From 134c3b8a0aa3503f0fd1869c667a06266bbb3c70 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 12 Jun 2025 17:45:38 -0300 Subject: [PATCH 01/60] Initial generate_layers_metrics version Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 278 +++++++++++++++++++++++++++++ 1 file changed, 278 insertions(+) create mode 100644 scripts/generate_layers_metrics.py diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py new file mode 100644 index 0000000..f6fd210 --- /dev/null +++ b/scripts/generate_layers_metrics.py @@ -0,0 +1,278 @@ +import os +import time + +import pytest +import itertools +import torch + +from fms.utils import tokenizers +from fms.models import get_model +from fms.utils.generation import pad_input_ids, generate + + +from aiu_fms_testing_utils.utils import ( + sample_sharegpt_requests, + ids_for_prompt, +) +from aiu_fms_testing_utils.utils.aiu_setup import dprint + + + +ORIGINAL_HF_HOME = os.environ.get("HF_HOME", None) + +SHARE_GPT_DATASET_PATH = os.environ.get( + "SHARE_GPT_DATASET_PATH", os.path.expanduser("~/share_gpt.json") +) + +common_model_paths = "/tmp/models/ibm-granite/granite-3.2-8b-instruct" +common_batch_sizes = [1] +common_seq_lengths = [64] +common_max_new_tokens = [128] + +# pass custom model path list for eg: EXPORT FMS_TESTING_COMMON_MODEL_PATHS="/tmp/models/granite-3-8b-base,/tmp/models/granite-7b-base" +if isinstance(common_model_paths, str): + common_model_paths = common_model_paths.split(",") + +# pass custom common batch sizes as a comma separated str of ints +if isinstance(common_batch_sizes, str): + common_batch_sizes = [int(bs) for bs in common_batch_sizes.split(",")] + +# pass custom common seq lengths as a comma separated str of ints +if isinstance(common_seq_lengths, str): + common_seq_lengths = [int(sl) for sl in common_seq_lengths.split(",")] + +# pass custom common max new tokens as a comma separated str of ints +if isinstance(common_max_new_tokens, str): + common_max_new_tokens = [int(mnt) for mnt in common_max_new_tokens.split(",")] + +common_shapes = list( + itertools.product( + common_model_paths, + common_batch_sizes, + common_seq_lengths, + common_max_new_tokens, + ) +) + +def __prepare_inputs(batch_size, seq_length, tokenizer, seed=0): + prompts_and_sizes = sample_sharegpt_requests( + SHARE_GPT_DATASET_PATH, + batch_size, + tokenizer, + int(seq_length / 2), + seq_length, + seed, + ) + prompt_list = [] + for prompt, _ in prompts_and_sizes: + prompt_list.append(ids_for_prompt(prompt, tokenizer)) + + input_ids, padding_kwargs = pad_input_ids(prompt_list, min_pad_length=seq_length) + return input_ids, padding_kwargs + +def __infer_layer(warmup, model, max_len, device, + max_new_tokens, batch_size, tokenizer): + + + do_sample = False + use_cache = True + + prompts = __prepare_inputs(batch_size, max_len, tokenizer) + ids, pad_input_ids = prompts + + if "cuda" in device: + print("cuda prompts") + print(len(prompts)) + ids = ids.to("cuda") + + if hasattr(model.config, "ntk_scaling") and model.config.ntk_scaling: + max_seq_len = max(max_len, model.config.max_expected_seq_len) + else: + # without ntk scaling, extending the seq length too far gives bogus results. + max_seq_len = model.config.max_expected_seq_len + + result = generate( + model, + ids, + max_new_tokens=max_new_tokens, + use_cache=use_cache, + do_sample=do_sample, + max_seq_len=max_seq_len, + timing="e2e", + eos_token_id=None, + contiguous_cache=True, + extra_kwargs={}, + ) + result, timings = result + dprint(f"E2E timing information: {timings[0]:.3f}s") + if len(result.shape) == 1: + result = result.unsqueeze(0) + + if not warmup: + for i in range(result.shape[0]): + print(result[i]) + +def __register_call_layers(model, batch_size, device, seq_length, max_new_tokens, tokenizer): + layer_stack = [] + pt_compile_model_time = time.time() + + module_depth = {} + module_name = {} + + def register_depths(module, current_depth=0, name='model'): + module_depth[module] = current_depth + module_name[module] = name + parent=name + # if we are dealing with array of layers + array_layers = all(key.isdigit() for key in module._modules.keys()) + for name, child in module._modules.items(): + if array_layers: + register_depths(child, current_depth + 1, parent+'['+name+']') + else: + register_depths(child, current_depth + 1, parent+'.'+name) + + register_depths(model) + + def wrap_forward(layer): + original_forward = layer.forward + + def safe_forward(*args, **kwargs): + #print("In safe forward") + try: + #print("No Error") + return original_forward(*args, **kwargs) + except (RuntimeError,TypeError) as e: + print(f"Error in {layer.__class__.__name__}: {e}") + return torch.zeros_like(args[0]) if args else None + layer.forward = safe_forward + + + hooks = [] + def pre_hook_fn(module, input): + depth = module_depth.get(module, 0) + layer_name = module_name.get(module, 0) + prefix = '│ ' * depth + if len(input) == 0: return + input_shape_str = f"[{', '.join(map(str, input[0].shape))}]" + input_type = str(input[0].dtype) + if module.parameters() == None: return + param_size = sum(p.numel() for p in module.parameters() if p.requires_grad) + param_size_str = f"{param_size:,}" if param_size > 0 else "--" + print(f"DEBUG TOOL {prefix}├─{layer_name}() -> {module.__class__.__name__} : | Input(arg): {input_shape_str} | {input_type} | Params: {param_size_str}") + wrap_forward(module) + # save input for later use with outputs + module._debug_input = input + + def post_hook_fn(module, input, output): + print("post_hook_fn") + layer_name = module_name.get(module, 0) + # Save inputs and outputs + if hasattr(module, '_debug_input'): + print(module._debug_input) + print(output) + layer_stack.append((layer_name, output)) + # Clean up + delattr(module, '_debug_input') + + for name, layer in model.named_modules(): + print(name) + hooks.append(layer.register_forward_pre_hook(pre_hook_fn)) + hooks.append(layer.register_forward_hook(post_hook_fn)) + + + __infer_layer(warmup=True, + model= model, max_len=seq_length, + device=device, max_new_tokens=max_new_tokens, + batch_size=batch_size, tokenizer=tokenizer) + + for hook in hooks: + hook.remove() + + pt_compile_model_time = time.time() - pt_compile_model_time + dprint(f"PT compile complete, took {pt_compile_model_time:.3f}s") + + return layer_stack + +def test_common_shapes(model_path, batch_size, seq_length, max_new_tokens): + torch.manual_seed(42) + os.environ["COMPILATION_MODE"] = "offline_decoder" + + if "HF_HOME" not in os.environ: + os.environ["HF_HOME"] = "/tmp/models/hf_cache" + + model_path_kwargs = {"model_path": model_path} + micro_model_kwargs = {"architecture": "hf_pretrained"} + + get_model_kwargs = { + **model_path_kwargs, + **micro_model_kwargs, + } + + tokenizer = tokenizers.get_tokenizer(model_path) + + # prepare the cpu model + validation_model = get_model( + device_type="cpu", + data_type=torch.float32, + fused_weights=False, + **get_model_kwargs, + ) + + # prepare the cuda model + validation_model_cuda = get_model( + device_type="cuda", + data_type=torch.float16, + fused_weights=False, + **get_model_kwargs, + ).to("cuda") + + layer_stack_cpu = __register_call_layers(model=validation_model, + batch_size=batch_size, + device="cpu", + seq_length=seq_length, max_new_tokens=max_new_tokens, + tokenizer=tokenizer) + + layer_stack_cuda = __register_call_layers(model=validation_model_cuda, + batch_size=batch_size, + device="cuda", + seq_length=seq_length, max_new_tokens=max_new_tokens, + tokenizer=tokenizer) + + absolute_differences = [] + + for layer, cpu_out in layer_stack_cpu: + cuda_eq = [cuda_layer for cuda_layer in layer_stack_cuda if layer in cuda_layer] + print("cuda layer equivalent") + print(cuda_eq) + print(layer) + print("cpu output") + cuda_layer, cuda_out = cuda_eq[0] + print(len(cpu_out)) + + for layer, cuda_out in layer_stack_cuda: + cuda_eq = [cuda_layer for cuda_layer in layer_stack_cuda if layer in cuda_layer] + print(cuda_eq) + print(layer) + print("cuda output") + cuda_layer, cuda_out = cuda_eq[0] + print(len(cuda_out)) + + abs_diff = torch.abs(cpu_out - cuda_out).flatten().tolist() + absolute_differences.extend(abs_diff) + + if len(absolute_differences) == 0: + abs_diff = {"mean": float('nan'), "median": float('nan'), "q1": float('nan'), "q3": float('nan')} + + print("abs_diff") + print(abs_diff) + abs_diff_tensor = torch.tensor(absolute_differences) + abs_diff_tensor = torch.nan_to_num(abs_diff_tensor, nan=0.0) + mean_diff = torch.mean(abs_diff_tensor).item() + median_diff = torch.median(abs_diff_tensor).item() + +for model_id, max_new_token, batch_size, sequence_length in common_shapes: + print("testing ", "model_id-", model_id, ", max_new_tokens-", max_new_token, ", batch_size-",batch_size, ", seq_length-",sequence_length) + test_common_shapes(model_path=model_id, batch_size=batch_size, seq_length=sequence_length, max_new_tokens=max_new_token) + +# if local_rank == 0: +# write_csv(prob_diff_metrics, os.path.join(args.output_dir, f"{prefix}.diff_mean.csv"), "diff_mean") \ No newline at end of file From ce42e329dc7f5de876fa8149b78059658f6c9bb6 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Mon, 16 Jun 2025 10:18:08 -0300 Subject: [PATCH 02/60] Initial version of inference with pre and post hooks Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 81 ++++++++++++++++-------------- 1 file changed, 44 insertions(+), 37 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index f6fd210..d6871f7 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -1,7 +1,6 @@ import os import time -import pytest import itertools import torch @@ -9,6 +8,7 @@ from fms.models import get_model from fms.utils.generation import pad_input_ids, generate +from aiu_fms_testing_utils.testing.validation import get_default_validation_prefix from aiu_fms_testing_utils.utils import ( sample_sharegpt_requests, @@ -24,11 +24,14 @@ "SHARE_GPT_DATASET_PATH", os.path.expanduser("~/share_gpt.json") ) -common_model_paths = "/tmp/models/ibm-granite/granite-3.2-8b-instruct" +common_model_paths = "ibm-granite/granite-3.2-8b-instruct" common_batch_sizes = [1] common_seq_lengths = [64] common_max_new_tokens = [128] +output_dir = os.environ.get("OUTPUT_PATH", os.path.expanduser("~/tmp/output") +) + # pass custom model path list for eg: EXPORT FMS_TESTING_COMMON_MODEL_PATHS="/tmp/models/granite-3-8b-base,/tmp/models/granite-7b-base" if isinstance(common_model_paths, str): common_model_paths = common_model_paths.split(",") @@ -63,6 +66,8 @@ def __prepare_inputs(batch_size, seq_length, tokenizer, seed=0): seq_length, seed, ) + print(prompts_and_sizes) + ## TODO: for each prompt prompt_list = [] for prompt, _ in prompts_and_sizes: prompt_list.append(ids_for_prompt(prompt, tokenizer)) @@ -168,14 +173,12 @@ def post_hook_fn(module, input, output): layer_name = module_name.get(module, 0) # Save inputs and outputs if hasattr(module, '_debug_input'): - print(module._debug_input) print(output) layer_stack.append((layer_name, output)) # Clean up delattr(module, '_debug_input') for name, layer in model.named_modules(): - print(name) hooks.append(layer.register_forward_pre_hook(pre_hook_fn)) hooks.append(layer.register_forward_hook(post_hook_fn)) @@ -193,14 +196,21 @@ def post_hook_fn(module, input, output): return layer_stack -def test_common_shapes(model_path, batch_size, seq_length, max_new_tokens): +def write_csv(l, path, metric): + with open(path, 'w') as f: + f.write(f'{metric}\n') + for t in l: + f.write(f"{t[2].item()}\n") + f.close() + +def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): torch.manual_seed(42) os.environ["COMPILATION_MODE"] = "offline_decoder" if "HF_HOME" not in os.environ: os.environ["HF_HOME"] = "/tmp/models/hf_cache" - model_path_kwargs = {"model_path": model_path} + model_path_kwargs = {"variant": model_path} micro_model_kwargs = {"architecture": "hf_pretrained"} get_model_kwargs = { @@ -224,7 +234,7 @@ def test_common_shapes(model_path, batch_size, seq_length, max_new_tokens): data_type=torch.float16, fused_weights=False, **get_model_kwargs, - ).to("cuda") + ) layer_stack_cpu = __register_call_layers(model=validation_model, batch_size=batch_size, @@ -240,39 +250,36 @@ def test_common_shapes(model_path, batch_size, seq_length, max_new_tokens): absolute_differences = [] - for layer, cpu_out in layer_stack_cpu: - cuda_eq = [cuda_layer for cuda_layer in layer_stack_cuda if layer in cuda_layer] - print("cuda layer equivalent") - print(cuda_eq) - print(layer) - print("cpu output") - cuda_layer, cuda_out = cuda_eq[0] - print(len(cpu_out)) + assert len(layer_stack_cuda) == len(layer_stack_cpu) for layer, cuda_out in layer_stack_cuda: - cuda_eq = [cuda_layer for cuda_layer in layer_stack_cuda if layer in cuda_layer] - print(cuda_eq) - print(layer) - print("cuda output") - cuda_layer, cuda_out = cuda_eq[0] - print(len(cuda_out)) + for cpu_layer, cpu_output in layer_stack_cpu: + if cpu_layer == layer: + print("CPU Layer {} GPU Layer {}".format(cpu_layer, layer)) - abs_diff = torch.abs(cpu_out - cuda_out).flatten().tolist() - absolute_differences.extend(abs_diff) + tensor_cpu_cuda_out = cuda_out.to(torch.device('cpu')) + abs_diff = torch.abs(cpu_output - tensor_cpu_cuda_out).flatten().tolist() + absolute_differences.extend(abs_diff) if len(absolute_differences) == 0: - abs_diff = {"mean": float('nan'), "median": float('nan'), "q1": float('nan'), "q3": float('nan')} - - print("abs_diff") - print(abs_diff) - abs_diff_tensor = torch.tensor(absolute_differences) - abs_diff_tensor = torch.nan_to_num(abs_diff_tensor, nan=0.0) - mean_diff = torch.mean(abs_diff_tensor).item() - median_diff = torch.median(abs_diff_tensor).item() - -for model_id, max_new_token, batch_size, sequence_length in common_shapes: - print("testing ", "model_id-", model_id, ", max_new_tokens-", max_new_token, ", batch_size-",batch_size, ", seq_length-",sequence_length) - test_common_shapes(model_path=model_id, batch_size=batch_size, seq_length=sequence_length, max_new_tokens=max_new_token) + abs_diff = {"mean": float('nan'), "median": float('nan'), "q1": float('nan'), "q3": float('nan')} + + abs_diff_tensor = torch.tensor(absolute_differences) + abs_diff_tensor = torch.nan_to_num(abs_diff_tensor, nan=0.0) + mean_diff = torch.mean(abs_diff_tensor).item() + median_diff = torch.median(abs_diff_tensor).item() -# if local_rank == 0: -# write_csv(prob_diff_metrics, os.path.join(args.output_dir, f"{prefix}.diff_mean.csv"), "diff_mean") \ No newline at end of file + return abs_diff, mean_diff, median_diff + +for model_id, batch_size, sequence_length, max_new_token in common_shapes: + print("testing ", "model_id-", model_id, ", max_new_tokens-", max_new_token, ", batch_size-",batch_size, ", seq_length-",sequence_length) + abs_diff, mean_diff, median_diff = generate_layers_metrics(model_path=model_id, batch_size=batch_size, seq_length=sequence_length, max_new_tokens=max_new_token) + + prefix = get_default_validation_prefix(model_id, max_new_token, batch_size, 0, 'float16') + if os.path.exists(os.path.join(output_dir, f"{prefix}.abs_diff.csv")): + print("skipping metric generation as it has already been done") + exit(0) + write_csv(abs_diff, os.path.join(output_dir, f"{prefix}.abs_diff.csv"), "abs_diff") + write_csv(mean_diff, os.path.join(output_dir, f"{prefix}.mean_diff.csv"), "mean_diff") + write_csv(median_diff, os.path.join(output_dir, f"{prefix}.median_diff.csv"), "median_diff") + \ No newline at end of file From c48e27182d5dd60bd481fa86d5202b9d42528d0a Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 17 Jun 2025 10:28:46 -0300 Subject: [PATCH 03/60] Checks output type Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index d6871f7..14906f6 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -257,7 +257,13 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): if cpu_layer == layer: print("CPU Layer {} GPU Layer {}".format(cpu_layer, layer)) - tensor_cpu_cuda_out = cuda_out.to(torch.device('cpu')) + if not type(cuda_out) is tuple: + tensor_cpu_cuda_out = cuda_out.to(torch.device('cpu')) + else: + cuda_out_unique = set(cuda_out) + keys = {key: value for key, value in zip(cuda_out_unique, range(len(cuda_out_unique)))} + tensor_cpu_cuda_out = torch.zeros(size=(len(cuda_out), len(keys))) + abs_diff = torch.abs(cpu_output - tensor_cpu_cuda_out).flatten().tolist() absolute_differences.extend(abs_diff) From f78909d10a4d8e28cde9af9f202cdb6c443588ca Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 17 Jun 2025 11:44:31 -0300 Subject: [PATCH 04/60] Convert tensor method and save files Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 60 +++++++++++++++++------------- 1 file changed, 35 insertions(+), 25 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 14906f6..eeab1a2 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -29,8 +29,7 @@ common_seq_lengths = [64] common_max_new_tokens = [128] -output_dir = os.environ.get("OUTPUT_PATH", os.path.expanduser("~/tmp/output") -) +output_dir = os.environ.get("OUTPUT_PATH", "/tmp/output") # pass custom model path list for eg: EXPORT FMS_TESTING_COMMON_MODEL_PATHS="/tmp/models/granite-3-8b-base,/tmp/models/granite-7b-base" if isinstance(common_model_paths, str): @@ -199,10 +198,18 @@ def post_hook_fn(module, input, output): def write_csv(l, path, metric): with open(path, 'w') as f: f.write(f'{metric}\n') - for t in l: - f.write(f"{t[2].item()}\n") + if type(l) is list: + for t in l: + f.write(f"{t}\n") + else: + f.write(f"{l}\n") f.close() +def convert_tensor(output): + out_unique = set(output) + keys = {key: value for key, value in zip(out_unique, range(len(out_unique)))} + return torch.zeros(size=(len(output), len(keys))) + def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): torch.manual_seed(42) os.environ["COMPILATION_MODE"] = "offline_decoder" @@ -249,43 +256,46 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): tokenizer=tokenizer) absolute_differences = [] + mean_diff_list = [] + median_diff_list = [] + abs_diff_list = [] assert len(layer_stack_cuda) == len(layer_stack_cpu) - for layer, cuda_out in layer_stack_cuda: + for layer, cuda_output in layer_stack_cuda: for cpu_layer, cpu_output in layer_stack_cpu: if cpu_layer == layer: print("CPU Layer {} GPU Layer {}".format(cpu_layer, layer)) - if not type(cuda_out) is tuple: - tensor_cpu_cuda_out = cuda_out.to(torch.device('cpu')) + if not type(cuda_output) is tuple: + tensor_cuda_out = cuda_output.to(torch.device('cpu')) else: - cuda_out_unique = set(cuda_out) - keys = {key: value for key, value in zip(cuda_out_unique, range(len(cuda_out_unique)))} - tensor_cpu_cuda_out = torch.zeros(size=(len(cuda_out), len(keys))) - - abs_diff = torch.abs(cpu_output - tensor_cpu_cuda_out).flatten().tolist() - absolute_differences.extend(abs_diff) + tensor_cuda_out = convert_tensor(cuda_output) + if type(cpu_output) is tuple: + tensor_cpu_out = convert_tensor(cpu_output) + else: + tensor_cpu_out = cpu_output + abs_diff = torch.abs(tensor_cpu_out - tensor_cuda_out).flatten().tolist() + absolute_differences.extend(abs_diff) if len(absolute_differences) == 0: abs_diff = {"mean": float('nan'), "median": float('nan'), "q1": float('nan'), "q3": float('nan')} - abs_diff_tensor = torch.tensor(absolute_differences) - abs_diff_tensor = torch.nan_to_num(abs_diff_tensor, nan=0.0) - mean_diff = torch.mean(abs_diff_tensor).item() - median_diff = torch.median(abs_diff_tensor).item() + abs_diff_tensor = torch.tensor(absolute_differences) + abs_diff_tensor = torch.nan_to_num(abs_diff_tensor, nan=0.0) + mean_diff = torch.mean(abs_diff_tensor).item() + median_diff = torch.median(abs_diff_tensor).item() + + prefix = get_default_validation_prefix(model_id, max_new_token, batch_size, 0, 'float16') - return abs_diff, mean_diff, median_diff + write_csv(abs_diff, os.path.join(output_dir, f"{prefix}--{layer}.abs_diff.csv"), "abs_diff") + write_csv(mean_diff, os.path.join(output_dir, f"{prefix}--{layer}.mean_diff.csv"), "mean_diff") + write_csv(median_diff, os.path.join(output_dir, f"{prefix}--{layer}.median_diff.csv"), "median_diff") + + print(f"Completed {model_id} layers' metrics generation") for model_id, batch_size, sequence_length, max_new_token in common_shapes: print("testing ", "model_id-", model_id, ", max_new_tokens-", max_new_token, ", batch_size-",batch_size, ", seq_length-",sequence_length) abs_diff, mean_diff, median_diff = generate_layers_metrics(model_path=model_id, batch_size=batch_size, seq_length=sequence_length, max_new_tokens=max_new_token) - prefix = get_default_validation_prefix(model_id, max_new_token, batch_size, 0, 'float16') - if os.path.exists(os.path.join(output_dir, f"{prefix}.abs_diff.csv")): - print("skipping metric generation as it has already been done") - exit(0) - write_csv(abs_diff, os.path.join(output_dir, f"{prefix}.abs_diff.csv"), "abs_diff") - write_csv(mean_diff, os.path.join(output_dir, f"{prefix}.mean_diff.csv"), "mean_diff") - write_csv(median_diff, os.path.join(output_dir, f"{prefix}.median_diff.csv"), "median_diff") \ No newline at end of file From 9b4853cd8dbec3aeaeffd7e67a95e02eb439fd2e Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 18 Jun 2025 16:52:13 -0300 Subject: [PATCH 05/60] Adds Cosine Similarity + prefix files Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 51 ++++++++++++++++-------------- 1 file changed, 27 insertions(+), 24 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index eeab1a2..8aded0e 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -3,6 +3,7 @@ import itertools import torch +import torch.nn as nn from fms.utils import tokenizers from fms.models import get_model @@ -24,10 +25,13 @@ "SHARE_GPT_DATASET_PATH", os.path.expanduser("~/share_gpt.json") ) -common_model_paths = "ibm-granite/granite-3.2-8b-instruct" -common_batch_sizes = [1] -common_seq_lengths = [64] -common_max_new_tokens = [128] +common_model_paths = common_model_paths = os.environ.get( + "MODEL_PATHS", + ["ibm-granite/granite-3.2-8b-instruct"], +) +common_batch_sizes = os.environ.get("BATCH_SIZES", [1, 2, 4, 8]) +common_seq_lengths = os.environ.get("SEQ_LENGTHS", [64, 2048]) +common_max_new_tokens = os.environ.get("MAX_NEW_TOKENS", [128]) output_dir = os.environ.get("OUTPUT_PATH", "/tmp/output") @@ -196,17 +200,20 @@ def post_hook_fn(module, input, output): return layer_stack def write_csv(l, path, metric): + print("saving file") with open(path, 'w') as f: f.write(f'{metric}\n') - if type(l) is list: + if not type(l) is float: + print("saving list") for t in l: - f.write(f"{t}\n") + f.write(f"{t}\n") else: - f.write(f"{l}\n") + print("saving float") + f.write(f"{l}\n") f.close() def convert_tensor(output): - out_unique = set(output) + out_unique = set(list(itertools.chain.from_iterable(output))) keys = {key: value for key, value in zip(out_unique, range(len(out_unique)))} return torch.zeros(size=(len(output), len(keys))) @@ -256,9 +263,6 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): tokenizer=tokenizer) absolute_differences = [] - mean_diff_list = [] - median_diff_list = [] - abs_diff_list = [] assert len(layer_stack_cuda) == len(layer_stack_cpu) @@ -275,27 +279,26 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): tensor_cpu_out = convert_tensor(cpu_output) else: tensor_cpu_out = cpu_output - + print("tensor converted... get torch abs diff") abs_diff = torch.abs(tensor_cpu_out - tensor_cuda_out).flatten().tolist() - absolute_differences.extend(abs_diff) - if len(absolute_differences) == 0: - abs_diff = {"mean": float('nan'), "median": float('nan'), "q1": float('nan'), "q3": float('nan')} - - abs_diff_tensor = torch.tensor(absolute_differences) - abs_diff_tensor = torch.nan_to_num(abs_diff_tensor, nan=0.0) - mean_diff = torch.mean(abs_diff_tensor).item() - median_diff = torch.median(abs_diff_tensor).item() + cos = nn.CosineSimilarity() + cos_sim = cos(tensor_cpu_out - tensor_cuda_out) + + print("abs_diff and cos_sim calculated") + absolute_differences.append(abs_diff) + print("abs_diff list extended") prefix = get_default_validation_prefix(model_id, max_new_token, batch_size, 0, 'float16') + layer_name = str(layer).replace('[','').replace(']', '') - write_csv(abs_diff, os.path.join(output_dir, f"{prefix}--{layer}.abs_diff.csv"), "abs_diff") - write_csv(mean_diff, os.path.join(output_dir, f"{prefix}--{layer}.mean_diff.csv"), "mean_diff") - write_csv(median_diff, os.path.join(output_dir, f"{prefix}--{layer}.median_diff.csv"), "median_diff") + print("saving files") + write_csv(abs_diff, os.path.join(output_dir, f"{prefix}--{layer_name}.abs_diff.csv"), "abs_diff") + write_csv(cos_sim, os.path.join(output_dir, f"{prefix}--{layer_name}.cos_sim.csv"), "cos_sim") print(f"Completed {model_id} layers' metrics generation") for model_id, batch_size, sequence_length, max_new_token in common_shapes: print("testing ", "model_id-", model_id, ", max_new_tokens-", max_new_token, ", batch_size-",batch_size, ", seq_length-",sequence_length) - abs_diff, mean_diff, median_diff = generate_layers_metrics(model_path=model_id, batch_size=batch_size, seq_length=sequence_length, max_new_tokens=max_new_token) + generate_layers_metrics(model_path=model_id, batch_size=batch_size, seq_length=sequence_length, max_new_tokens=max_new_token) \ No newline at end of file From f212b8a40496194b8073ef71a400ff3f6f913447 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 19 Jun 2025 19:26:48 -0300 Subject: [PATCH 06/60] Adds dim=1 to Cosine sim Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 8aded0e..e972968 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -267,26 +267,31 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): assert len(layer_stack_cuda) == len(layer_stack_cpu) for layer, cuda_output in layer_stack_cuda: + tensor_cuda_out = None + tensor_cpu_out = None + abs_diff = None for cpu_layer, cpu_output in layer_stack_cpu: if cpu_layer == layer: print("CPU Layer {} GPU Layer {}".format(cpu_layer, layer)) if not type(cuda_output) is tuple: - tensor_cuda_out = cuda_output.to(torch.device('cpu')) + tensor_cuda_out = cuda_output else: tensor_cuda_out = convert_tensor(cuda_output) if type(cpu_output) is tuple: tensor_cpu_out = convert_tensor(cpu_output) else: - tensor_cpu_out = cpu_output + tensor_cpu_out = cpu_output.to('cuda') print("tensor converted... get torch abs diff") abs_diff = torch.abs(tensor_cpu_out - tensor_cuda_out).flatten().tolist() - cos = nn.CosineSimilarity() - cos_sim = cos(tensor_cpu_out - tensor_cuda_out) - - print("abs_diff and cos_sim calculated") + print("abs_diff calculated") + cos = nn.CosineSimilarity(dim=1) + cos_sim = cos(tensor_cpu_out, tensor_cuda_out) + print(cos_sim) + absolute_differences.append(abs_diff) - print("abs_diff list extended") + print("abs_diff list appended") + print(len(absolute_differences)) prefix = get_default_validation_prefix(model_id, max_new_token, batch_size, 0, 'float16') layer_name = str(layer).replace('[','').replace(']', '') From c9c54a43cc5e5faa54cbbe66bf62d56e08c54295 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 19 Jun 2025 21:26:53 -0300 Subject: [PATCH 07/60] Removes extra space Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index e972968..d02dddb 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -305,5 +305,3 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): for model_id, batch_size, sequence_length, max_new_token in common_shapes: print("testing ", "model_id-", model_id, ", max_new_tokens-", max_new_token, ", batch_size-",batch_size, ", seq_length-",sequence_length) generate_layers_metrics(model_path=model_id, batch_size=batch_size, seq_length=sequence_length, max_new_tokens=max_new_token) - - \ No newline at end of file From 315ff3551e62fed4b041a63286b916025e474b33 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Mon, 30 Jun 2025 17:55:30 -0300 Subject: [PATCH 08/60] Adds layer IO mode to get_thresholds Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 43 ++++++++++++++++++++++++------- 1 file changed, 34 insertions(+), 9 deletions(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 7dedb70..b826bb8 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -1,5 +1,4 @@ import glob -import os import numpy as np import argparse import os @@ -31,22 +30,48 @@ required=True, help="Path where the thresholds output from the generate_metrics.py script were stored.", ) +parser.add_argument( + "--layer_io", + action="store_true", + help="Sets the metric generation mode to layers IO" +) args = parser.parse_args() models = [model.replace("/", "--") for model in args.models] metrics = [metric for metric in args.metrics] file_base = args.file_base +layer_mode = args.file_base if args.file_base else False for model in models: for metric in metrics: path = os.path.join(file_base, f"{model}*{metric}*.csv") metric_files = glob.glob(path) - metric_list = [] - for metric_file in metric_files: + if not layer_mode: + metric_list = [] + for metric_file in metric_files: + + with open(metric_file, "r") as file: + next(file) + for line in file: + metric_list.append(float(line)) + print(f"found {len(metric_files)} metric files") + print(model, metric, np.percentile(metric_list, 99.0)) + else: + metric_list = [] + layers = [] + for metric_file in metric_files: + layer_dict = {} + layer_name = metric_file.split("--")[-1].replace(".{}".format(metric), "") + with open(metric_file, "r") as file: + next(file) + for line in file: + metric_list.append(float(line)) + layer_dict[layer_name] = metric_list + layers.append(layer_dict) + print(f"found {len(layers)} layers metric files") + + for l in layers: + for key in l.keys(): + print(f"Layer {key} avg {metric}") + print(metric, np.percentile(l[key], 99.0)) - with open(metric_file, "r") as file: - next(file) - for line in file: - metric_list.append(float(line)) - print(f"found {len(metric_files)} metric files") - print(model, metric, np.percentile(metric_list, 99.0)) From 09c76c2617f665a295538eb58418cf372a429ff4 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 1 Jul 2025 15:49:51 -0300 Subject: [PATCH 09/60] Changes model_id to model_path Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index d02dddb..8a486e4 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -293,14 +293,14 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): print("abs_diff list appended") print(len(absolute_differences)) - prefix = get_default_validation_prefix(model_id, max_new_token, batch_size, 0, 'float16') + prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, 0, 'float16') layer_name = str(layer).replace('[','').replace(']', '') print("saving files") write_csv(abs_diff, os.path.join(output_dir, f"{prefix}--{layer_name}.abs_diff.csv"), "abs_diff") write_csv(cos_sim, os.path.join(output_dir, f"{prefix}--{layer_name}.cos_sim.csv"), "cos_sim") - print(f"Completed {model_id} layers' metrics generation") + print(f"Completed {model_path} layers' metrics generation") for model_id, batch_size, sequence_length, max_new_token in common_shapes: print("testing ", "model_id-", model_id, ", max_new_tokens-", max_new_token, ", batch_size-",batch_size, ", seq_length-",sequence_length) From 3afcac283fd3ab5e312d2bd958400446cd84fca3 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 1 Jul 2025 15:51:00 -0300 Subject: [PATCH 10/60] Fixes model_path assignment Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 8a486e4..e9accd6 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -25,7 +25,7 @@ "SHARE_GPT_DATASET_PATH", os.path.expanduser("~/share_gpt.json") ) -common_model_paths = common_model_paths = os.environ.get( +common_model_paths = os.environ.get( "MODEL_PATHS", ["ibm-granite/granite-3.2-8b-instruct"], ) From 3ce8e9c1263d5b03a9312485a7700c591f789ebb Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 1 Jul 2025 15:58:45 -0300 Subject: [PATCH 11/60] Save metrics to json Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index b826bb8..2064c7d 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -3,6 +3,8 @@ import argparse import os +import json + parser = argparse.ArgumentParser( description="Script to get thresholds metrics" ) @@ -62,6 +64,7 @@ for metric_file in metric_files: layer_dict = {} layer_name = metric_file.split("--")[-1].replace(".{}".format(metric), "") + layer_name = layer_name.replace(".csv","") with open(metric_file, "r") as file: next(file) for line in file: @@ -72,6 +75,10 @@ for l in layers: for key in l.keys(): - print(f"Layer {key} avg {metric}") - print(metric, np.percentile(l[key], 99.0)) + metric_val = np.percentile(l[key], 99.0) + print(f"Layer {key} avg {metric} = {metric_val}") + l[metric] = metric_val + f_result_path = os.path.join(file_base, f"{model}-{metric}.json") + with open(f_result_path, 'w') as fp: + json.dump(layers, fp) From 55b7811a3975039cc7149d5a39f4fba64f46d8e8 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 1 Jul 2025 16:08:32 -0300 Subject: [PATCH 12/60] Fix json results assignment Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 2064c7d..8e00c50 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -48,8 +48,9 @@ path = os.path.join(file_base, f"{model}*{metric}*.csv") metric_files = glob.glob(path) + metric_list = [] + if not layer_mode: - metric_list = [] for metric_file in metric_files: with open(metric_file, "r") as file: @@ -59,8 +60,9 @@ print(f"found {len(metric_files)} metric files") print(model, metric, np.percentile(metric_list, 99.0)) else: - metric_list = [] layers = [] + result_dict = {"model_id": model} + result_dict[metric] = [] for metric_file in metric_files: layer_dict = {} layer_name = metric_file.split("--")[-1].replace(".{}".format(metric), "") @@ -75,10 +77,13 @@ for l in layers: for key in l.keys(): + tmp = {} metric_val = np.percentile(l[key], 99.0) print(f"Layer {key} avg {metric} = {metric_val}") - l[metric] = metric_val - f_result_path = os.path.join(file_base, f"{model}-{metric}.json") - with open(f_result_path, 'w') as fp: - json.dump(layers, fp) + tmp[key] = metric_val + result_dict[metric].append(tmp) + + f_result_path = os.path.join(file_base, f"{model}-thresholds.json") + with open(f_result_path, 'w') as fp: + json.dump(result_dict, fp) From 54017ac714228bd1bec35e1277e4a63af72b41f3 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 1 Jul 2025 20:32:18 -0300 Subject: [PATCH 13/60] Adds python logger Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 68 +++++++++++++----------------- 1 file changed, 29 insertions(+), 39 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index e9accd6..a555e1b 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -17,6 +17,9 @@ ) from aiu_fms_testing_utils.utils.aiu_setup import dprint +import logging +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) ORIGINAL_HF_HOME = os.environ.get("HF_HOME", None) @@ -69,7 +72,6 @@ def __prepare_inputs(batch_size, seq_length, tokenizer, seed=0): seq_length, seed, ) - print(prompts_and_sizes) ## TODO: for each prompt prompt_list = [] for prompt, _ in prompts_and_sizes: @@ -89,8 +91,6 @@ def __infer_layer(warmup, model, max_len, device, ids, pad_input_ids = prompts if "cuda" in device: - print("cuda prompts") - print(len(prompts)) ids = ids.to("cuda") if hasattr(model.config, "ntk_scaling") and model.config.ntk_scaling: @@ -99,26 +99,27 @@ def __infer_layer(warmup, model, max_len, device, # without ntk scaling, extending the seq length too far gives bogus results. max_seq_len = model.config.max_expected_seq_len - result = generate( - model, - ids, - max_new_tokens=max_new_tokens, - use_cache=use_cache, - do_sample=do_sample, - max_seq_len=max_seq_len, - timing="e2e", - eos_token_id=None, - contiguous_cache=True, - extra_kwargs={}, - ) - result, timings = result - dprint(f"E2E timing information: {timings[0]:.3f}s") + with torch.no_grad(): + result = generate( + model, + ids, + max_new_tokens=max_new_tokens, + use_cache=use_cache, + do_sample=do_sample, + max_seq_len=max_seq_len, + timing="e2e", + eos_token_id=None, + contiguous_cache=True, + extra_kwargs={}, + ) + result, timings = result + logger.info(f"E2E timing information: {timings[0]:.3f}s") if len(result.shape) == 1: result = result.unsqueeze(0) if not warmup: for i in range(result.shape[0]): - print(result[i]) + logger.debug(result[i]) def __register_call_layers(model, batch_size, device, seq_length, max_new_tokens, tokenizer): layer_stack = [] @@ -145,12 +146,10 @@ def wrap_forward(layer): original_forward = layer.forward def safe_forward(*args, **kwargs): - #print("In safe forward") try: - #print("No Error") return original_forward(*args, **kwargs) except (RuntimeError,TypeError) as e: - print(f"Error in {layer.__class__.__name__}: {e}") + logger.error(f"Error in {layer.__class__.__name__}: {e}") return torch.zeros_like(args[0]) if args else None layer.forward = safe_forward @@ -166,17 +165,15 @@ def pre_hook_fn(module, input): if module.parameters() == None: return param_size = sum(p.numel() for p in module.parameters() if p.requires_grad) param_size_str = f"{param_size:,}" if param_size > 0 else "--" - print(f"DEBUG TOOL {prefix}├─{layer_name}() -> {module.__class__.__name__} : | Input(arg): {input_shape_str} | {input_type} | Params: {param_size_str}") + logger.info(f"DEBUG TOOL {prefix}├─{layer_name}() -> {module.__class__.__name__} : | Input(arg): {input_shape_str} | {input_type} | Params: {param_size_str}") wrap_forward(module) # save input for later use with outputs module._debug_input = input def post_hook_fn(module, input, output): - print("post_hook_fn") layer_name = module_name.get(module, 0) # Save inputs and outputs if hasattr(module, '_debug_input'): - print(output) layer_stack.append((layer_name, output)) # Clean up delattr(module, '_debug_input') @@ -195,20 +192,17 @@ def post_hook_fn(module, input, output): hook.remove() pt_compile_model_time = time.time() - pt_compile_model_time - dprint(f"PT compile complete, took {pt_compile_model_time:.3f}s") + logger.info(f"PT compile complete, took {pt_compile_model_time:.3f}s") return layer_stack def write_csv(l, path, metric): - print("saving file") with open(path, 'w') as f: f.write(f'{metric}\n') if not type(l) is float: - print("saving list") for t in l: f.write(f"{t}\n") else: - print("saving float") f.write(f"{l}\n") f.close() @@ -272,7 +266,7 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): abs_diff = None for cpu_layer, cpu_output in layer_stack_cpu: if cpu_layer == layer: - print("CPU Layer {} GPU Layer {}".format(cpu_layer, layer)) + logger.info("CPU Layer {} GPU Layer {}".format(cpu_layer, layer)) if not type(cuda_output) is tuple: tensor_cuda_out = cuda_output @@ -282,26 +276,22 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): tensor_cpu_out = convert_tensor(cpu_output) else: tensor_cpu_out = cpu_output.to('cuda') - print("tensor converted... get torch abs diff") + logger.debug("tensor converted... get torch abs diff") abs_diff = torch.abs(tensor_cpu_out - tensor_cuda_out).flatten().tolist() - print("abs_diff calculated") + logger.debug("abs_diff calculated") cos = nn.CosineSimilarity(dim=1) cos_sim = cos(tensor_cpu_out, tensor_cuda_out) - print(cos_sim) - - absolute_differences.append(abs_diff) - print("abs_diff list appended") - print(len(absolute_differences)) + logger.debug(cos_sim) prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, 0, 'float16') layer_name = str(layer).replace('[','').replace(']', '') - print("saving files") + logger.debug("saving files") write_csv(abs_diff, os.path.join(output_dir, f"{prefix}--{layer_name}.abs_diff.csv"), "abs_diff") write_csv(cos_sim, os.path.join(output_dir, f"{prefix}--{layer_name}.cos_sim.csv"), "cos_sim") - print(f"Completed {model_path} layers' metrics generation") + logger.info(f"Completed {model_path} layers' metrics generation") for model_id, batch_size, sequence_length, max_new_token in common_shapes: - print("testing ", "model_id-", model_id, ", max_new_tokens-", max_new_token, ", batch_size-",batch_size, ", seq_length-",sequence_length) + logger.info("testing ", "model_id-", model_id, ", max_new_tokens-", max_new_token, ", batch_size-",batch_size, ", seq_length-",sequence_length) generate_layers_metrics(model_path=model_id, batch_size=batch_size, seq_length=sequence_length, max_new_tokens=max_new_token) From ace8dfe929fb722fabee299707aa04f29b5d0b03 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 1 Jul 2025 20:48:14 -0300 Subject: [PATCH 14/60] Fix logs Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index a555e1b..82ba117 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -165,7 +165,7 @@ def pre_hook_fn(module, input): if module.parameters() == None: return param_size = sum(p.numel() for p in module.parameters() if p.requires_grad) param_size_str = f"{param_size:,}" if param_size > 0 else "--" - logger.info(f"DEBUG TOOL {prefix}├─{layer_name}() -> {module.