key=sample_key,
         )
         # if the path is found, we are done searching and can return
         if os.path.exists(full_path):

aiu_fms_testing_utils/utils/__init__.py

@@ -11,6 +11,7 @@
 
 from aiu_fms_testing_utils.utils.aiu_setup import dprint, rank, world_size
 from transformers.tokenization_utils_base import PreTrainedTokenizerBase
+from aiu_fms_testing_utils.testing.utils import format_kwargs_to_string
 
 from fms.utils.generation import pad_input_ids
 import torch
@@ -482,6 +483,7 @@ def sample_rag_factoid_requests(
     enforce_sizes: List[int] = [],
     truncation: bool = False,
     pad_multiple: int = 64,
+    return_key: bool = False,
 ) -> List[Tuple[str, int]]:
     if not os.path.exists(dataset_path):
         print("error dataset does not exist")
@@ -492,7 +494,7 @@ def sample_rag_factoid_requests(
         for line in f:
             dataset.append(line)
 
-    return __sample_requests(
+    sample_request = __sample_requests(
         dataset,
         num_requests,
         tokenizer,
@@ -506,6 +508,24 @@ def sample_rag_factoid_requests(
         _cached_dataset_key=dataset_path,
     )
 
+    if return_key:
+        sample_key: str = format_kwargs_to_string(
+            dataset="rag_factoid",
+            num_requests=num_requests,
+            tokenizer=tokenizer.name_or_path.replace("/", "--"),
+            prompt_length_min=prompt_length_min,
+            prompt_length_max=prompt_length_max,
+            seed=seed,
+            enforce_heterogeneous=enforce_heterogeneous,
+            enforce_sizes=enforce_sizes,
+            truncate=truncation,
+            pad_multiple=pad_multiple,
+        )
+
+        return sample_request, sample_key
+    else:
+        return sample_request
+
 
 def sample_sharegpt_requests(
     dataset_path: str,
@@ -518,6 +538,7 @@ def sample_sharegpt_requests(
     enforce_sizes: List[int] | None = None,
     truncation: bool = False,
     pad_multiple: int = 64,
+    return_key: bool = False,
 ) -> List[Tuple[str, int]]:
     if not os.path.exists(dataset_path):
         print("downloading share-gpt dataset as it does not exist")
@@ -543,7 +564,7 @@ def sample_sharegpt_requests(
     dataset = [data for data in dataset if len(data["conversations"]) >= 2]
     dataset: List[str] = [data["conversations"][0]["value"] for data in dataset]
 
-    return __sample_requests(
+    sample_request = __sample_requests(
         dataset,
         num_requests,
         tokenizer,
@@ -557,6 +578,23 @@ def sample_sharegpt_requests(
         _cached_dataset_key=dataset_path,
     )
 
+    if return_key:
+        sample_key: str = format_kwargs_to_string(
+            dataset="sharegpt",
+            num_requests=num_requests,
+            tokenizer=tokenizer.name_or_path.replace("/", "--"),
+            prompt_length_min=prompt_length_min,
+            prompt_length_max=prompt_length_max,
+            seed=seed,
+            enforce_heterogeneous=enforce_heterogeneous,
+            enforce_sizes=enforce_sizes,
+            truncate=truncation,
+            pad_multiple=pad_multiple,
+        )
+        return sample_request, sample_key
+    else:
+        return sample_request
+
 
 def sample_squad_v2_qa_requests(
     dataset_path: str,
@@ -569,6 +607,7 @@ def sample_squad_v2_qa_requests(
     enforce_sizes: List[int] | None = None,
     truncation: bool = False,
     pad_multiple: int = 64,
+    return_key: bool = False,
 ) -> List[Tuple[str, int]]:
     from datasets import load_dataset
 
@@ -582,7 +621,7 @@ def sample_squad_v2_qa_requests(
 
     ds = [f"{data['context']}\n{data['question']}" for data in ds]
 
-    return __sample_requests(
+    sample_request = __sample_requests(
         ds,
         num_requests,
         tokenizer,
@@ -595,6 +634,23 @@ def sample_squad_v2_qa_requests(
         pad_multiple,
     )
 
+    if return_key:
+        sample_key: str = format_kwargs_to_string(
+            dataset="squad_v2",
+            num_requests=num_requests,
+            tokenizer=tokenizer.name_or_path.replace("/", "--"),
+            prompt_length_min=prompt_length_min,
+            prompt_length_max=prompt_length_max,
+            seed=seed,
+            enforce_heterogeneous=enforce_heterogeneous,
+            enforce_sizes=enforce_sizes,
+            truncate=truncation,
+            pad_multiple=pad_multiple,
+        )
+        return sample_request, sample_key
+    else:
+        return sample_request
+
 
 def prepare_inputs(
     batch_size, seq_length, tokenizer, ds_path, seed=0, ds_type="sharegpt"

scripts/drive_paged_programs.py

@@ -40,6 +40,7 @@
     get_programs_prompts,
     KVCACHE_NUM_BLOCKS_HINT,
 )
+from aiu_fms_testing_utils.testing.utils import format_kwargs_to_string
 
 parser = argparse.ArgumentParser(
     description="Script which will drive paged programs for debugging"
@@ -195,6 +196,10 @@
     custom_shape = (len(result), max([_[1] for _ in result]))
 
     def __custom_line_sampler(*args, **kwargs):
+        return_key = kwargs.get("return_key", False)
+        sample_key = format_kwargs_to_string(**kwargs)
+        if return_key:
+            return result, sample_key
         return result
 
     sampler = __custom_line_sampler
@@ -245,7 +250,7 @@ def __custom_line_sampler(*args, **kwargs):
 