__class__.__name__} : | Input(arg): {input_shape_str} | {input_type} | Params: {param_size_str}") + logger.info(f"{prefix}├─{layer_name}() -> {module.__class__.__name__} : | Input(arg): {input_shape_str} | {input_type} | Params: {param_size_str}") wrap_forward(module) # save input for later use with outputs module._debug_input = input @@ -293,5 +293,5 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): logger.info(f"Completed {model_path} layers' metrics generation") for model_id, batch_size, sequence_length, max_new_token in common_shapes: - logger.info("testing ", "model_id-", model_id, ", max_new_tokens-", max_new_token, ", batch_size-",batch_size, ", seq_length-",sequence_length) + logger.info(f"testing model_id-{model_id}, max_new_tokens-{max_new_token}, batch_size-{batch_size}, seq_length-{sequence_length}") generate_layers_metrics(model_path=model_id, batch_size=batch_size, seq_length=sequence_length, max_new_tokens=max_new_token) From 9934746a9f5cd160b63024f5e1e612ba8a5cd76a Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 2 Jul 2025 10:19:24 -0300 Subject: [PATCH 15/60] Adds env variable for LOG LEVEL Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 19 ++++++++++--------- tests/resources/get_thresholds.py | 2 +- 2 files changed, 11 insertions(+), 10 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 82ba117..640960d 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -1,5 +1,6 @@ import os import time +import logging import itertools import torch @@ -15,12 +16,11 @@ sample_sharegpt_requests, ids_for_prompt, ) -from aiu_fms_testing_utils.utils.aiu_setup import dprint -import logging -logger = logging.getLogger(__name__) -logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) +LOG_LEVEL = os.environ.get('LOG_LEVEL', 'INFO').upper() +logging.basicConfig(level=LOG_LEVEL, format="%(asctime)s %(message)s") ORIGINAL_HF_HOME = os.environ.get("HF_HOME", None) @@ -283,12 +283,13 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): cos_sim = cos(tensor_cpu_out, tensor_cuda_out) logger.debug(cos_sim) - prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, 0, 'float16') - layer_name = str(layer).replace('[','').replace(']', '') + prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, 0, 'float16') + layer_name = str(layer).replace('[','').replace(']', '') - logger.debug("saving files") - write_csv(abs_diff, os.path.join(output_dir, f"{prefix}--{layer_name}.abs_diff.csv"), "abs_diff") - write_csv(cos_sim, os.path.join(output_dir, f"{prefix}--{layer_name}.cos_sim.csv"), "cos_sim") + if not os.path.exists(os.path.join(output_dir, f"{prefix}--{layer_name}.abs_diff.csv")): + logger.debug("saving files") + write_csv(abs_diff, os.path.join(output_dir, f"{prefix}--{layer_name}.abs_diff.csv"), "abs_diff") + write_csv(cos_sim, os.path.join(output_dir, f"{prefix}--{layer_name}.cos_sim.csv"), "cos_sim") logger.info(f"Completed {model_path} layers' metrics generation") diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 8e00c50..2788f65 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -78,7 +78,7 @@ for l in layers: for key in l.keys(): tmp = {} - metric_val = np.percentile(l[key], 99.0) + metric_val = np.average(l[key]) print(f"Layer {key} avg {metric} = {metric_val}") tmp[key] = metric_val result_dict[metric].append(tmp) From 5e1f043c09edc0edf706e39f746b1ee0eae53f3f Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 2 Jul 2025 11:10:47 -0300 Subject: [PATCH 16/60] unsqueeze cosine similarity Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 640960d..14d8ef1 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -255,8 +255,6 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): device="cuda", seq_length=seq_length, max_new_tokens=max_new_tokens, tokenizer=tokenizer) - - absolute_differences = [] assert len(layer_stack_cuda) == len(layer_stack_cpu) @@ -280,16 +278,21 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): abs_diff = torch.abs(tensor_cpu_out - tensor_cuda_out).flatten().tolist() logger.debug("abs_diff calculated") cos = nn.CosineSimilarity(dim=1) - cos_sim = cos(tensor_cpu_out, tensor_cuda_out) + cos_sim = cos(cpu_output.unsqueeze(0), cuda_output.unsqueeze(0)) logger.debug(cos_sim) prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, 0, 'float16') layer_name = str(layer).replace('[','').replace(']', '') - if not os.path.exists(os.path.join(output_dir, f"{prefix}--{layer_name}.abs_diff.csv")): - logger.debug("saving files") - write_csv(abs_diff, os.path.join(output_dir, f"{prefix}--{layer_name}.abs_diff.csv"), "abs_diff") - write_csv(cos_sim, os.path.join(output_dir, f"{prefix}--{layer_name}.cos_sim.csv"), "cos_sim") + abs_diff_path = os.path.join(output_dir, f"{prefix}--{layer_name}.abs_diff.csv") + cos_sim_path = os.path.join(output_dir, f"{prefix}--{layer_name}.cos_sim.csv") + + if not os.path.exists(abs_diff_path): + logger.debug("saving abs_diff files") + write_csv(abs_diff, abs_diff_path, "abs_diff") + if not os.path.exists(cos_sim_path): + logger.debug("saving cos_sim files") + write_csv(cos_sim, cos_sim_path, "cos_sim") logger.info(f"Completed {model_path} layers' metrics generation") From 02a01ce36599550a2f505ead0c9c700df72bc7b5 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 2 Jul 2025 11:28:08 -0300 Subject: [PATCH 17/60] Fix same device for cosine similarity Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 14d8ef1..15aab7b 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -275,10 +275,10 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): else: tensor_cpu_out = cpu_output.to('cuda') logger.debug("tensor converted... get torch abs diff") - abs_diff = torch.abs(tensor_cpu_out - tensor_cuda_out).flatten().tolist() + abs_diff = torch.abs(tensor_cpu_out - tensor_cpu_out).flatten().tolist() logger.debug("abs_diff calculated") cos = nn.CosineSimilarity(dim=1) - cos_sim = cos(cpu_output.unsqueeze(0), cuda_output.unsqueeze(0)) + cos_sim = cos(tensor_cpu_out.unsqueeze(0), tensor_cpu_out.unsqueeze(0)) logger.debug(cos_sim) prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, 0, 'float16') From c7d5a40a85b16d75b9fc94855de116cb8d195382 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 2 Jul 2025 11:45:05 -0300 Subject: [PATCH 18/60] Convert cos sim to list Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 15aab7b..3b69941 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -274,12 +274,11 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): tensor_cpu_out = convert_tensor(cpu_output) else: tensor_cpu_out = cpu_output.to('cuda') - logger.debug("tensor converted... get torch abs diff") abs_diff = torch.abs(tensor_cpu_out - tensor_cpu_out).flatten().tolist() logger.debug("abs_diff calculated") cos = nn.CosineSimilarity(dim=1) - cos_sim = cos(tensor_cpu_out.unsqueeze(0), tensor_cpu_out.unsqueeze(0)) - logger.debug(cos_sim) + cos_sim = cos(tensor_cpu_out.unsqueeze(0), tensor_cpu_out.unsqueeze(0)).flatten().tolist() + logger.debug("cos_sim calculated") prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, 0, 'float16') layer_name = str(layer).replace('[','').replace(']', '') From 3a963971fca58e3eef58340e02f3727aa6e3c3bd Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 2 Jul 2025 12:09:01 -0300 Subject: [PATCH 19/60] Test euclidean dist Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 3b69941..3d146df 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -274,11 +274,12 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): tensor_cpu_out = convert_tensor(cpu_output) else: tensor_cpu_out = cpu_output.to('cuda') - abs_diff = torch.abs(tensor_cpu_out - tensor_cpu_out).flatten().tolist() - logger.debug("abs_diff calculated") - cos = nn.CosineSimilarity(dim=1) - cos_sim = cos(tensor_cpu_out.unsqueeze(0), tensor_cpu_out.unsqueeze(0)).flatten().tolist() - logger.debug("cos_sim calculated") + abs_diff = torch.abs(tensor_cpu_out - tensor_cuda_out).flatten().tolist() + cos = nn.CosineSimilarity(dim=0) + cos_sim = cos(tensor_cpu_out.unsqueeze(0), tensor_cuda_out.unsqueeze(0)).flatten().tolist() + + distance = torch.sqrt(torch.sum((tensor_cpu_out.flatten() - tensor_cuda_out.flatten())**2)) + logger.debug(f"Euclidean Distance: {distance}") prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, 0, 'float16') layer_name = str(layer).replace('[','').replace(']', '') From d68c52f872930c09c5d21a79e1fc808b1b927379 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 2 Jul 2025 14:52:14 -0300 Subject: [PATCH 20/60] Adds sample json output to layer th Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 3 - tests/resources/get_thresholds.py | 15 +- tests/resources/sample_layer_th.json | 2917 ++++++++++++++++++++++++++ 3 files changed, 2926 insertions(+), 9 deletions(-) create mode 100644 tests/resources/sample_layer_th.json diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 3d146df..370fc58 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -278,9 +278,6 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): cos = nn.CosineSimilarity(dim=0) cos_sim = cos(tensor_cpu_out.unsqueeze(0), tensor_cuda_out.unsqueeze(0)).flatten().tolist() - distance = torch.sqrt(torch.sum((tensor_cpu_out.flatten() - tensor_cuda_out.flatten())**2)) - logger.debug(f"Euclidean Distance: {distance}") - prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, 0, 'float16') layer_name = str(layer).replace('[','').replace(']', '') diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 2788f65..1637844 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -44,10 +44,11 @@ layer_mode = args.file_base if args.file_base else False for model in models: + result_dict = {"model_id": model} for metric in metrics: path = os.path.join(file_base, f"{model}*{metric}*.csv") metric_files = glob.glob(path) - + result_dict[metric] = [] metric_list = [] if not layer_mode: @@ -61,24 +62,26 @@ print(model, metric, np.percentile(metric_list, 99.0)) else: layers = [] - result_dict = {"model_id": model} - result_dict[metric] = [] for metric_file in metric_files: layer_dict = {} layer_name = metric_file.split("--")[-1].replace(".{}".format(metric), "") layer_name = layer_name.replace(".csv","") + metric_layer_list = [] with open(metric_file, "r") as file: next(file) for line in file: - metric_list.append(float(line)) - layer_dict[layer_name] = metric_list + metric_layer_list.append(float(line)) + layer_dict[layer_name] = metric_layer_list layers.append(layer_dict) print(f"found {len(layers)} layers metric files") for l in layers: for key in l.keys(): tmp = {} - metric_val = np.average(l[key]) + if "abs_diff" in metric: + metric_val = np.linalg.norm(l[key]) + else: + metric_val = np.mean(l[key]) print(f"Layer {key} avg {metric} = {metric_val}") tmp[key] = metric_val result_dict[metric].append(tmp) diff --git a/tests/resources/sample_layer_th.json b/tests/resources/sample_layer_th.json new file mode 100644 index 0000000..b5eb184 --- /dev/null +++ b/tests/resources/sample_layer_th.json @@ -0,0 +1,2917 @@ +{ + "model_id": "ibm-granite--granite-3.2-8b-instruct", + "abs_diff": [ + { + "model.base_model.embedding": 1.7258892434335918e-07 + }, + { + "model.base_model.layers0.ln": 0.4083323414747196 + }, + { + "model.base_model.layers0.attn.in_proj.query": 0.7099368339133884 + }, + { + "model.base_model.layers0.attn.in_proj.key": 0.40915828503373886 + }, + { + "model.base_model.layers0.attn.in_proj.value": 0.12381335209555287 + }, + { + "model.base_model.layers0.attn.in_proj": 0.0 + }, + { + "model.base_model.layers0.attn.dense": 0.20848351243216826 + }, + { + "model.base_model.layers0.ff_ln": 0.341461752477386 + }, + { + "model.base_model.layers0.ff_sub_layer.wg": 0.567641596277766 + }, + { + "model.base_model.layers0.ff_sub_layer.a": 0.22434598762517147 + }, + { + "model.base_model.layers0.ff_sub_layer.w1": 0.6067234563084546 + }, + { + "model.base_model.layers0.ff_sub_layer.w2": 0.26350909056410365 + }, + { + "model.base_model.layers0.ff_sub_layer": 0.26350909056410365 + }, + { + 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os.environ.get('LOG_LEVEL', 'INFO').upper() +logging.basicConfig(level=LOG_LEVEL, format="%(asctime)s %(message)s") + parser = argparse.ArgumentParser( description="Script to get thresholds metrics" ) @@ -58,8 +65,8 @@ next(file) for line in file: metric_list.append(float(line)) - print(f"found {len(metric_files)} metric files") - print(model, metric, np.percentile(metric_list, 99.0)) + logger.info(f"found {len(metric_files)} metric files") + logger.info(model, metric, np.percentile(metric_list, 99.0)) else: layers = [] for metric_file in metric_files: @@ -73,7 +80,7 @@ metric_layer_list.append(float(line)) layer_dict[layer_name] = metric_layer_list layers.append(layer_dict) - print(f"found {len(layers)} layers metric files") + logger.info(f"found {len(layers)} layers metric files") for l in layers: for key in l.keys(): @@ -82,9 +89,9 @@ metric_val = np.linalg.norm(l[key]) else: metric_val = np.mean(l[key]) - print(f"Layer {key} avg {metric} = {metric_val}") - tmp[key] = metric_val + tmp[key] = metric_val if not math.isnan(metric_val) else 0.0 result_dict[metric].append(tmp) + logger.info(f"Layer {key} avg {metric} = {metric_val}") f_result_path = os.path.join(file_base, f"{model}-thresholds.json") with open(f_result_path, 'w') as fp: From dee632ef31da13792491cb95f2ba4ac8de47afd9 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 3 Jul 2025 09:59:26 -0300 Subject: [PATCH 22/60] Model forward mode Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 66 +++++++++++++++++++----------- 1 file changed, 41 insertions(+), 25 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 370fc58..c6b8232 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -1,6 +1,7 @@ import os import time import logging +import argparse import itertools import torch @@ -22,6 +23,21 @@ LOG_LEVEL = os.environ.get('LOG_LEVEL', 'INFO').upper() logging.basicConfig(level=LOG_LEVEL, format="%(asctime)s %(message)s") +parser = argparse.ArgumentParser( + description="Script to generate the model's metrics by layer" +) + +parser.add_argument( + "--mode", + choices=["generate", "model-forward"], + default="generate", + required=True, + help="Sets the output generation mode." +) + +args = parser.parse_args() +mode = args.mode + ORIGINAL_HF_HOME = os.environ.get("HF_HOME", None) SHARE_GPT_DATASET_PATH = os.environ.get( @@ -80,10 +96,8 @@ def __prepare_inputs(batch_size, seq_length, tokenizer, seed=0): input_ids, padding_kwargs = pad_input_ids(prompt_list, min_pad_length=seq_length) return input_ids, padding_kwargs -def __infer_layer(warmup, model, max_len, device, - max_new_tokens, batch_size, tokenizer): +def __infer_layer(model, max_len, device, max_new_tokens, batch_size, tokenizer): - do_sample = False use_cache = True @@ -99,27 +113,30 @@ def __infer_layer(warmup, model, max_len, device, # without ntk scaling, extending the seq length too far gives bogus results. max_seq_len = model.config.max_expected_seq_len - with torch.no_grad(): - result = generate( - model, + if "generate" in mode: + with torch.no_grad(): + result = generate( + model, + ids, + max_new_tokens=max_new_tokens, + use_cache=use_cache, + do_sample=do_sample, + max_seq_len=max_seq_len, + timing="e2e", + eos_token_id=None, + contiguous_cache=True, + extra_kwargs={}, + ) + result, timings = result + logger.info(f"Generation completed: Result len is {len(result)}") + if len(result.shape) == 1: + result = result.unsqueeze(0) + else: + result = model.forward( ids, - max_new_tokens=max_new_tokens, - use_cache=use_cache, - do_sample=do_sample, - max_seq_len=max_seq_len, - timing="e2e", - eos_token_id=None, - contiguous_cache=True, - extra_kwargs={}, - ) - result, timings = result - logger.info(f"E2E timing information: {timings[0]:.3f}s") - if len(result.shape) == 1: - result = result.unsqueeze(0) - - if not warmup: - for i in range(result.shape[0]): - logger.debug(result[i]) + use_cache=use_cache + ) + logger.info(f"Model forward completed: Result len is {len(result)}") def __register_call_layers(model, batch_size, device, seq_length, max_new_tokens, tokenizer): layer_stack = [] @@ -183,8 +200,7 @@ def post_hook_fn(module, input, output): hooks.append(layer.register_forward_hook(post_hook_fn)) - __infer_layer(warmup=True, - model= model, max_len=seq_length, + __infer_layer(model= model, max_len=seq_length, device=device, max_new_tokens=max_new_tokens, batch_size=batch_size, tokenizer=tokenizer) From 4576a3c9ec3f6459c8ffa7839f07f9254d508471 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 3 Jul 2025 11:29:15 -0300 Subject: [PATCH 23/60] Adds docs Signed-off-by: Flavia Beo --- tests/LAYERS.md | 110 ++++++ tests/MODEL.md | 313 +++++++++++++++++ tests/README.md | 315 +----------------- .../metrics_fms_deepview_integration.full.png | Bin 0 -> 1600908 bytes .../metrics_fms_deepview_integration.zoom.png | Bin 0 -> 228034 bytes .../assets/metrics_generation_layers.png | Bin 0 -> 184595 bytes 6 files changed, 427 insertions(+), 311 deletions(-) create mode 100644 tests/LAYERS.md create mode 100644 tests/MODEL.md create mode 100644 tests/resources/assets/metrics_fms_deepview_integration.full.png create mode 100644 tests/resources/assets/metrics_fms_deepview_integration.zoom.png create mode 100644 tests/resources/assets/metrics_generation_layers.png diff --git a/tests/LAYERS.md b/tests/LAYERS.md new file mode 100644 index 0000000..6597c83 --- /dev/null +++ b/tests/LAYERS.md @@ -0,0 +1,110 @@ +# Layer Metrics Generation + +Generate metrics by layers to be used in tests and model enablement debugging. + +1. [Generate metrics by layer in GPU](./LAYERS.md#1-generate-metrics-by-layer) +2. [Get Thresholds](./LAYERS.md#2-get-thresholds) +3. [Apply metrics where needed](./LAYERS.md#3-apply-the-thresholds-where-needed) + +The steps as part of the diagram bellow: +![generate flow](./resources/assets/metrics_fms_deepview_integration.zoom.png) +To see the full integration with other debugging tools, check [item 3](./LAYERS.md#3-apply-the-thresholds-where-needed). + +## 1. Generate Metrics by Layer + +The idea is to run, the prompts through the model with the pre- and post-hooks added, and then get the metrics for the outputs intercepted by each layer, as in this diagram. Then we can have a baseline with CPU/GPU for a failure threshold in AIU tests. Same idea as the [test_decoders.py](https://github.com/foundation-model-stack/aiu-fms-testing-utils/blob/main/tests/models/test_decoders.py), but for each layer. This way we can measure the discrepancies for the outputs and use the thresholds for detailed debugging problems in AIU. + +![metrics generation by layer](./resources/assets/metrics_generation_layers.png) + +The script [generate_layers_metrics.py](../scripts/generate_layers_metrics.py) requires the following environment variables: + +```bash +export MODEL_PATHS=ibm-granite/granite-3.2-8b-instruct +export BATCH_SIZES=1 +export SEQ_LENGTHS=64 +export MAX_NEW_TOKENS=128 +export OUTPUT_PATH=/tmp/output/granite +``` + +These variables suport single and array values. + +The argument required for this script is the `--mode`, which is the generation mode desired for the output; The choices can be `generate` or `model-forward`. +- `generate` uses fms [generate](../scripts/generate_layers_metrics.py#L118) from ; It’s a high-level API that wraps many operations: forward pass, KV cache logic, sampling or greeting decoding, post-processing. +```python +result = generate( + model, + ids, + max_new_tokens=max_new_tokens, + use_cache=use_cache, + do_sample=do_sample, + max_seq_len=max_seq_len, + timing="e2e", + eos_token_id=None, + contiguous_cache=True, + extra_kwargs={}, +) +``` +- `model-forward` will call [model.forward](../scripts/generate_layers_metrics.py#L135); Avoids introducing noise from sampling, past key caching, etc. +```python +result = model.forward( + ids, + use_cache=use_cache + ) +``` + +### How to run + +Once all is set-up, we can generate the csv metrics: + +```bash +cd aiu-fms-testing-utils/tests/resources + +mkdir /tmp/output + +python3 aiu-fms-testing-utils/scripts/generate_layers_metrics.py --mode generate +``` +The files should get created at `/tmp/output` dir: +```bash +ibm-granite--granite-3.2-8b-instruct_max-new-tokens-128_batch-size-1_seq-length-0_dtype-float16--model.base_model.layers7.ln.abs_diff.csv +ibm-granite--granite-3.2-8b-instruct_max-new-tokens-128_batch-size-1_seq-length-0_dtype-float16--model.base_model.layers7.ln.cos_sim.csv +ibm-granite--granite-3.2-8b-instruct_max-new-tokens-128_batch-size-1_seq-length-0_dtype-float16--model.base_model.layers8.attn.dense.abs_diff.csv +ibm-granite--granite-3.2-8b-instruct_max-new-tokens-128_batch-size-1_seq-length-0_dtype-float16--model.base_model.layers8.attn.dense.cos_sim.csv +``` + +## 2. Get Thresholds + +To get the second step of the flow and get the thresholds by layer, run: +```bash +cd /aiu-fms-testing-utils/tests/resources + +python3 get_thresholds.py --models ibm-granite/granite-3.2-8b-instruct --metrics abs_diff cos_sim --file_base /tmp/output --layer_io +``` +It should print the metric of each layer: +```bash +Layer model.base_model.layers25.attn.in_proj.query avg abs_diff = 2.079996666484281 +Layer model.base_model.layers25.attn.in_proj.key avg abs_diff = 1.2256532914682756 +Layer model.base_model.layers25.attn.in_proj.value avg abs_diff = 0.8446561344670284 +Layer model.base_model.layers25.attn.in_proj avg abs_diff = 0.0 +Layer model.base_model.layers25.attn.dense avg abs_diff = 0.23142293885894077 +Layer model.base_model.layers25.ff_ln avg abs_diff = 0.9550253005897409 +Layer model.base_model.layers25.ff_sub_layer.wg avg abs_diff = 1.2256491705546648 +Layer model.base_model.layers25.ff_sub_layer.a avg abs_diff = 0.5235781749861929 +Layer model.base_model.layers25.ff_sub_layer.w1 avg abs_diff = 1.2707070667436549 +Layer model.base_model.layers25.ff_sub_layer.w2 avg abs_diff = 0.5201997339672954 +Layer model.base_model.layers25.ff_sub_layer avg abs_diff = 0.5201997339672954 +Layer model.base_model.layers26.ln avg abs_diff = 0.04852477119171675 +[...] +Layer model.base_model.layers39.attn.in_proj.query avg cos_sim = 0.999176025390625 +Layer model.base_model.layers39.attn.in_proj.key avg cos_sim = 0.9991455078125 +Layer model.base_model.layers39.attn.in_proj.value avg cos_sim = 0.9986572265625 +Layer model.base_model.layers39.attn.in_proj avg cos_sim = 0.0 +Layer model.base_model.layers39.attn.dense avg cos_sim = 0.9987258911132812 +``` +Also, a JSON file is saved to the same output dir. A sample file can be found at: [sample_layer_th.json](https://github.com/flaviabeo/aiu-fms-testing-utils/blob/generate_metrics_layers/tests/resources/sample_layer_th.json) + +## 3. Apply the thresholds where needed + +In case of AIU debugging tools, the thresholds will be applied to compare AIU outputs with CPU, and then assert if the differences are within the threhsolds generated. Bellow, is an archtechture of the full integration: +![full integration](./resources/assets/metrics_fms_deepview_integration.full.png) + +The box named `deepview layer debug` has the diagram of how the model layers outputs are generated to be compared against the CPU results. This is important so that the debug tools can catch operations and layers that have issues in their enablement for AIU hardware. \ No newline at end of file diff --git a/tests/MODEL.md b/tests/MODEL.md new file mode 100644 index 0000000..0f7ba61 --- /dev/null +++ b/tests/MODEL.md @@ -0,0 +1,313 @@ +# Model Tests +How to run the pytest test suites at [aiu-fms-testing-utils](https://github.com/aiu-fms-testing-utils/tree/main/tests/models). + +1. [Generate metrics in GPU](MODEL.md#1-run-first-on-gpu) +2. [Get Thresholds](MODEL.md#2-get-thresholds) +3. [Apply thresholds into test_decoders](MODEL.md#3-apply-thresholds-in-aiu-test_decoders) +4. [Run test_model_expectations](MODEL.md#4-run-test_model_expectations) + +![diagram](./resources/assets/test_flow_diagram.png) + +## The test scripts + +- **test_decoders** - this will test the decoder models (text-generation) with certain shapes. Most of this is configurable (model, batch_size, prompt_length, max_new_tokens, metrics_thresholds, failure_rate_thresholds, mini models, etc.) +Example: +```bash +# Note: you might need an hf_token if the model requires it (this will download) +export FMS_TEST_SHAPES_COMMON_BATCH_SIZES=1 +export FMS_TEST_SHAPES_COMMON_SEQ_LENGTHS=128 +export FMS_TEST_SHAPES_COMMON_MODEL_PATHS=/local-path/granite-20b-code-cobol-v1/ +export FMS_TEST_SHAPES_USE_MICRO_MODELS=0 +pytest tests/models/test_decoders.py +``` +The above will test shapes batch_size 1, with sequence length 128 of granite-20b-code-cobol-v1. We can set `FMS_TEST_SHAPES_USE_MICRO_MODELS=0` for not using micro models. Or set it to `FMS_TEST_SHAPES_USE_MICRO_MODELS=1` and add the micro models version to the model paths. + +- **test_model_expectations** - this test will capture a snapshot in time of what a randomly initialized model would produce on the AIU. To add a model to this, you simply add it to either the models list or tuple_output_models list which will generate 2 expectation tests. The first time you run this test, you run it with --capture_expectation which will create a resource file with the expected output. The next time you run it, you run without the --capture_expectation and all should pass. + +### Thresholds for the tests baselines for `test_decoders` + +The `test_decoders.py` file contains tests written for models that have **decoder** architecture. For each model to be tested, specific metrics baselines need to be created by following the next steps in this documentation. Four different metrics are generated with top k per token as base lines for these tests; Cross entropy loss per token, probability mean, probability standard deviation and absolute diff mean. + +- **cross_entropy**: Cross entropy is a measure from information theory that quantifies the difference between two probability distributions. Cross entropy serves as a measure of the differences when comparing expected generated tokens and the actual output of the model. Quantifying the distance between the ground-truth distribution and the predicted distribution. +A lower cross entropy indicates a closer match in expected versus generated. +- **prob_mean**: Probability Mean typically refers to the average probability assigned by the model to a sequence of words or tokens. It's a measure of how well the model understands and predicts language, with lower mean probabilities often indicating a poorer model that struggles to generate coherent or plausible text. +- **prob_std**: Probability standard deviation assesses how spread out or consistent the model's predictions are when it assigns probabilities to different possible outcomes. A high standard deviation indicates wide variation in the model's certainty, while a low standard deviation suggests more consistent and confident prediction +- **diff_mean**: The difference of the average or central tendency of a set of data points, often used to measure the model's performance. It can also refer to the intended purpose or interpretation of a text or sentence produced by the model. + +They are calculated in lines [228 - 231 at generate_metrics.py](../scripts/generate_metrics.py#L253) script. +```python +cross_entropy = lambda r, t: torch.nn.CrossEntropyLoss()(r, t.softmax(dim=1).to(dtype=torch.float32)) +prob_mean = lambda r, t: torch.mean((r.softmax(dim=1).to(dtype=torch.float32) / t.softmax(dim=1).to(dtype=torch.float32)) - 1.0) +prob_std = lambda r, t: torch.std(r.softmax(dim=1).to(dtype=torch.float32) / t.softmax(dim=1).to(dtype=torch.float32)) +diff_mean = lambda r, t: torch.mean(torch.abs(r.softmax(dim=1).to(dtype=torch.float32) - t.softmax(dim=1).to(dtype=torch.float32))) +``` +More at [pytorch.org](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html), [Yiren,Wang](https://courses.grainger.illinois.edu/ece598pv/fa2017/Lecture13_LM_YirenWang.pdf), [Li, Wang, Shang Et al.](https://arxiv.org/abs/2412.12177#:~:text=%5B2412.12177%5D%20Model%2Ddiff:,%3E%20cs%20%3E%20arXiv:2412.12177) and [Wu,Hilton](https://arxiv.org/html/2410.13211v1). +
+ +This metrics will be set at the [fail thresholds](./models/test_decoders.py#L146), so **cross_entropy** and **diff_mean** can be used to compare between the GPU generated text output by the same model in AIU. + +## 1. Run first on GPU + +Set shapes: +```bash +export MODEL_PATH=/model-path/ +export MAX_NEW_TOKENS=128 +export BATCH_SIZES=1 +export SEQ_LENS=64 +export DEFAULT_TYPES="fp16" +export DS_PATH=/resources/sharegpt/share_gpt.json +``` + +Then run the command for the metrics script: +```bash +python generate_metrics.py --architecture=hf_pretrained --model_path=$MODEL_PATH --tokenizer=$MODEL_PATH --unfuse_weights --output_dir=/tmp/aiu-fms-testing-utils/output/ --compile_dynamic --max_new_tokens=$MAX_NEW_TOKENS --min_pad_length=$SEQ_LENS --batch_size=$BATCH_SIZES --default_dtype=$DEFAULT_TYPES --sharegpt_path=$DS_PATH --num_test_tokens_per_sequence=1024 +``` + +This will generate csv files with the results of the metrics calculation. Typically, this is run with batch size 1, 8 and sequency length 64, 2048 (4 runs in total). Then, we can run [get_thresholds.py](./resources/get_thresholds.py) to summarize the results and get the single values for each metric as the following. +
+At the output path, you will see the out and csv files generated as the sample in the following lines: +```bash +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.ce.csv +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.0.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.1.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.2.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.3.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.4.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.5.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.6.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.7.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.0.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.1.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.2.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.3.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.4.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.5.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.6.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.7.out +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.diff_mean.csv +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.prob_mean.csv +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.prob_std.csv +``` +## 2. Get Thresholds +Get the thresholds by running the [get_thresholds.py](./resources/get_thresholds.py): +```bash +python3 get_thresholds.py --models /tmp/aiu-fms-testing-utils/models/model-name-version-v1 --metrics diff_mean ce --file_base /tmp/aiu-fms-testing-utils/output +``` +After running these scripts in namespace with 1 GPU, these were the thresholds generated: + +```bash +python3 get_thresholds.py --models /tmp/aiu-fms-testing-utils/models/Mistral-7B-Instruct-v0.3 --metrics diff_mean ce --file_base /tmp/aiu-fms-testing-utils/output +found 7 metric files +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3 diff_mean 0.0007839603102183846 +found 7 metric files +--tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3 ce 2.8364005851745624 +``` + +These can now be used for the model testing scripts at AIU. + +## 3. Apply thresholds in AIU `test_decoders` + +These are the variables set at the deployment: + +| Name | Value +| ------------- | ---------------- +| FMS_TEST_SHAPES_COMMON_MODEL_PATHS | mistralai/Mistral-7B-Instruct-v0.3 +| FMS_TEST_SHAPES_FORCE_VALIDATION_LEVEL_1 | 1 +| FMS_TEST_SHAPES_COMMON_BATCH_SIZES | 1 +| FMS_TEST_SHAPES_COMMON_SEQ_LENGTHS | 64 +| FMS_TEST_SHAPES_COMMON_MAX_NEW_TOKENS | 16 +| FMS_TEST_SHAPES_USE_MICRO_MODELS | 0 +| FMS_TEST_SHAPES_METRICS_THRESHOLD | 2.8364005851745624,0.0007839603102183846 + + +> Set `FMS_TEST_SHAPES_METRICS_THRESHOLD` in case there is no need to add the model to the default ones. No code changes needed, just this environment variable set with the metrics values. Set `FMS_TEST_SHAPES_VALIDATION_INFO_DIR` to speed up the tests considerably when testing larger models by using the output logits saved from generating the metrics. Set `FMS_TEST_SHAPES_FAILURE_THRESHOLD` if you would like to relax the threshold - default is `0.01`. + +Add the new numbers at the end of the [dictionary](./models/test_decoders.py#L116): +```python +# thresholds are chosen based on 1024 tokens per sequence +# 1% error threshold rate between cpu fp32 and cuda fp16 +# if a models failure thresholds do not exist in this dict, default to the default_metrics_threshold defined above +# threshold key is (model_id, is_tiny_model) +fail_thresholds = { + (LLAMA_3p1_8B_INSTRUCT, True): ( + 3.7392955756187423, + .001, # FIXME: compute + ), + (GRANITE_3p2_8B_INSTRUCT, True): ( + 2.996668996810913, + .001, # FIXME: compute + ), + (GRANITE_20B_CODE_INSTRUCT_8K, True): ( + 3.7392955756187423, # FIXME: compute -- setting to micro llama 3.1 8b instruct + .001, # FIXME: compute + ), + (LLAMA_3p1_70B_INSTRUCT, True): ( + 3.8235735702514626, + .001, # FIXME: compute + ), + (LLAMA_3p1_8B_INSTRUCT, False): ( + 2.6994638133048965, + 0.00047589250549208347, + ), + (GRANITE_3p2_8B_INSTRUCT, False): ( + 2.3919514417648315, + 0.0005767398688476533, + ), + (GRANITE_20B_CODE_INSTRUCT_8K, False): ( + 2.640706129074097, + 0.00034344267623964697, + ), + (LLAMA_3p1_70B_INSTRUCT, False): ( + 2.841279556751251, + 0.0044301633024588115, + ), +} +``` + +The command to run is: +```bash +pytest tests/models/test_decoders.py -vv +``` +Add the `-vv` for verbose output. + +### Test Results Samples + +Here is a result sample of the test outputs: + +```bash +Starting to run pytest tests/models/test_decoders.py +[ 0/ 1]: Sentient AIU: Enabled +============================= test session starts ============================== +platform linux -- Python 3.11.9, pytest-8.3.5, pluggy-1.5.0 +rootdir: /tmp/aiu-fms-testing-utils +plugins: durations-1.4.0, env-1.1.5 +collected 1 item + +tests/models/test_decoders.py . [100%] + +=============================== warnings summary =============================== +../foundation-model-stack/fms/triton/pytorch_ops.py:103 + /tmp/foundation-model-stack/fms/triton/pytorch_ops.py:103: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch. + @torch.library.impl_abstract("moe::moe_mm") + +-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html +============================= fixture duration top ============================= +total name num avg min +0:00:00.000140 grand total 5 0:00:00.000014 0:00:00.000012 +============================ test call duration top ============================ +total name num avg min +0:02:18.965102 test_common_shapes 1 0:02:18.965102 0:02:18.965102 +0:02:18.965102 grand total 1 0:02:18.965102 0:02:18.965102 +=========================== test setup duration top ============================ +total name num avg min +0:00:00.000553 grand total 1 0:00:00.000553 0:00:00.000553 +========================== test teardown duration top ========================== +total name num avg min +0:00:00.000969 grand total 1 0:00:00.000969 0:00:00.000969 +=================== 1 passed, 1 warning in 140.35s (0:02:20) =================== +Finished running pytests +``` +In case the thresholds fails: +```bash +[ 0/ 1]: testing model=/mnt/aiu-models-en-shared/models/hf/Mistral-7B-Instruct-v0.3, batch_size=1, seq_length=64, max_new_tokens=16, micro_model=False +[ 0/ 1]: AIU warmup +Using AIU_TOPO_FILE=/etc/aiu/topo.json +[ 0/ 1]: PT compile complete, took 211.912s +[ 0/ 1]: cpu validation info extracted for validation level 0 and validation level 1 (iter=0) +[ 0/ 1]: aiu validation info extracted for validation level 0 +[ 0/ 1]: failed validation level 0, testing validation level 1 +[ 0/ 1]: aiu validation info extracted for validation level 1 - iter=0 +[ 0/ 1]: cpu validation info extracted for validation level 1 - iter=1 +[ 0/ 1]: aiu validation info extracted for validation level 1 - iter=1 +[ 0/ 1]: cpu validation info extracted for validation level 1 - iter=2 +[...] (iteractions removed for better readability) +[ 0/ 1]: aiu validation info extracted for validation level 1 - iter=60 +[ 0/ 1]: cpu validation info extracted for validation level 1 - iter=61 +[ 0/ 1]: aiu validation info extracted for validation level 1 - iter=61 +[ 0/ 1]: cpu validation info extracted for validation level 1 - iter=62 +[ 0/ 1]: aiu validation info extracted for validation level 1 - iter=62 +[ 0/ 1]: cpu validation info extracted for validation level 1 - iter=63 +[ 0/ 1]: aiu validation info extracted for validation level 1 - iter=63 +[ 0/ 1]: mean diff failure rate: 0.7638888888888888 +[ 0/ 1]: cross entropy loss failure rate: 0.000992063492063492 +===================================================================================== fixture duration top ===================================================================================== +total name num avg min +0:00:00.000130 grand total 5 0:00:00.000012 0:00:00.000009 +==================================================================================== test call duration top ==================================================================================== +total name num avg min +0:16:31.480337 test_common_shapes 1 0:16:31.480337 0:16:31.480337 +0:16:31.480337 grand total 1 0:16:31.480337 0:16:31.480337 +=================================================================================== test setup duration top ==================================================================================== +total name num avg min +0:00:00.000555 grand total 1 0:00:00.000555 0:00:00.000555 +================================================================================== test teardown duration top ================================================================================== +total name num avg min +0:00:00.001416 grand total 1 0:00:00.001416 0:00:00.001416 +=================================================================================== short test summary info ==================================================================================== +FAILED tests/models/test_decoders.py::test_common_shapes[/mnt/aiu-models-en-shared/models/hf/Mistral-7B-Instruct-v0.3-1-64-16] - AssertionError: failure rate for mean diff was too high: 0.7638888888888888 +assert 0.7638888888888888 < 0.01 +``` +## 4. Run `test_model_expectations` + +- First add the desired model to the [decoder_models](./models/test_model_expectations.py#L55) variable. +- If the models tested are too big, it is a valid option to add the micro model version for this specific test. +- 4.1 Run `pytest tests/models/test_model_expectations.py::TestAIUDecoderModels --capture_expectation` to save the model weights. +After that you will get an output like this: +```bash +FAILED tests/models/test_model_expectations.py::TestAIUDecoderModels::test_model_output[/tmp/models/mistralai/Mistral-7B-Instruct-v0.3-True] - Failed: Signature file has been saved, please re-run the tests without --capture_expectation +FAILED tests/models/test_model_expectations.py::TestAIUDecoderModels::test_model_weight_keys[/tmp/models/mistralai/Mistral-7B-Instruct-v0.3-True] - Failed: Weights Key file has been saved, please re-run the tests without --capture_expectation +``` +This will tell that the weights and signature have been saved, so you can run the complete suite again to get the tests results. +- 4.2 Then running the complete suite: + +```bash +[1000780000@e2e-vllm-dt2-646f66647b-68dh6 aiu-fms-testing-utils]$ pytest tests/models/test_model_expectations.py::TestAIUDecoderModels -vv +[ 0/ 1]: Sentient AIU: Enabled +===================================================================================== test session starts ====================================================================================== +platform linux -- Python 3.12.5, pytest-8.3.5, pluggy-1.5.0 -- /usr/bin/python3.12 +cachedir: .pytest_cache +rootdir: /tmp/aiu-fms-testing-utils +plugins: durations-1.5.2, env-1.1.5 +collected 3 items + +tests/models/test_model_expectations.py::TestAIUDecoderModels::test_model_output[/tmp/models/mistralai/Mistral-7B-Instruct-v0.3-False] <- ../foundation-model-stack/fms/testing/_internal/model_test_suite.py PASSED [ 33%] +tests/models/test_model_expectations.py::TestAIUDecoderModels::test_model_weight_keys[/tmp/models/mistralai/Mistral-7B-Instruct-v0.3-False] <- ../foundation-model-stack/fms/testing/_internal/model_test_suite.py PASSED [ 66%] +tests/models/test_model_expectations.py::TestAIUDecoderModels::test_model_unfused[/tmp/models/mistralai/Mistral-7B-Instruct-v0.3] SKIPPED (All AIU models are already unfused) [100%] + +===================================================================================== fixture duration top ===================================================================================== +total name num avg min +0:00:02.201162 uninitialized_model 1 0:00:02.201162 0:00:02.201162 +0:00:00.051478 model 1 0:00:00.051478 0:00:00.051478 +0:00:02.252951 grand total 6 0:00:00.000135 0:00:00.000046 +==================================================================================== test call duration top ==================================================================================== +total name num avg min +0:03:05.951278 TestAIUDecoderModels::test_model_output 1 0:03:05.951278 0:03:05.951278 +0:03:05.954470 grand total 3 0:00:00.003095 0:00:00.000097 +=================================================================================== test setup duration top ==================================================================================== +total name num avg min +0:00:00.002004 grand total 3 0:00:00.000289 0:00:00.000102 +================================================================================== test teardown duration top ================================================================================== +total name num avg min +0:00:00.000363 grand total 3 0:00:00.000090 0:00:00.000077 +=========================================================================== 2 passed, 1 skipped in 189.01s (0:03:09) =========================================================================== + +``` + +In this case, the model tested was a decoder model with a single output, the `TestAIUDecoderModels` is the most important case. In the next section, check the applicability for the [TestAIUModelsTupleOutput](./MODEL.md#case-of-multiple-output---testaiumodelstupleoutput) cases. + +#### Case of multiple output - TestAIUModelsTupleOutput + +The case **TestAIUModelsTupleOutput** is applicable if the model being tested has output of more than one tensor. Like the model in the example default [tuple_output_models](./models/test_model_expectations.py#L76), is a RoBERTa model that can output in this different format. + +- Add the model also to [tuple_output_models](./models/test_model_expectations.py#L76). +- 4.1 Run `pytest tests/models/test_model_expectations.py::TestAIUModelsTupleOutput --capture_expectation` to save the model weights; + +```bash +tests/models/test_model_expectations.py::TestAIUModelsTupleOutput::test_model_output[/ibm-dmf/models/watsonx/shared/granite-20b-code-cobol-v1/20240603-False] <- ../foundation-model-stack/fms/testing/_internal/model_test_suite.py PASSED [ 66%] +tests/models/test_model_expectations.py::TestAIUModelsTupleOutput::test_model_weight_keys[/ibm-dmf/models/watsonx/shared/granite-20b-code-cobol-v1/20240603-False] <- ../foundation-model-stack/fms/testing/_internal/model_test_suite.py PASSED [ 83%] +tests/models/test_model_expectations.py::TestAIUModelsTupleOutput::test_model_unfused[/ibm-dmf/models/watsonx/shared/granite-20b-code-cobol-v1/20240603] SKIPPED [100%] +``` + +> When adding new models expectations, please include in the PR with capture expectation tests added, the date of the image used to generate the file. + +Check this example of a PR for adding a new model expectations' files and results [here](https://github.com/foundation-model-stack/aiu-fms-testing-utils/pull/48). diff --git a/tests/README.md b/tests/README.md index 836181c..07a9e53 100644 --- a/tests/README.md +++ b/tests/README.md @@ -1,313 +1,6 @@ -# Model Tests -How to run the pytest test suites at [aiu-fms-testing-utils](https://github.com/aiu-fms-testing-utils/tree/main/tests/models). +# Tests and Metrics generation -1. [Generate metrics in GPU](README.md#1-run-first-on-gpu) -2. [Get Thresholds](README.md#2-get-thresholds) -3. [Apply thresholds into test_decoders](README.md#3-apply-thresholds-in-aiu-test_decoders) -4. [Run test_model_expectations](README.md#4-run-test_model_expectations) +In these docs you will find: -![diagram](./resources/assets/test_flow_diagram.png) - -## The test scripts - -- **test_decoders** - this will test the decoder models (text-generation) with certain shapes. Most of this is configurable (model, batch_size, prompt_length, max_new_tokens, metrics_thresholds, failure_rate_thresholds, mini models, etc.) -Example: -```bash -# Note: you might need an hf_token if the model requires it (this will download) -export FMS_TEST_SHAPES_COMMON_BATCH_SIZES=1 -export FMS_TEST_SHAPES_COMMON_SEQ_LENGTHS=128 -export FMS_TEST_SHAPES_COMMON_MODEL_PATHS=/local-path/granite-20b-code-cobol-v1/ -export FMS_TEST_SHAPES_USE_MICRO_MODELS=0 -pytest tests/models/test_decoders.py -``` -The above will test shapes batch_size 1, with sequence length 128 of granite-20b-code-cobol-v1. We can set `FMS_TEST_SHAPES_USE_MICRO_MODELS=0` for not using micro models. Or set it to `FMS_TEST_SHAPES_USE_MICRO_MODELS=1` and add the micro models version to the model paths. - -- **test_model_expectations** - this test will capture a snapshot in time of what a randomly initialized model would produce on the AIU. To add a model to this, you simply add it to either the models list or tuple_output_models list which will generate 2 expectation tests. The first time you run this test, you run it with --capture_expectation which will create a resource file with the expected output. The next time you run it, you run without the --capture_expectation and all should pass. - -### Thresholds for the tests baselines for `test_decoders` - -The `test_decoders.py` file contains tests written for models that have **decoder** architecture. For each model to be tested, specific metrics baselines need to be created by following the next steps in this documentation. Four different metrics are generated with top k per token as base lines for these tests; Cross entropy loss per token, probability mean, probability standard deviation and absolute diff mean. - -- **cross_entropy**: Cross entropy is a measure from information theory that quantifies the difference between two probability distributions. Cross entropy serves as a measure of the differences when comparing expected generated tokens and the actual output of the model. Quantifying the distance between the ground-truth distribution and the predicted distribution. -A lower cross entropy indicates a closer match in expected versus generated. -- **prob_mean**: Probability Mean typically refers to the average probability assigned by the model to a sequence of words or tokens. It's a measure of how well the model understands and predicts language, with lower mean probabilities often indicating a poorer model that struggles to generate coherent or plausible text. -- **prob_std**: Probability standard deviation assesses how spread out or consistent the model's predictions are when it assigns probabilities to different possible outcomes. A high standard deviation indicates wide variation in the model's certainty, while a low standard deviation suggests more consistent and confident prediction -- **diff_mean**: The difference of the average or central tendency of a set of data points, often used to measure the model's performance. It can also refer to the intended purpose or interpretation of a text or sentence produced by the model. - -They are calculated in lines [228 - 231 at generate_metrics.py](../scripts/generate_metrics.py#L253) script. -```python -cross_entropy = lambda r, t: torch.nn.CrossEntropyLoss()(r, t.softmax(dim=1).to(dtype=torch.float32)) -prob_mean = lambda r, t: torch.mean((r.softmax(dim=1).to(dtype=torch.float32) / t.softmax(dim=1).to(dtype=torch.float32)) - 1.0) -prob_std = lambda r, t: torch.std(r.softmax(dim=1).to(dtype=torch.float32) / t.softmax(dim=1).to(dtype=torch.float32)) -diff_mean = lambda r, t: torch.mean(torch.abs(r.softmax(dim=1).to(dtype=torch.float32) - t.softmax(dim=1).to(dtype=torch.float32))) -``` -More at [pytorch.org](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html), [Yiren,Wang](https://courses.grainger.illinois.edu/ece598pv/fa2017/Lecture13_LM_YirenWang.pdf), [Li, Wang, Shang Et al.](https://arxiv.org/abs/2412.12177#:~:text=%5B2412.12177%5D%20Model%2Ddiff:,%3E%20cs%20%3E%20arXiv:2412.12177) and [Wu,Hilton](https://arxiv.org/html/2410.13211v1). -
- -This metrics will be set at the [fail thresholds](./models/test_decoders.py#L146), so **cross_entropy** and **diff_mean** can be used to compare between the GPU generated text output by the same model in AIU. - -## 1. Run first on GPU - -Set shapes: -```bash -export MODEL_PATH=/model-path/ -export MAX_NEW_TOKENS=128 -export BATCH_SIZES=1 -export SEQ_LENS=64 -export DEFAULT_TYPES="fp16" -export DS_PATH=/resources/sharegpt/share_gpt.json -``` - -Then run the command for the metrics script: -```bash -python generate_metrics.py --architecture=hf_pretrained --model_path=$MODEL_PATH --tokenizer=$MODEL_PATH --unfuse_weights --output_dir=/tmp/aiu-fms-testing-utils/output/ --compile_dynamic --max_new_tokens=$MAX_NEW_TOKENS --min_pad_length=$SEQ_LENS --batch_size=$BATCH_SIZES --default_dtype=$DEFAULT_TYPES --sharegpt_path=$DS_PATH --num_test_tokens_per_sequence=1024 -``` - -This will generate csv files with the results of the metrics calculation. Typically, this is run with batch size 1, 8 and sequency length 64, 2048 (4 runs in total). Then, we can run [get_thresholds.py](./resources/get_thresholds.py) to summarize the results and get the single values for each metric as the following. -
-At the output path, you will see the out and csv files generated as the sample in the following lines: -```bash ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.ce.csv ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.0.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.1.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.2.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.3.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.4.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.5.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.6.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cpu_validation_info.7.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.0.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.1.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.2.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.3.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.4.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.5.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.6.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.cuda_validation_info.7.out ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.diff_mean.csv ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.prob_mean.csv ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3_max-new-tokens-128_batch-size-8_seq-length64_dtype-fp16.prob_std.csv -``` -## 2. Get Thresholds -Get the thresholds by running the [get_thresholds.py](./resources/get_thresholds.py): -```bash -python3 get_thresholds.py --models /tmp/aiu-fms-testing-utils/models/model-name-version-v1 --metrics diff_mean ce --file_base /tmp/aiu-fms-testing-utils/output -``` -After running these scripts in namespace with 1 GPU, these were the thresholds generated: - -```bash -python3 get_thresholds.py --models /tmp/aiu-fms-testing-utils/models/Mistral-7B-Instruct-v0.3 --metrics diff_mean ce --file_base /tmp/aiu-fms-testing-utils/output -found 7 metric files ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3 diff_mean 0.0007839603102183846 -found 7 metric files ---tmp--aiu-fms-testing-utils--models--Mistral-7B-Instruct-v0.3 ce 2.8364005851745624 -``` - -These can now be used for the model testing scripts at AIU. - -## 3. Apply thresholds in AIU `test_decoders` - -These are the variables set at the deployment: - -| Name | Value -| ------------- | ---------------- -| FMS_TEST_SHAPES_COMMON_MODEL_PATHS | mistralai/Mistral-7B-Instruct-v0.3 -| FMS_TEST_SHAPES_FORCE_VALIDATION_LEVEL_1 | 1 -| FMS_TEST_SHAPES_COMMON_BATCH_SIZES | 1 -| FMS_TEST_SHAPES_COMMON_SEQ_LENGTHS | 64 -| FMS_TEST_SHAPES_COMMON_MAX_NEW_TOKENS | 16 -| FMS_TEST_SHAPES_USE_MICRO_MODELS | 0 -| FMS_TEST_SHAPES_METRICS_THRESHOLD | 2.8364005851745624,0.0007839603102183846 - - -> Set `FMS_TEST_SHAPES_METRICS_THRESHOLD` in case there is no need to add the model to the default ones. No code changes needed, just this environment variable set with the metrics values. Set `FMS_TEST_SHAPES_VALIDATION_INFO_DIR` to speed up the tests considerably when testing larger models by using the output logits saved from generating the metrics. Set `FMS_TEST_SHAPES_FAILURE_THRESHOLD` if you would like to relax the threshold - default is `0.01`. - -Add the new numbers at the end of the [dictionary](./models/test_decoders.py#L116): -```python -# thresholds are chosen based on 1024 tokens per sequence -# 1% error threshold rate between cpu fp32 and cuda fp16 -# if a models failure thresholds do not exist in this dict, default to the default_metrics_threshold defined above -# threshold key is (model_id, is_tiny_model) -fail_thresholds = { - (LLAMA_3p1_8B_INSTRUCT, True): ( - 3.7392955756187423, - .001, # FIXME: compute - ), - (GRANITE_3p2_8B_INSTRUCT, True): ( - 2.996668996810913, - .001, # FIXME: compute - ), - (GRANITE_20B_CODE_INSTRUCT_8K, True): ( - 3.7392955756187423, # FIXME: compute -- setting to micro llama 3.1 8b instruct - .001, # FIXME: compute - ), - (LLAMA_3p1_70B_INSTRUCT, True): ( - 3.8235735702514626, - .001, # FIXME: compute - ), - (LLAMA_3p1_8B_INSTRUCT, False): ( - 2.6994638133048965, - 0.00047589250549208347, - ), - (GRANITE_3p2_8B_INSTRUCT, False): ( - 2.3919514417648315, - 0.0005767398688476533, - ), - (GRANITE_20B_CODE_INSTRUCT_8K, False): ( - 2.640706129074097, - 0.00034344267623964697, - ), - (LLAMA_3p1_70B_INSTRUCT, False): ( - 2.841279556751251, - 0.0044301633024588115, - ), -} -``` - -The command to run is: -```bash -pytest tests/models/test_decoders.py -vv -``` -Add the `-vv` for verbose output. - -### Test Results Samples - -Here is a result sample of the test outputs: - -```bash -Starting to run pytest tests/models/test_decoders.py -[ 0/ 1]: Sentient AIU: Enabled -============================= test session starts ============================== -platform linux -- Python 3.11.9, pytest-8.3.5, pluggy-1.5.0 -rootdir: /tmp/aiu-fms-testing-utils -plugins: durations-1.4.0, env-1.1.5 -collected 1 item - -tests/models/test_decoders.py . [100%] - -=============================== warnings summary =============================== -../foundation-model-stack/fms/triton/pytorch_ops.py:103 - /tmp/foundation-model-stack/fms/triton/pytorch_ops.py:103: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch. - @torch.library.impl_abstract("moe::moe_mm") - --- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html -============================= fixture duration top ============================= -total name num avg min -0:00:00.000140 grand total 5 0:00:00.000014 0:00:00.000012 -============================ test call duration top ============================ -total name num avg min -0:02:18.965102 test_common_shapes 1 0:02:18.965102 0:02:18.965102 -0:02:18.965102 grand total 1 0:02:18.965102 0:02:18.965102 -=========================== test setup duration top ============================ -total name num avg min -0:00:00.000553 grand total 1 0:00:00.000553 0:00:00.000553 -========================== test teardown duration top ========================== -total name num avg min -0:00:00.000969 grand total 1 0:00:00.000969 0:00:00.000969 -=================== 1 passed, 1 warning in 140.35s (0:02:20) =================== -Finished running pytests -``` -In case the thresholds fails: -```bash -[ 0/ 1]: testing model=/mnt/aiu-models-en-shared/models/hf/Mistral-7B-Instruct-v0.3, batch_size=1, seq_length=64, max_new_tokens=16, micro_model=False -[ 0/ 1]: AIU warmup -Using AIU_TOPO_FILE=/etc/aiu/topo.json -[ 0/ 1]: PT compile complete, took 211.912s -[ 0/ 1]: cpu validation info extracted for validation level 0 and validation level 1 (iter=0) -[ 0/ 1]: aiu validation info extracted for validation level 0 -[ 0/ 1]: failed validation level 0, testing validation level 1 -[ 0/ 1]: aiu validation info extracted for validation level 1 - iter=0 -[ 0/ 1]: cpu validation info extracted for validation level 1 - iter=1 -[ 0/ 1]: aiu validation info extracted for validation level 1 - iter=1 -[ 0/ 1]: cpu validation info extracted for validation level 1 - iter=2 -[...] (iteractions removed for better readability) -[ 0/ 1]: aiu validation info extracted for validation level 1 - iter=60 -[ 0/ 1]: cpu validation info extracted for validation level 1 - iter=61 -[ 0/ 1]: aiu validation info extracted for validation level 1 - iter=61 -[ 0/ 1]: cpu validation info extracted for validation level 1 - iter=62 -[ 0/ 1]: aiu validation info extracted for validation level 1 - iter=62 -[ 0/ 1]: cpu validation info extracted for validation level 1 - iter=63 -[ 0/ 1]: aiu validation info extracted for validation level 1 - iter=63 -[ 0/ 1]: mean diff failure rate: 0.7638888888888888 -[ 0/ 1]: cross entropy loss failure rate: 0.000992063492063492 -===================================================================================== fixture duration top ===================================================================================== -total name num avg min -0:00:00.000130 grand total 5 0:00:00.000012 0:00:00.000009 -==================================================================================== test call duration top ==================================================================================== -total name num avg min -0:16:31.480337 test_common_shapes 1 0:16:31.480337 0:16:31.480337 -0:16:31.480337 grand total 1 0:16:31.480337 0:16:31.480337 -=================================================================================== test setup duration top ==================================================================================== -total name num avg min -0:00:00.000555 grand total 1 0:00:00.000555 0:00:00.000555 -================================================================================== test teardown duration top ================================================================================== -total name num avg min -0:00:00.001416 grand total 1 0:00:00.001416 0:00:00.001416 -=================================================================================== short test summary info ==================================================================================== -FAILED tests/models/test_decoders.py::test_common_shapes[/mnt/aiu-models-en-shared/models/hf/Mistral-7B-Instruct-v0.3-1-64-16] - AssertionError: failure rate for mean diff was too high: 0.7638888888888888 -assert 0.7638888888888888 < 0.01 -``` -## 4. Run `test_model_expectations` - -- First add the desired model to the [decoder_models](./models/test_model_expectations.py#L55) variable. -- If the models tested are too big, it is a valid option to add the micro model version for this specific test. -- 4.1 Run `pytest tests/models/test_model_expectations.py::TestAIUDecoderModels --capture_expectation` to save the model weights. -After that you will get an output like this: -```bash -FAILED tests/models/test_model_expectations.py::TestAIUDecoderModels::test_model_output[/tmp/models/mistralai/Mistral-7B-Instruct-v0.3-True] - Failed: Signature file has been saved, please re-run