 def __prepare_inputs(batch_size, seq_length, tokenizer, enforce_sizes=[], seed=0):
     start = time.time()
-    prompts_and_sizes = sampler(
+    prompts_and_sizes, sample_key = sampler(
         DATASET_PATH,
         batch_size,
         tokenizer,
@@ -254,6 +259,7 @@ def __prepare_inputs(batch_size, seq_length, tokenizer, enforce_sizes=[], seed=0
         seed,
         enforce_sizes=enforce_sizes,
         truncation=allow_truncation,
+        return_key=True,
     )
     end = time.time()
     if local_rank == 0:
@@ -274,7 +280,7 @@ def __prepare_inputs(batch_size, seq_length, tokenizer, enforce_sizes=[], seed=0
 
     input_ids, extra_kwargs = pad_input_ids(prompt_list, min_pad_length=seq_length)
     extra_kwargs["mask"] = extra_kwargs["mask"].to(torch.float16)
-    return input_ids, extra_kwargs
+    return input_ids, extra_kwargs, sample_key
 
 
 def __maybe_prepare_fp8_weights(model_in, is_fp8):
@@ -296,7 +302,9 @@ def __load_validation_info(
     tokenizer,
     seed,
     attn_type: str,
+    **kwargs,
 ):
+    sample_key = kwargs.get("sample_key", None)
     full_path = find_validation_info_path(
         args.validation_info_outputs_dir,
         model_variant,
@@ -307,6 +315,7 @@ def __load_validation_info(
         attn_type,
         version_allow_decrement=True,
         dtype=CPU_DTYPE,
+        sample_key=sample_key,
     )
     if full_path is not None:
         dprint(f"cpu validation info found for seed={seed} -- loading it")
@@ -367,13 +376,14 @@ def __load_validation_info(
 
 # warmup with any input so compiler produces criteria json
 # TODO: Swap this with __prepare_inputs once fix for shape_id is available
-# input_ids, extra_kwargs = __prepare_inputs(2, max_tkv, tokenizer)
+# input_ids, extra_kwargs, sample_key = __prepare_inputs(2, max_tkv, tokenizer)
 prompt_list = [torch.arange(0, 64, dtype=torch.int64)]
 # matching vllm warmup to pad to 2 on fp8, and no pad for fp16
 if is_fp8:
     prompt_list = prompt_list * 2
 input_ids, extra_kwargs = pad_input_ids(prompt_list, min_pad_length=64)
 extra_kwargs["mask"] = extra_kwargs["mask"].to(torch.float16)
+
 extra_kwargs["attn_name"] = ATTN_NAME
 if (
     "granite-3.3-8b-instruct" in model_variant
@@ -494,7 +504,7 @@ def parse_program_limit(limit_str: str) -> tuple[int, str]:
         for valid_prompt_shape in valid_prompt_shapes:
             if valid_prompt_shape == custom_shape:
                 enforce_sizes = [valid_prompt_shape[1]]
-                input_ids, extra_kwargs = __prepare_inputs(
+                input_ids, extra_kwargs, sample_key = __prepare_inputs(
                     valid_prompt_shape[0],
                     valid_prompt_shape[1],
                     tokenizer,
@@ -506,6 +516,7 @@ def parse_program_limit(limit_str: str) -> tuple[int, str]:
                         custom_shape,
                         input_ids,
                         extra_kwargs,
+                        sample_key,
                     )
                 ]
                 break
@@ -566,7 +577,7 @@ def parse_program_limit(limit_str: str) -> tuple[int, str]:
                             )
                         )
                     try:
-                        input_ids, extra_kwargs = __prepare_inputs(
+                        input_ids, extra_kwargs, sample_key = __prepare_inputs(
                             valid_prompt_shape[0],
                             valid_prompt_shape[1],
                             tokenizer,
@@ -578,6 +589,7 @@ def parse_program_limit(limit_str: str) -> tuple[int, str]:
                                 valid_prompt_shape,
                                 input_ids,
                                 extra_kwargs,
+                                sample_key,
                             )
                         )
                         used_keys.add(program_seq_key[0])
@@ -609,7 +621,7 @@ def __metric_calculator(r: torch.Tensor, t: torch.Tensor):
 
 failed_cases = []
 # for each program and valid prompt (batch size, sequence length)
-for program_id, valid_prompt, input_ids, extra_kwargs in valid_prompts:
+for program_id, valid_prompt, input_ids, extra_kwargs, sample_key in valid_prompts:
     extra_kwargs["attn_name"] = ATTN_NAME
     if (
         "granite-3.3-8b-instruct" in model_variant
@@ -634,6 +646,7 @@ def __metric_calculator(r: torch.Tensor, t: torch.Tensor):
             tokenizer,
             seed=0,
             attn_type=ATTN_NAME,
+            sample_key=sample_key,
         )
         # if the cpu validation info is not yet computed, compute it
         if cpu_validation_info is None:
@@ -657,6 +670,7 @@ def __metric_calculator(r: torch.Tensor, t: torch.Tensor):
                         0,
                         ATTN_NAME,
                         dtype=CPU_DTYPE,
+                        sample_key=sample_key,
                     )
                 )
 

scripts/generate_layers_metrics.py

@@ -473,7 +473,11 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens):
                 cos_sim = tensor_cos_sim(tensor_cpu_out, cuda_output)
 
             prefix = get_default_validation_prefix(
-                model_path, max_new_token, batch_size, seq_length, "float16"
+                model_id=model_path,
+                max_new_tokens=max_new_token,
+                batch_size=batch_size,
+                seq_length=seq_length,
+                dtype="float16",
             )
             layer_name = str(layer_key).replace("[", "").replace("]", "")