the tests without --capture_expectation -FAILED tests/models/test_model_expectations.py::TestAIUDecoderModels::test_model_weight_keys[/tmp/models/mistralai/Mistral-7B-Instruct-v0.3-True] - Failed: Weights Key file has been saved, please re-run the tests without --capture_expectation -``` -This will tell that the weights and signature have been saved, so you can run the complete suite again to get the tests results. -- 4.2 Then running the complete suite: - -```bash -[1000780000@e2e-vllm-dt2-646f66647b-68dh6 aiu-fms-testing-utils]$ pytest tests/models/test_model_expectations.py::TestAIUDecoderModels -vv -[ 0/ 1]: Sentient AIU: Enabled -===================================================================================== test session starts ====================================================================================== -platform linux -- Python 3.12.5, pytest-8.3.5, pluggy-1.5.0 -- /usr/bin/python3.12 -cachedir: .pytest_cache -rootdir: /tmp/aiu-fms-testing-utils -plugins: durations-1.5.2, env-1.1.5 -collected 3 items - -tests/models/test_model_expectations.py::TestAIUDecoderModels::test_model_output[/tmp/models/mistralai/Mistral-7B-Instruct-v0.3-False] <- ../foundation-model-stack/fms/testing/_internal/model_test_suite.py PASSED [ 33%] -tests/models/test_model_expectations.py::TestAIUDecoderModels::test_model_weight_keys[/tmp/models/mistralai/Mistral-7B-Instruct-v0.3-False] <- ../foundation-model-stack/fms/testing/_internal/model_test_suite.py PASSED [ 66%] -tests/models/test_model_expectations.py::TestAIUDecoderModels::test_model_unfused[/tmp/models/mistralai/Mistral-7B-Instruct-v0.3] SKIPPED (All AIU models are already unfused) [100%] - -===================================================================================== fixture duration top ===================================================================================== -total name num avg min -0:00:02.201162 uninitialized_model 1 0:00:02.201162 0:00:02.201162 -0:00:00.051478 model 1 0:00:00.051478 0:00:00.051478 -0:00:02.252951 grand total 6 0:00:00.000135 0:00:00.000046 -==================================================================================== test call duration top ==================================================================================== -total name num avg min -0:03:05.951278 TestAIUDecoderModels::test_model_output 1 0:03:05.951278 0:03:05.951278 -0:03:05.954470 grand total 3 0:00:00.003095 0:00:00.000097 -=================================================================================== test setup duration top ==================================================================================== -total name num avg min -0:00:00.002004 grand total 3 0:00:00.000289 0:00:00.000102 -================================================================================== test teardown duration top ================================================================================== -total name num avg min -0:00:00.000363 grand total 3 0:00:00.000090 0:00:00.000077 -=========================================================================== 2 passed, 1 skipped in 189.01s (0:03:09) =========================================================================== - -``` - -In this case, the model tested was a decoder model with a single output, the `TestAIUDecoderModels` is the most important case. In the next section, check the applicability for the [TestAIUModelsTupleOutput](./README.md#case-of-multiple-output---testaiumodelstupleoutput) cases. - -#### Case of multiple output - TestAIUModelsTupleOutput - -The case **TestAIUModelsTupleOutput** is applicable if the model being tested has output of more than one tensor. Like the model in the example default [tuple_output_models](./models/test_model_expectations.py#L76), is a RoBERTa model that can output in this different format. - -- Add the model also to [tuple_output_models](./models/test_model_expectations.py#L76). -- 4.1 Run `pytest tests/models/test_model_expectations.py::TestAIUModelsTupleOutput --capture_expectation` to save the model weights; - -```bash -tests/models/test_model_expectations.py::TestAIUModelsTupleOutput::test_model_output[/ibm-dmf/models/watsonx/shared/granite-20b-code-cobol-v1/20240603-False] <- ../foundation-model-stack/fms/testing/_internal/model_test_suite.py PASSED [ 66%] -tests/models/test_model_expectations.py::TestAIUModelsTupleOutput::test_model_weight_keys[/ibm-dmf/models/watsonx/shared/granite-20b-code-cobol-v1/20240603-False] <- ../foundation-model-stack/fms/testing/_internal/model_test_suite.py PASSED [ 83%] -tests/models/test_model_expectations.py::TestAIUModelsTupleOutput::test_model_unfused[/ibm-dmf/models/watsonx/shared/granite-20b-code-cobol-v1/20240603] SKIPPED [100%] -``` - -> When adding new models expectations, please include in the PR with capture expectation tests added, the date of the image used to generate the file. - -Check this example of a PR for adding a new model expectations' files and results [here](https://github.com/foundation-model-stack/aiu-fms-testing-utils/pull/48). \ No newline at end of file +1. [Generate and run the model tests in this repo](./MODEL.md#model-tests) +2. 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zjS2dH-H8}zNvqjxg;*AU(XtH6L$F$A?)l`eJMn+h{f8C*XQs1Yk|E!WAb>Z4KXIi4 zZzPM}nQVWYe0n3Ka?pc-x%@hV;jcYV0QVJTZJDqUQjvTTi%MeTqyc~WI>y?inoePV E2WxxCw*UYD literal 0 HcmV?d00001 From dc311929f0699a387006deef59b6bade07800238 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 3 Jul 2025 11:35:25 -0300 Subject: [PATCH 24/60] Fix typos Signed-off-by: Flavia Beo --- tests/LAYERS.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/LAYERS.md b/tests/LAYERS.md index 6597c83..a3abe6b 100644 --- a/tests/LAYERS.md +++ b/tests/LAYERS.md @@ -6,7 +6,7 @@ Generate metrics by layers to be used in tests and model enablement debugging. 2. [Get Thresholds](./LAYERS.md#2-get-thresholds) 3. [Apply metrics where needed](./LAYERS.md#3-apply-the-thresholds-where-needed) -The steps as part of the diagram bellow: +The steps as part of the diagram below: ![generate flow](./resources/assets/metrics_fms_deepview_integration.zoom.png) To see the full integration with other debugging tools, check [item 3](./LAYERS.md#3-apply-the-thresholds-where-needed). @@ -26,10 +26,10 @@ export MAX_NEW_TOKENS=128 export OUTPUT_PATH=/tmp/output/granite ``` -These variables suport single and array values. +These variables support single and array values. The argument required for this script is the `--mode`, which is the generation mode desired for the output; The choices can be `generate` or `model-forward`. -- `generate` uses fms [generate](../scripts/generate_layers_metrics.py#L118) from ; It’s a high-level API that wraps many operations: forward pass, KV cache logic, sampling or greeting decoding, post-processing. +- `generate` uses FMS [generate](../scripts/generate_layers_metrics.py#L118); It’s a high-level API that wraps many operations: forward pass, KV cache logic, sampling or greeting decoding, post-processing. ```python result = generate( model, @@ -54,7 +54,7 @@ result = model.forward( ### How to run -Once all is set-up, we can generate the csv metrics: +Once all is set up, we can generate the CSV metrics: ```bash cd aiu-fms-testing-utils/tests/resources @@ -104,7 +104,7 @@ Also, a JSON file is saved to the same output dir. A sample file can be found at ## 3. Apply the thresholds where needed -In case of AIU debugging tools, the thresholds will be applied to compare AIU outputs with CPU, and then assert if the differences are within the threhsolds generated. Bellow, is an archtechture of the full integration: +In case of AIU debugging tools, the thresholds will be applied to compare AIU outputs with CPU, and then assert if the differences are within the thresholds generated. Bellow, is an architecture of the full integration: ![full integration](./resources/assets/metrics_fms_deepview_integration.full.png) The box named `deepview layer debug` has the diagram of how the model layers outputs are generated to be compared against the CPU results. 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za>9Y0FuN7H8j@&>OA~+!+fg^{e>5bZ_b;c(^()dV$%<>@wjnZ7xHQek-^q$B>wj|| z{&~?)dVsyuucUH?n7iQ8=)zjJ@;N}|)d`-!fY)zM*hCT7N|v`hBq1`ExHNaq2!7{Z z2!6gLW-6}lQNtfEV1_()$EAsY-By;H|FX&Fcdoeo>C3yX309sVh32@BoGmOHYbRPfx%8-AmW80$EAN z(K(Xj57~A7L~#-1i_y-0nS_zwtSN7A;`Czn($C0*y}EA~z&|nJr$TuG&)xn%qZlE( From 3ea408447a267dd3ccb483eed9ae4ea6b9c2b71a Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 3 Jul 2025 16:42:38 -0300 Subject: [PATCH 26/60] Prefix with sequence lenght on files' names Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index c6b8232..43dc889 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -294,7 +294,7 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): cos = nn.CosineSimilarity(dim=0) cos_sim = cos(tensor_cpu_out.unsqueeze(0), tensor_cuda_out.unsqueeze(0)).flatten().tolist() - prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, 0, 'float16') + prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, seq_length, 'float16') layer_name = str(layer).replace('[','').replace(']', '') abs_diff_path = os.path.join(output_dir, f"{prefix}--{layer_name}.abs_diff.csv") From ee32a6b1b29395b43cb2078111a6242798c2ccd7 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Mon, 7 Jul 2025 10:45:27 -0300 Subject: [PATCH 27/60] Adds output path to the json th Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 79a70c5..555678a 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -44,6 +44,13 @@ action="store_true", help="Sets the metric generation mode to layers IO" ) +parser.add_argument( + "--output_path", + type=str, + default="/tmp/aiu-fms-testing-utils/output", + required=False, + help="Path where the json thresholds output for the layers will be saved.", +) args = parser.parse_args() models = [model.replace("/", "--") for model in args.models] metrics = [metric for metric in args.metrics] @@ -93,7 +100,8 @@ result_dict[metric].append(tmp) logger.info(f"Layer {key} avg {metric} = {metric_val}") - f_result_path = os.path.join(file_base, f"{model}-thresholds.json") + json_output_path = args.output_path if args.output_path else file_base + f_result_path = os.path.join(json_output_path, f"{model}-thresholds.json") with open(f_result_path, 'w') as fp: json.dump(result_dict, fp) From 45b6514ad470e17ec81adccf4269f003dfb22783 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Mon, 7 Jul 2025 10:55:11 -0300 Subject: [PATCH 28/60] Catch StopIteration error Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 555678a..fecf900 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -69,9 +69,13 @@ for metric_file in metric_files: with open(metric_file, "r") as file: - next(file) - for line in file: - metric_list.append(float(line)) + try: + next(file) + for line in file: + metric_list.append(float(line)) + except StopIteration: + logger.info("No more metric files") + pass logger.info(f"found {len(metric_files)} metric files") logger.info(model, metric, np.percentile(metric_list, 99.0)) else: @@ -82,9 +86,13 @@ layer_name = layer_name.replace(".csv","") metric_layer_list = [] with open(metric_file, "r") as file: - next(file) - for line in file: - metric_layer_list.append(float(line)) + try: + next(file) + for line in file: + metric_layer_list.append(float(line)) + except StopIteration: + logger.info("No more metric files") + pass layer_dict[layer_name] = metric_layer_list layers.append(layer_dict) logger.info(f"found {len(layers)} layers metric files") From a7732e9408514baa617cfff56f1d1236eed2c665 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Mon, 7 Jul 2025 14:28:29 -0300 Subject: [PATCH 29/60] Adds docstring to methods Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 42 ++++++++++++++++++++++++++++++ tests/resources/get_thresholds.py | 20 ++++++++++++++ 2 files changed, 62 insertions(+) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 43dc889..5a0de46 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -97,6 +97,20 @@ def __prepare_inputs(batch_size, seq_length, tokenizer, seed=0): return input_ids, padding_kwargs def __infer_layer(model, max_len, device, max_new_tokens, batch_size, tokenizer): + """ + Infer a model with registered layer hooks using generated inputs. + + Args: + model (nn.Module): The model to infer. + max_len (int): The maximum length of the input sequence. + device (str): The device to use for inference. + max_new_tokens (int): The maximum number of new tokens to generate. + batch_size (int): The batch size for inference. + tokenizer (Tokenizer): The tokenizer to use for encoding inputs. + + Returns: + torch.Tensor: The inferred layer output. + """ do_sample = False use_cache = True @@ -139,6 +153,21 @@ def __infer_layer(model, max_len, device, max_new_tokens, batch_size, tokenizer) logger.info(f"Model forward completed: Result len is {len(result)}") def __register_call_layers(model, batch_size, device, seq_length, max_new_tokens, tokenizer): + """ + This function registers hooks on the model to track the forward pass of each layer. + It returns a list of tuples containing the name and output of each layer in the model. + + Args: + model (nn.Module): The PyTorch model to be analyzed. + batch_size (int): The batch size used for inference. + device (torch.device): The device on which the model is running. + seq_length (int): The maximum sequence length of the input data. + max_new_tokens (int): The maximum number of new tokens to be generated during inference. + tokenizer (Tokenizer): The tokenizer used for tokenization. + + Returns: + list: A list of tuples containing the name and output of each layer in the model. + """ layer_stack = [] pt_compile_model_time = time.time() @@ -228,6 +257,19 @@ def convert_tensor(output): return torch.zeros(size=(len(output), len(keys))) def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): + """ + Generate metrics for layers in a given model. + + Args: + model_path (str): The path to the Hugging Face model. + batch_size (int): The batch size used for inference. + seq_length (int): The sequence length used for inference. + max_new_tokens (int): The maximum number of new tokens allowed for generation. + + Returns: + None + """ + torch.manual_seed(42) os.environ["COMPILATION_MODE"] = "offline_decoder" diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index fecf900..80764c2 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -57,6 +57,25 @@ file_base = args.file_base layer_mode = args.file_base if args.file_base else False +def sort_list_of_dictionaries(data): + """Sorts a list of dictionaries alphabetically by key. + + Args: + data: A list of dictionaries, each with a single key-value pair. + + Returns: + A new list of dictionaries sorted alphabetically by key. + """ + # Extract keys and store them in a separate list + keys = [list(d.keys())[0] for d in data] + + # Get the indices that would sort the keys + sorted_indices = sorted(range(len(keys)), key=lambda k: keys[k]) + + # Reconstruct the list in the sorted order + sorted_data = [data[i] for i in sorted_indices] + return sorted_data + for model in models: result_dict = {"model_id": model} for metric in metrics: @@ -109,6 +128,7 @@ logger.info(f"Layer {key} avg {metric} = {metric_val}") json_output_path = args.output_path if args.output_path else file_base + result_dict = sort_list_of_dictionaries(result_dict) f_result_path = os.path.join(json_output_path, f"{model}-thresholds.json") with open(f_result_path, 'w') as fp: json.dump(result_dict, fp) From d35b5219b05da1d24f58bf27c41dbdbea202dc31 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 8 Jul 2025 10:15:48 -0300 Subject: [PATCH 30/60] Fix cosine similarity calculation Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 16 +++++++++------- tests/resources/get_thresholds.py | 3 ++- 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 5a0de46..b620279 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -241,9 +241,11 @@ def post_hook_fn(module, input, output): return layer_stack -def write_csv(l, path, metric): +def write_csv(l, path, metric, gpu_layer_shape, cpu_layer_shape, output_shape): with open(path, 'w') as f: f.write(f'{metric}\n') + f.write(f'GPU shape {gpu_layer_shape} CPU shape {cpu_layer_shape}\n') + f.write(f'Metric shape {output_shape}\n') if not type(l) is float: for t in l: f.write(f"{t}\n") @@ -332,9 +334,9 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): tensor_cpu_out = convert_tensor(cpu_output) else: tensor_cpu_out = cpu_output.to('cuda') - abs_diff = torch.abs(tensor_cpu_out - tensor_cuda_out).flatten().tolist() - cos = nn.CosineSimilarity(dim=0) - cos_sim = cos(tensor_cpu_out.unsqueeze(0), tensor_cuda_out.unsqueeze(0)).flatten().tolist() + abs_diff = torch.abs(tensor_cpu_out - tensor_cuda_out) + cos = nn.CosineSimilarity(dim=-1) + cos_sim = cos(tensor_cpu_out, tensor_cuda_out) prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, seq_length, 'float16') layer_name = str(layer).replace('[','').replace(']', '') @@ -344,11 +346,11 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): if not os.path.exists(abs_diff_path): logger.debug("saving abs_diff files") - write_csv(abs_diff, abs_diff_path, "abs_diff") + write_csv(abs_diff.flatten().tolist(), abs_diff_path, "abs_diff", tensor_cuda_out.shape, abs_diff.shape) if not os.path.exists(cos_sim_path): logger.debug("saving cos_sim files") - write_csv(cos_sim, cos_sim_path, "cos_sim") - + write_csv(cos_sim.flatten().tolist(), cos_sim_path, "cos_sim", tensor_cuda_out.shape, cos_sim.shape) + logger.info(f"Completed {model_path} layers' metrics generation") for model_id, batch_size, sequence_length, max_new_token in common_shapes: diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 80764c2..161bfd8 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -106,7 +106,8 @@ def sort_list_of_dictionaries(data): metric_layer_list = [] with open(metric_file, "r") as file: try: - next(file) + for _ in range(3): + next(file) for line in file: metric_layer_list.append(float(line)) except StopIteration: From d90b22721ed28b17068d795da94890c03fd3355b Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 8 Jul 2025 10:32:46 -0300 Subject: [PATCH 31/60] Fix print cpu output shape Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index b620279..ab34032 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -346,10 +346,10 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): if not os.path.exists(abs_diff_path): logger.debug("saving abs_diff files") - write_csv(abs_diff.flatten().tolist(), abs_diff_path, "abs_diff", tensor_cuda_out.shape, abs_diff.shape) + write_csv(abs_diff.flatten().tolist(), abs_diff_path, "abs_diff", tensor_cuda_out.shape, tensor_cpu_out.shape, abs_diff.shape) if not os.path.exists(cos_sim_path): logger.debug("saving cos_sim files") - write_csv(cos_sim.flatten().tolist(), cos_sim_path, "cos_sim", tensor_cuda_out.shape, cos_sim.shape) + write_csv(cos_sim.flatten().tolist(), cos_sim_path, "cos_sim", tensor_cuda_out.shape, tensor_cpu_out.shape, cos_sim.shape) logger.info(f"Completed {model_path} layers' metrics generation") From a6894ced97b75aaea7d62cc967dc6e334c34c7a0 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 8 Jul 2025 11:09:11 -0300 Subject: [PATCH 32/60] Order result JSON for th Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 161bfd8..936cfa6 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -127,9 +127,8 @@ def sort_list_of_dictionaries(data): tmp[key] = metric_val if not math.isnan(metric_val) else 0.0 result_dict[metric].append(tmp) logger.info(f"Layer {key} avg {metric} = {metric_val}") - + result_dict[metric] = sort_list_of_dictionaries(result_dict[metric]) json_output_path = args.output_path if args.output_path else file_base - result_dict = sort_list_of_dictionaries(result_dict) f_result_path = os.path.join(json_output_path, f"{model}-thresholds.json") with open(f_result_path, 'w') as fp: json.dump(result_dict, fp) From e41bf208fa8caa9d1e15a5dfff334ac3327da2d5 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 8 Jul 2025 14:09:23 -0300 Subject: [PATCH 33/60] Review fixes required Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 23 +++++++++++--- tests/resources/get_thresholds.py | 48 +++++++++++++++++------------- 2 files changed, 47 insertions(+), 24 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index ab34032..42dad7f 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -241,16 +241,31 @@ def post_hook_fn(module, input, output): return layer_stack -def write_csv(l, path, metric, gpu_layer_shape, cpu_layer_shape, output_shape): +def write_csv(values, path, metric, gpu_layer_shape, cpu_layer_shape, output_shape): + """ + Write values to a CSV file at the given path. + + Args: + values (list or float): A list of values to be written to the CSV file. + If `values` is a single float, it will be written as a scalar value in the first column of the CSV file. + path (str): The path to the CSV file to write to. + metric (str): The name of the metric being evaluated. + gpu_layer_shape (tuple): The shape of the GPU layer used for training. + cpu_layer_shape (tuple): The shape of the CPU layer used for training. + output_shape (tuple): The shape of the output generated by the model. + + Returns: + None + """ with open(path, 'w') as f: f.write(f'{metric}\n') f.write(f'GPU shape {gpu_layer_shape} CPU shape {cpu_layer_shape}\n') f.write(f'Metric shape {output_shape}\n') - if not type(l) is float: - for t in l: + if not isinstance(values, float): + for t in values: f.write(f"{t}\n") else: - f.write(f"{l}\n") + f.write(f"{values}\n") f.close() def convert_tensor(output): diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 936cfa6..96a3c5b 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -76,6 +76,32 @@ def sort_list_of_dictionaries(data): sorted_data = [data[i] for i in sorted_indices] return sorted_data +def load_metric_file(file_path, layer_header): + """ + Loads a metric file and returns its values as a list of floats. + + Args: + file_path (str): The path to the metric file. + layer_header (bool): Whether to skip the first three lines of the file. Default is False. + + Returns: + list[float]: A list of metric values read from the file. + """ + values = [] + try: + with open(file_path, "r") as file: + if layer_header: + for _ in range(3): + next(file) + else: + next(file) # skip single header + for line in file: + values.append(float(line)) + except StopIteration: + logger.info("Path empty or no more metric files found.") + pass + return values + for model in models: result_dict = {"model_id": model} for metric in metrics: @@ -83,18 +109,9 @@ def sort_list_of_dictionaries(data): metric_files = glob.glob(path) result_dict[metric] = [] metric_list = [] - if not layer_mode: for metric_file in metric_files: - - with open(metric_file, "r") as file: - try: - next(file) - for line in file: - metric_list.append(float(line)) - except StopIteration: - logger.info("No more metric files") - pass + metric_list = load_metric_file(metric_file) logger.info(f"found {len(metric_files)} metric files") logger.info(model, metric, np.percentile(metric_list, 99.0)) else: @@ -103,16 +120,7 @@ def sort_list_of_dictionaries(data): layer_dict = {} layer_name = metric_file.split("--")[-1].replace(".{}".format(metric), "") layer_name = layer_name.replace(".csv","") - metric_layer_list = [] - with open(metric_file, "r") as file: - try: - for _ in range(3): - next(file) - for line in file: - metric_layer_list.append(float(line)) - except StopIteration: - logger.info("No more metric files") - pass + metric_layer_list = load_metric_file(metric_file) layer_dict[layer_name] = metric_layer_list layers.append(layer_dict) logger.info(f"found {len(layers)} layers metric files") From 86b5feade8bb141417e6f4e4783a94e3164fc2b2 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 8 Jul 2025 14:12:22 -0300 Subject: [PATCH 34/60] Adds layer mode header Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 96a3c5b..489a813 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -111,7 +111,7 @@ def load_metric_file(file_path, layer_header): metric_list = [] if not layer_mode: for metric_file in metric_files: - metric_list = load_metric_file(metric_file) + metric_list = load_metric_file(metric_file, layer_mode) logger.info(f"found {len(metric_files)} metric files") logger.info(model, metric, np.percentile(metric_list, 99.0)) else: @@ -120,7 +120,7 @@ def load_metric_file(file_path, layer_header): layer_dict = {} layer_name = metric_file.split("--")[-1].replace(".{}".format(metric), "") layer_name = layer_name.replace(".csv","") - metric_layer_list = load_metric_file(metric_file) + metric_layer_list = load_metric_file(metric_file, layer_mode) layer_dict[layer_name] = metric_layer_list layers.append(layer_dict) logger.info(f"found {len(layers)} layers metric files") From 41b849a10ec941f6865c88d446ad99e16584c9ca Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 8 Jul 2025 15:18:53 -0300 Subject: [PATCH 35/60] Includes head sub-tensors values Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 68 ++++++++++++++++++++++-------- 1 file changed, 50 insertions(+), 18 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 42dad7f..32bae92 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -241,6 +241,16 @@ def post_hook_fn(module, input, output): return layer_stack +def get_metric_values(metric_list): + if isinstance(metric_list, list): + metric_shape = metric_list[0].shape + metric_list_res = metric_list + else: + metric_shape = metric_list.shape + metric_list_res = metric_list.flatten().tolist() + + return metric_list_res, metric_shape + def write_csv(values, path, metric, gpu_layer_shape, cpu_layer_shape, output_shape): """ Write values to a CSV file at the given path. @@ -268,11 +278,6 @@ def write_csv(values, path, metric, gpu_layer_shape, cpu_layer_shape, output_sha f.write(f"{values}\n") f.close() -def convert_tensor(output): - out_unique = set(list(itertools.chain.from_iterable(output))) - keys = {key: value for key, value in zip(out_unique, range(len(out_unique)))} - return torch.zeros(size=(len(output), len(keys))) - def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): """ Generate metrics for layers in a given model. @@ -334,24 +339,48 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): assert len(layer_stack_cuda) == len(layer_stack_cpu) for layer, cuda_output in layer_stack_cuda: - tensor_cuda_out = None tensor_cpu_out = None - abs_diff = None + tensor_cuda_out = None + cos = nn.CosineSimilarity(dim=-1) for cpu_layer, cpu_output in layer_stack_cpu: if cpu_layer == layer: logger.info("CPU Layer {} GPU Layer {}".format(cpu_layer, layer)) - if not type(cuda_output) is tuple: - tensor_cuda_out = cuda_output - else: - tensor_cuda_out = convert_tensor(cuda_output) - if type(cpu_output) is tuple: - tensor_cpu_out = convert_tensor(cpu_output) + if type(cpu_output) is tuple and type(cuda_output) is tuple: + cos_sim = [] + abs_diff = [] + if len(cpu_layer) < 2 and len(cpu_layer[-1]) == 1: + tensor_cuda_out = cuda_output[-1] + tensor_cpu_out = cpu_layer[-1] + for i in range(len(cpu_layer)): + logger.debug(f"inputs: {cuda_output[i].shape} {cpu_output[i].to('cuda').shape}") + cos_sim.append(cos(cuda_output[i], cpu_output[i].to('cuda'))) + logger.debug(f"cos_sim output:{cos(cuda_output[i], cpu_output[i].to('cuda')).shape}") + abs_diff.append(torch.abs(cuda_output[i] - cpu_output[i].to('cuda'))) + else: + head_tensor_cpu = cpu_output[-1] + head_tensor_gpu = cuda_output[-1] + for i in range(len(head_tensor_gpu)): + if isinstance(head_tensor_gpu[i], tuple): + for j in range(len(head_tensor_gpu[i])): + tensor_cuda_out = head_tensor_gpu[i][j] + tensor_cpu_out = head_tensor_cpu[i][j] + logger.debug(f"inputs: {head_tensor_gpu[i][j].shape} {head_tensor_cpu[i][j].to('cuda').shape}") + cos_sim.append(cos(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j])) + logger.debug(f"cos_sim output:{cos(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j]).shape}") + abs_diff.append(torch.abs(head_tensor_cpu[i][j].to('cuda') - head_tensor_gpu[i][j])) + else: + tensor_cuda_out = head_tensor_gpu[i] + tensor_cpu_out = head_tensor_cpu[i] + logger.debug(f"inputs: {head_tensor_gpu[i].shape} {head_tensor_cpu[i].to('cuda').shape}") + cos_sim.append(cos(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i])) + logger.debug(f"cos_sim output:{cos(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i]).shape}") + abs_diff.append(torch.abs(head_tensor_cpu[i].to('cuda') - head_tensor_gpu[i])) else: tensor_cpu_out = cpu_output.to('cuda') - abs_diff = torch.abs(tensor_cpu_out - tensor_cuda_out) - cos = nn.CosineSimilarity(dim=-1) - cos_sim = cos(tensor_cpu_out, tensor_cuda_out) + tensor_cuda_out = cuda_output + abs_diff = torch.abs(tensor_cpu_out - cuda_output) + cos_sim = cos(tensor_cpu_out, cuda_output) prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, seq_length, 'float16') layer_name = str(layer).replace('[','').replace(']', '') @@ -359,12 +388,15 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): abs_diff_path = os.path.join(output_dir, f"{prefix}--{layer_name}.abs_diff.csv") cos_sim_path = os.path.join(output_dir, f"{prefix}--{layer_name}.cos_sim.csv") + cos_sim_res, cos_shape = get_metric_values(cos_sim) + abs_diff_res, abs_diff_shape = get_metric_values(abs_diff) + if not os.path.exists(abs_diff_path): logger.debug("saving abs_diff files") - write_csv(abs_diff.flatten().tolist(), abs_diff_path, "abs_diff", tensor_cuda_out.shape, tensor_cpu_out.shape, abs_diff.shape) + write_csv(abs_diff_res, abs_diff_path, "abs_diff", tensor_cuda_out.shape, tensor_cpu_out.shape, abs_diff_shape) if not os.path.exists(cos_sim_path): logger.debug("saving cos_sim files") - write_csv(cos_sim.flatten().tolist(), cos_sim_path, "cos_sim", tensor_cuda_out.shape, tensor_cpu_out.shape, cos_sim.shape) + write_csv(cos_sim_res, cos_sim_path, "cos_sim", tensor_cuda_out.shape, tensor_cpu_out.shape, cos_shape) logger.info(f"Completed {model_path} layers' metrics generation") From db9b9fe87da190b7b48de38e517e2fec340bc274 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 8 Jul 2025 18:52:17 -0300 Subject: [PATCH 36/60] Metric list shape Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 32bae92..0274dc9 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -244,7 +244,7 @@ def post_hook_fn(module, input, output): def get_metric_values(metric_list): if isinstance(metric_list, list): metric_shape = metric_list[0].shape - metric_list_res = metric_list + metric_list_res = metric_list[0] else: metric_shape = metric_list.shape metric_list_res = metric_list.flatten().tolist() From d4aa817988275866aba0cb8fd600d5a7697559f0 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Tue, 8 Jul 2025 20:29:23 -0300 Subject: [PATCH 37/60] Metric list shape Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 0274dc9..e532d2a 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -244,7 +244,7 @@ def post_hook_fn(module, input, output): def get_metric_values(metric_list): if isinstance(metric_list, list): metric_shape = metric_list[0].shape - metric_list_res = metric_list[0] + metric_list_res = metric_list[0].flatten().tolist() else: metric_shape = metric_list.shape metric_list_res = metric_list.flatten().tolist() From b8d900c274c092e41cc740c7d651366c36e58ffa Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 9 Jul 2025 12:24:04 -0300 Subject: [PATCH 38/60] First part of review fixes requested Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 80 ++++++++++++++++++++++-------- tests/LAYERS.md | 2 +- 2 files changed, 60 insertions(+), 22 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index e532d2a..dec2ecc 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -27,6 +27,14 @@ description="Script to generate the model's metrics by layer" ) +parser.add_argument( + "--models", + type=str, + default=[], + nargs='+', + required=True, + help="List of models id separated by space. Eg.: ibm-granite/granite-20b-code-instruct-8k /tmp/models/granite-20b-code-cobol-v1" +) parser.add_argument( "--mode", choices=["generate", "model-forward"], @@ -34,39 +42,65 @@ required=True, help="Sets the output generation mode." ) - -args = parser.parse_args() -mode = args.mode - -ORIGINAL_HF_HOME = os.environ.get("HF_HOME", None) - -SHARE_GPT_DATASET_PATH = os.environ.get( - "SHARE_GPT_DATASET_PATH", os.path.expanduser("~/share_gpt.json") +parser.add_argument( + "--batch_sizes", + type=int, + default=1, + nargs='+', + required=True, + help="Batch sizes separated by comma." ) - -common_model_paths = os.environ.get( - "MODEL_PATHS", - ["ibm-granite/granite-3.2-8b-instruct"], +parser.add_argument( + "--seq_lengths", + type=int, + default=64, + nargs='+', + required=True, + help="Sequence lengths separated by comma." +) +parser.add_argument( + "--max_new_tokens", + type=int, + default=128, + nargs='+', + required=True, + help="Max number of generated tokens separated by comma." +) +parser.add_argument( + "--output_path", + type=str, + default="/tmp/output", + help="Path to save output files" +) +parser.add_argument( + "--sharegpt_path", + type=str, + default=os.path.expanduser("~/share_gpt.json"), + help="Path to sharegpt data json" ) -common_batch_sizes = os.environ.get("BATCH_SIZES", [1, 2, 4, 8]) -common_seq_lengths = os.environ.get("SEQ_LENGTHS", [64, 2048]) -common_max_new_tokens = os.environ.get("MAX_NEW_TOKENS", [128]) -output_dir = os.environ.get("OUTPUT_PATH", "/tmp/output") +args = parser.parse_args() +mode = args.mode +output_path = args.output_path +sharegpt_path = args.sharegpt_path # pass custom model path list for eg: EXPORT FMS_TESTING_COMMON_MODEL_PATHS="/tmp/models/granite-3-8b-base,/tmp/models/granite-7b-base" +common_model_paths = args.models if isinstance(common_model_paths, str): common_model_paths = common_model_paths.split(",") # pass custom common batch sizes as a comma separated str of ints +common_batch_sizes = args.batch_sizes if isinstance(common_batch_sizes, str): common_batch_sizes = [int(bs) for bs in common_batch_sizes.split(",")] # pass custom common seq lengths as a comma separated str of ints +common_seq_lengths = args.seq_lengths if isinstance(common_seq_lengths, str): common_seq_lengths = [int(sl) for sl in common_seq_lengths.split(",")] # pass custom common max new tokens as a comma separated str of ints +common_max_new_tokens = args.max_new_tokens if isinstance(common_max_new_tokens, str): common_max_new_tokens = [int(mnt) for mnt in common_max_new_tokens.split(",")] @@ -81,7 +115,7 @@ def __prepare_inputs(batch_size, seq_length, tokenizer, seed=0): prompts_and_sizes = sample_sharegpt_requests( - SHARE_GPT_DATASET_PATH, + sharegpt_path, batch_size, tokenizer, int(seq_length / 2), @@ -243,8 +277,9 @@ def post_hook_fn(module, input, output): def get_metric_values(metric_list): if isinstance(metric_list, list): - metric_shape = metric_list[0].shape - metric_list_res = metric_list[0].flatten().tolist() + # shape of the first tensor to be printed in file + metric_shape = metric_list[0].shape + metric_list_res = torch.stack(metric_list).flatten().tolist() else: metric_shape = metric_list.shape metric_list_res = metric_list.flatten().tolist() @@ -350,6 +385,9 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): cos_sim = [] abs_diff = [] if len(cpu_layer) < 2 and len(cpu_layer[-1]) == 1: + # Some layers have tuple outputs, with more than 2 tensors - this path compares this type of output; + # In case of head layers, the last item of the tuple is a list of tensors with the same lenght as the + # number of layers in the model. The else path compares this other case. tensor_cuda_out = cuda_output[-1] tensor_cpu_out = cpu_layer[-1] for i in range(len(cpu_layer)): @@ -385,8 +423,8 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, seq_length, 'float16') layer_name = str(layer).replace('[','').replace(']', '') - abs_diff_path = os.path.join(output_dir, f"{prefix}--{layer_name}.abs_diff.csv") - cos_sim_path = os.path.join(output_dir, f"{prefix}--{layer_name}.cos_sim.csv") + abs_diff_path = os.path.join(output_path, f"{prefix}--{layer_name}.abs_diff.csv") + cos_sim_path = os.path.join(output_path, f"{prefix}--{layer_name}.cos_sim.csv") cos_sim_res, cos_shape = get_metric_values(cos_sim) abs_diff_res, abs_diff_shape = get_metric_values(abs_diff) diff --git a/tests/LAYERS.md b/tests/LAYERS.md index a3abe6b..8dba458 100644 --- a/tests/LAYERS.md +++ b/tests/LAYERS.md @@ -104,7 +104,7 @@ Also, a JSON file is saved to the same output dir. A sample file can be found at ## 3. Apply the thresholds where needed -In case of AIU debugging tools, the thresholds will be applied to compare AIU outputs with CPU, and then assert if the differences are within the thresholds generated. Bellow, is an architecture of the full integration: +In case of AIU debugging tools, the thresholds will be applied to compare AIU outputs with CPU, and then assert if the differences are within the thresholds generated. Below, is an architecture of the full integration: ![full integration](./resources/assets/metrics_fms_deepview_integration.full.png) The box named `deepview layer debug` has the diagram of how the model layers outputs are generated to be compared against the CPU results. This is important so that the debug tools can catch operations and layers that have issues in their enablement for AIU hardware. \ No newline at end of file From 1c800e3b5c024f2b6c640c48e13b0d4c1918f702 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 9 Jul 2025 12:31:06 -0300 Subject: [PATCH 39/60] Help argsparse added Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 21 +++++++++------------ 1 file changed, 9 insertions(+), 12 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index dec2ecc..1238517 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -44,27 +44,24 @@ ) parser.add_argument( "--batch_sizes", - type=int, - default=1, - nargs='+', + type=str, + default="1", required=True, - help="Batch sizes separated by comma." + help="Batch sizes separated by comma. Eg.: 1,2" ) parser.add_argument( "--seq_lengths", - type=int, - default=64, - nargs='+', + type=str, + default="64", required=True, - help="Sequence lengths separated by comma." + help="Sequence lengths separated by comma. Eg.: 64,2048" ) parser.add_argument( "--max_new_tokens", - type=int, - default=128, - nargs='+', + type=str, + default="128", required=True, - help="Max number of generated tokens separated by comma." + help="Max number of generated tokens separated by comma. Eg.: 64,128" ) parser.add_argument( "--output_path", From f74cbbc607c4d28924724c4e91a9b43133883ff1 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 9 Jul 2025 12:46:27 -0300 Subject: [PATCH 40/60] Adds docs about the arg parse Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 7 ++--- tests/LAYERS.md | 42 ++++++++++++++++++++++-------- 2 files changed, 33 insertions(+), 16 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 1238517..96d2aff 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -33,7 +33,7 @@ default=[], nargs='+', required=True, - help="List of models id separated by space. Eg.: ibm-granite/granite-20b-code-instruct-8k /tmp/models/granite-20b-code-cobol-v1" + help="List of models id separated by space. Eg.: ibm-granite/granite-3.3-8b-instruct mistralai/Mistral-7B-Instruct-v0.3" ) parser.add_argument( "--mode", @@ -81,10 +81,7 @@ output_path = args.output_path sharegpt_path = args.sharegpt_path -# pass custom model path list for eg: EXPORT FMS_TESTING_COMMON_MODEL_PATHS="/tmp/models/granite-3-8b-base,/tmp/models/granite-7b-base" -common_model_paths = args.models -if isinstance(common_model_paths, str): - common_model_paths = common_model_paths.split(",") +common_model_paths = [model for model in args.models] # pass custom common batch sizes as a comma separated str of ints common_batch_sizes = args.batch_sizes diff --git a/tests/LAYERS.md b/tests/LAYERS.md index 8dba458..bc7a6c9 100644 --- a/tests/LAYERS.md +++ b/tests/LAYERS.md @@ -16,14 +16,34 @@ The idea is to run, the prompts through the model with the pre- and post-hooks a ![metrics generation by layer](./resources/assets/metrics_generation_layers.png) -The script [generate_layers_metrics.py](../scripts/generate_layers_metrics.py) requires the following environment variables: +The script [generate_layers_metrics.py](../scripts/generate_layers_metrics.py) requires the following arguments to be run: ```bash -export MODEL_PATHS=ibm-granite/granite-3.2-8b-instruct -export BATCH_SIZES=1 -export SEQ_LENGTHS=64 -export MAX_NEW_TOKENS=128 -export OUTPUT_PATH=/tmp/output/granite +usage: generate_layers_metrics.py [-h] --models MODELS [MODELS ...] --mode + {generate,model-forward} --batch_sizes BATCH_SIZES + [BATCH_SIZES ...] --seq_lengths SEQ_LENGTHS [SEQ_LENGTHS ...] + --max_new_tokens MAX_NEW_TOKENS [MAX_NEW_TOKENS ...] + [--output_path OUTPUT_PATH] [--sharegpt_path SHAREGPT_PATH] + +Script to generate the model's metrics by layer + +options: + -h, --help show this help message and exit + --models MODELS [MODELS ...] + List of models id separated by space. Eg.: ibm-granite/granite-20b-code- + instruct-8k /tmp/models/granite-20b-code-cobol-v1 + --mode {generate,model-forward} + Sets the output generation mode. + --batch_sizes BATCH_SIZES [BATCH_SIZES ...] + Batch sizes separated by comma. + --seq_lengths SEQ_LENGTHS [SEQ_LENGTHS ...] + Sequence lengths separated by comma. + --max_new_tokens MAX_NEW_TOKENS [MAX_NEW_TOKENS ...] + Max number of generated tokens separated by comma. + --output_path OUTPUT_PATH + Path to save output files + --sharegpt_path SHAREGPT_PATH + Path to sharegpt data json ``` These variables support single and array values. @@ -61,14 +81,14 @@ cd aiu-fms-testing-utils/tests/resources mkdir /tmp/output -python3 aiu-fms-testing-utils/scripts/generate_layers_metrics.py --mode generate +python3 generate_layers_metrics.py --mode model-forward --models ibm-granite/granite-20b-code-instruct-8k --batch_sizes 1 --seq_lengths 64 --max_new_tokens 128 ``` The files should get created at `/tmp/output` dir: ```bash -ibm-granite--granite-3.2-8b-instruct_max-new-tokens-128_batch-size-1_seq-length-0_dtype-float16--model.base_model.layers7.ln.abs_diff.csv -ibm-granite--granite-3.2-8b-instruct_max-new-tokens-128_batch-size-1_seq-length-0_dtype-float16--model.base_model.layers7.ln.cos_sim.csv -ibm-granite--granite-3.2-8b-instruct_max-new-tokens-128_batch-size-1_seq-length-0_dtype-float16--model.base_model.layers8.attn.dense.abs_diff.csv -ibm-granite--granite-3.2-8b-instruct_max-new-tokens-128_batch-size-1_seq-length-0_dtype-float16--model.base_model.layers8.attn.dense.cos_sim.csv +ibm-granite--granite-3.2-8b-instruct_max-new-tokens-128_batch-size-1_seq-length-64_dtype-float16--model.base_model.layers7.ln.abs_diff.csv +ibm-granite--granite-3.2-8b-instruct_max-new-tokens-128_batch-size-1_seq-length-64_dtype-float16--model.base_model.layers7.ln.cos_sim.csv +ibm-granite--granite-3.2-8b-instruct_max-new-tokens-128_batch-size-1_seq-length-64_dtype-float16--model.base_model.layers8.attn.dense.abs_diff.csv +ibm-granite--granite-3.2-8b-instruct_max-new-tokens-128_batch-size-1_seq-length-64_dtype-float16--model.base_model.layers8.attn.dense.cos_sim.csv ``` ## 2. Get Thresholds From c8aed03bd978dd37fddd5d2a4bea179c625e7986 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 9 Jul 2025 13:23:47 -0300 Subject: [PATCH 41/60] Modifies the th output json to all dicts Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 29 ++++------------------------- 1 file changed, 4 insertions(+), 25 deletions(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 489a813..0236157 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -57,25 +57,6 @@ file_base = args.file_base layer_mode = args.file_base if args.file_base else False -def sort_list_of_dictionaries(data): - """Sorts a list of dictionaries alphabetically by key. - - Args: - data: A list of dictionaries, each with a single key-value pair. - - Returns: - A new list of dictionaries sorted alphabetically by key. - """ - # Extract keys and store them in a separate list - keys = [list(d.keys())[0] for d in data] - - # Get the indices that would sort the keys - sorted_indices = sorted(range(len(keys)), key=lambda k: keys[k]) - - # Reconstruct the list in the sorted order - sorted_data = [data[i] for i in sorted_indices] - return sorted_data - def load_metric_file(file_path, layer_header): """ Loads a metric file and returns its values as a list of floats. @@ -107,7 +88,7 @@ def load_metric_file(file_path, layer_header): for metric in metrics: path = os.path.join(file_base, f"{model}*{metric}*.csv") metric_files = glob.glob(path) - result_dict[metric] = [] + result_dict[metric] = {} metric_list = [] if not layer_mode: for metric_file in metric_files: @@ -132,12 +113,10 @@ def load_metric_file(file_path, layer_header): metric_val = np.linalg.norm(l[key]) else: metric_val = np.mean(l[key]) - tmp[key] = metric_val if not math.isnan(metric_val) else 0.0 - result_dict[metric].append(tmp) + result_dict[metric][key] = metric_val if not math.isnan(metric_val) else 0.0 logger.info(f"Layer {key} avg {metric} = {metric_val}") - result_dict[metric] = sort_list_of_dictionaries(result_dict[metric]) + json_output_path = args.output_path if args.output_path else file_base f_result_path = os.path.join(json_output_path, f"{model}-thresholds.json") with open(f_result_path, 'w') as fp: - json.dump(result_dict, fp) - + json.dump(result_dict, fp) \ No newline at end of file From fc249c50fd5c80628e09ab4628e55589fb62dd9e Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 9 Jul 2025 14:11:17 -0300 Subject: [PATCH 42/60] Moves methods to utils Signed-off-by: Flavia Beo --- aiu_fms_testing_utils/utils/__init__.py | 18 +++++ aiu_fms_testing_utils/utils/metrics_utils.py | 79 ++++++++++++++++++++ scripts/generate_layers_metrics.py | 50 ++++--------- tests/resources/get_thresholds.py | 8 +- 4 files changed, 118 insertions(+), 37 deletions(-) create mode 100644 aiu_fms_testing_utils/utils/metrics_utils.py diff --git a/aiu_fms_testing_utils/utils/__init__.py b/aiu_fms_testing_utils/utils/__init__.py index bec9559..ffdb6ee 100644 --- a/aiu_fms_testing_utils/utils/__init__.py +++ b/aiu_fms_testing_utils/utils/__init__.py @@ -2,6 +2,8 @@ import torch.nn as nn import time from fms.utils.tokenizers import BaseTokenizer +from fms.utils.generation import pad_input_ids + from aiu_fms_testing_utils.utils.aiu_setup import dprint from typing import Optional, List, Tuple import os @@ -135,3 +137,19 @@ def sample_squad_v2_qa_requests( return __sample_requests(ds, num_requests, tokenizer, prompt_length_min, prompt_length_max, seed) +def prepare_inputs(batch_size, seq_length, tokenizer, sharegpt_path, seed=0): + prompts_and_sizes = sample_sharegpt_requests( + sharegpt_path, + batch_size, + tokenizer, + int(seq_length / 2), + seq_length, + seed, + ) + ## TODO: for each prompt + prompt_list = [] + for prompt, _ in prompts_and_sizes: + prompt_list.append(ids_for_prompt(prompt, tokenizer)) + + input_ids, padding_kwargs = pad_input_ids(prompt_list, min_pad_length=seq_length) + return input_ids, padding_kwargs \ No newline at end of file diff --git a/aiu_fms_testing_utils/utils/metrics_utils.py b/aiu_fms_testing_utils/utils/metrics_utils.py new file mode 100644 index 0000000..4e4f546 --- /dev/null +++ b/aiu_fms_testing_utils/utils/metrics_utils.py @@ -0,0 +1,79 @@ +import numpy as np +import torch +import torch.nn as nn + + +def abs_diff_linalg_norm(res_vector): + """ + Calculates the Euclidean norm (also known as the L2 norm) of a given array res_vector. This is equivalent to finding the square + root of the sum of the squares of all the elements in the array. It's a fundamental operation in linear algebra and is often used + to measure the "length" or "magnitude" of a vector. More at https://numpy.org/devdocs/reference/generated/numpy.linalg.norm.html + Args: + res_vector (list): The list of abs diff + + Returns: + float: "magnitude" of the diff vector. + """ + return np.linalg.norm(res_vector) + +def list_mean(val_list): + """ + Calculates the mean for all the values in a given list. + Args: + val_list (list): The list of values + + Returns: + float: mean value calculated. + """ + return np.mean(val_list) + +def list_avg(val_list): + """ + Calculates the average for all the values in a given list. + Args: + val_list (list): The list of values + + Returns: + float: average value calculated. + """ + return np.average(val_list) + +def tensor_abs_diff(tensor1, tensor2): + """ + Calculate the absolute difference between two tensors. + + Args: + tensor1 (torch.Tensor): The first input tensor. + tensor2 (torch.Tensor): The second input tensor. + + Returns: + torch.Tensor: The absolute difference tensor. + + Example: + >>> tensor1 = torch.tensor([1, 2, 3]) + >>> tensor2 = torch.tensor([4, 5, 6]) + >>> abs_diff(tensor1, tensor2) + torch.tensor([3, 3, 3]) + """ + return torch.abs(tensor1 - tensor2) + +def tensor_cos_sim(tensor1, tensor2): + """ + Computes the cosine similarity between two tensors. + + Args: + tensor1 (torch.Tensor): The first input tensor. + tensor2 (torch.Tensor): The second input tensor. + + Returns: + torch.Tensor: The cosine similarity between the two input tensors. + + Example: + >>> import torch + >>> tensor1 = torch.randn(3, 5) + >>> tensor2 = torch.randn(3, 5) + >>> sim = cos_sim(tensor1, tensor2) + >>> print(sim) + """ + cos = nn.CosineSimilarity(dim=-1) + return cos(tensor1, tensor2) \ No newline at end of file diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 96d2aff..b7f84c5 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -9,14 +9,12 @@ from fms.utils import tokenizers from fms.models import get_model -from fms.utils.generation import pad_input_ids, generate +from fms.utils.generation import generate from aiu_fms_testing_utils.testing.validation import get_default_validation_prefix -from aiu_fms_testing_utils.utils import ( - sample_sharegpt_requests, - ids_for_prompt, -) +from aiu_fms_testing_utils.utils import prepare_inputs +from aiu_fms_testing_utils.utils.metrics_utils import tensor_abs_diff, tensor_cos_sim logger = logging.getLogger(__name__) @@ -107,23 +105,6 @@ ) ) -def __prepare_inputs(batch_size, seq_length, tokenizer, seed=0): - prompts_and_sizes = sample_sharegpt_requests( - sharegpt_path, - batch_size, - tokenizer, - int(seq_length / 2), - seq_length, - seed, - ) - ## TODO: for each prompt - prompt_list = [] - for prompt, _ in prompts_and_sizes: - prompt_list.append(ids_for_prompt(prompt, tokenizer)) - - input_ids, padding_kwargs = pad_input_ids(prompt_list, min_pad_length=seq_length) - return input_ids, padding_kwargs - def __infer_layer(model, max_len, device, max_new_tokens, batch_size, tokenizer): """ Infer a model with registered layer hooks using generated inputs. @@ -143,7 +124,7 @@ def __infer_layer(model, max_len, device, max_new_tokens, batch_size, tokenizer) do_sample = False use_cache = True - prompts = __prepare_inputs(batch_size, max_len, tokenizer) + prompts = prepare_inputs(batch_size, max_len, tokenizer, sharegpt_path) ids, pad_input_ids = prompts if "cuda" in device: @@ -370,7 +351,6 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): for layer, cuda_output in layer_stack_cuda: tensor_cpu_out = None tensor_cuda_out = None - cos = nn.CosineSimilarity(dim=-1) for cpu_layer, cpu_output in layer_stack_cpu: if cpu_layer == layer: logger.info("CPU Layer {} GPU Layer {}".format(cpu_layer, layer)) @@ -386,9 +366,9 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): tensor_cpu_out = cpu_layer[-1] for i in range(len(cpu_layer)): logger.debug(f"inputs: {cuda_output[i].shape} {cpu_output[i].to('cuda').shape}") - cos_sim.append(cos(cuda_output[i], cpu_output[i].to('cuda'))) - logger.debug(f"cos_sim output:{cos(cuda_output[i], cpu_output[i].to('cuda')).shape}") - abs_diff.append(torch.abs(cuda_output[i] - cpu_output[i].to('cuda'))) + cos_sim.append(tensor_cos_sim(cuda_output[i], cpu_output[i].to('cuda'))) + logger.debug(f"cos_sim output:{tensor_cos_sim(cuda_output[i], cpu_output[i].to('cuda')).shape}") + abs_diff.append(tensor_abs_diff(cuda_output[i] - cpu_output[i].to('cuda'))) else: head_tensor_cpu = cpu_output[-1] head_tensor_gpu = cuda_output[-1] @@ -398,21 +378,21 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): tensor_cuda_out = head_tensor_gpu[i][j] tensor_cpu_out = head_tensor_cpu[i][j] logger.debug(f"inputs: {head_tensor_gpu[i][j].shape} {head_tensor_cpu[i][j].to('cuda').shape}") - cos_sim.append(cos(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j])) - logger.debug(f"cos_sim output:{cos(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j]).shape}") - abs_diff.append(torch.abs(head_tensor_cpu[i][j].to('cuda') - head_tensor_gpu[i][j])) + cos_sim.append(tensor_cos_sim(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j])) + logger.debug(f"cos_sim output:{tensor_cos_sim(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j]).shape}") + abs_diff.append(tensor_abs_diff(head_tensor_cpu[i][j].to('cuda') - head_tensor_gpu[i][j])) else: tensor_cuda_out = head_tensor_gpu[i] tensor_cpu_out = head_tensor_cpu[i] logger.debug(f"inputs: {head_tensor_gpu[i].shape} {head_tensor_cpu[i].to('cuda').shape}") - cos_sim.append(cos(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i])) - logger.debug(f"cos_sim output:{cos(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i]).shape}") - abs_diff.append(torch.abs(head_tensor_cpu[i].to('cuda') - head_tensor_gpu[i])) + cos_sim.append(tensor_cos_sim(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i])) + logger.debug(f"cos_sim output:{tensor_cos_sim(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i]).shape}") + abs_diff.append(tensor_abs_diff(head_tensor_cpu[i].to('cuda') - head_tensor_gpu[i])) else: tensor_cpu_out = cpu_output.to('cuda') tensor_cuda_out = cuda_output - abs_diff = torch.abs(tensor_cpu_out - cuda_output) - cos_sim = cos(tensor_cpu_out, cuda_output) + abs_diff = tensor_abs_diff(tensor_cpu_out - cuda_output) + 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') layer_name = str(layer).replace('[','').replace(']', '') diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 0236157..f5fdb13 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -8,6 +8,8 @@ import json +from aiu_fms_testing_utils.utils.metrics_utils import abs_diff_linalg_norm, list_mean, list_avg + logger = logging.getLogger(__name__) LOG_LEVEL = os.environ.get('LOG_LEVEL', 'INFO').upper() logging.basicConfig(level=LOG_LEVEL, format="%(asctime)s %(message)s") @@ -110,9 +112,11 @@ def load_metric_file(file_path, layer_header): for key in l.keys(): tmp = {} if "abs_diff" in metric: - metric_val = np.linalg.norm(l[key]) + metric_val = abs_diff_linalg_norm(l[key]) + if "mean" in metric: + metric_val = list_mean(l[key]) else: - metric_val = np.mean(l[key]) + metric_val = list_avg(l[key]) result_dict[metric][key] = metric_val if not math.isnan(metric_val) else 0.0 logger.info(f"Layer {key} avg {metric} = {metric_val}") From 0f7f69727be4a1cbd67ee061cd50461674b6724f Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 9 Jul 2025 14:15:47 -0300 Subject: [PATCH 43/60] Small fix Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 8 ++++---- tests/LAYERS.md | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index b7f84c5..06ae3e3 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -368,7 +368,7 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): logger.debug(f"inputs: {cuda_output[i].shape} {cpu_output[i].to('cuda').shape}") cos_sim.append(tensor_cos_sim(cuda_output[i], cpu_output[i].to('cuda'))) logger.debug(f"cos_sim output:{tensor_cos_sim(cuda_output[i], cpu_output[i].to('cuda')).shape}") - abs_diff.append(tensor_abs_diff(cuda_output[i] - cpu_output[i].to('cuda'))) + abs_diff.append(tensor_abs_diff(cuda_output[i], cpu_output[i].to('cuda'))) else: head_tensor_cpu = cpu_output[-1] head_tensor_gpu = cuda_output[-1] @@ -380,18 +380,18 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): logger.debug(f"inputs: {head_tensor_gpu[i][j].shape} {head_tensor_cpu[i][j].to('cuda').shape}") cos_sim.append(tensor_cos_sim(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j])) logger.debug(f"cos_sim output:{tensor_cos_sim(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j]).shape}") - abs_diff.append(tensor_abs_diff(head_tensor_cpu[i][j].to('cuda') - head_tensor_gpu[i][j])) + abs_diff.append(tensor_abs_diff(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j])) else: tensor_cuda_out = head_tensor_gpu[i] tensor_cpu_out = head_tensor_cpu[i] logger.debug(f"inputs: {head_tensor_gpu[i].shape} {head_tensor_cpu[i].to('cuda').shape}") cos_sim.append(tensor_cos_sim(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i])) logger.debug(f"cos_sim output:{tensor_cos_sim(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i]).shape}") - abs_diff.append(tensor_abs_diff(head_tensor_cpu[i].to('cuda') - head_tensor_gpu[i])) + abs_diff.append(tensor_abs_diff(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i])) else: tensor_cpu_out = cpu_output.to('cuda') tensor_cuda_out = cuda_output - abs_diff = tensor_abs_diff(tensor_cpu_out - cuda_output) + abs_diff = tensor_abs_diff(tensor_cpu_out, cuda_output) 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') diff --git a/tests/LAYERS.md b/tests/LAYERS.md index bc7a6c9..1bbead4 100644 --- a/tests/LAYERS.md +++ b/tests/LAYERS.md @@ -81,7 +81,7 @@ cd aiu-fms-testing-utils/tests/resources mkdir /tmp/output -python3 generate_layers_metrics.py --mode model-forward --models ibm-granite/granite-20b-code-instruct-8k --batch_sizes 1 --seq_lengths 64 --max_new_tokens 128 +python3 generate_layers_metrics.py --mode model-forward --models ibm-granite/granite-3.2-8b-instruct --batch_sizes 1 --seq_lengths 64 --max_new_tokens 128 ``` The files should get created at `/tmp/output` dir: ```bash From 12d8c9e83435ea3a556352abfad25ba59fc7323b Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 9 Jul 2025 14:42:20 -0300 Subject: [PATCH 44/60] Avg and mean for cosine similarity Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index f5fdb13..0628617 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -113,12 +113,17 @@ def load_metric_file(file_path, layer_header): tmp = {} if "abs_diff" in metric: metric_val = abs_diff_linalg_norm(l[key]) - if "mean" in metric: - metric_val = list_mean(l[key]) - else: - metric_val = list_avg(l[key]) - result_dict[metric][key] = metric_val if not math.isnan(metric_val) else 0.0 - logger.info(f"Layer {key} avg {metric} = {metric_val}") + logger.info(f"Layer {key} abs_diff_linalg_norm = {metric_val}") + result_dict[metric][key] = metric_val if not math.isnan(metric_val) else 0.0 + if "cos_sim" in metric: + cos_sim_mean = list_mean(l[key]) + mean_key = f"{metric}_mean" + result_dict[mean_key][key] = cos_sim_mean if not math.isnan(cos_sim_mean) else 0.0 + logger.info(f"Layer {key} cos_sim_mean = {cos_sim_mean}") + cos_sim_avg = list_avg(l[key]) + avg_key = f"{metric}_avg" + result_dict[avg_key][key] = cos_sim_avg if not math.isnan(cos_sim_avg) else 0.0 + logger.info(f"Layer {key} cos_sim_avg = {metric_val}") json_output_path = args.output_path if args.output_path else file_base f_result_path = os.path.join(json_output_path, f"{model}-thresholds.json") From de8ee15c18cf854b8cf12399fc7e7dfaad3789f6 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 9 Jul 2025 15:00:05 -0300 Subject: [PATCH 45/60] Fix avg and mean dict Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 0628617..3e77adc 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -87,6 +87,8 @@ def load_metric_file(file_path, layer_header): for model in models: result_dict = {"model_id": model} + if "cos_sim" in metrics: + result_dict.update({"cos_sim_mean":{},"cos_sim_avg":{}}) for metric in metrics: path = os.path.join(file_base, f"{model}*{metric}*.csv") metric_files = glob.glob(path) @@ -117,13 +119,11 @@ def load_metric_file(file_path, layer_header): result_dict[metric][key] = metric_val if not math.isnan(metric_val) else 0.0 if "cos_sim" in metric: cos_sim_mean = list_mean(l[key]) - mean_key = f"{metric}_mean" - result_dict[mean_key][key] = cos_sim_mean if not math.isnan(cos_sim_mean) else 0.0 + result_dict["cos_sim_mean"][key] = cos_sim_mean if not math.isnan(cos_sim_mean) else 0.0 logger.info(f"Layer {key} cos_sim_mean = {cos_sim_mean}") cos_sim_avg = list_avg(l[key]) - avg_key = f"{metric}_avg" - result_dict[avg_key][key] = cos_sim_avg if not math.isnan(cos_sim_avg) else 0.0 - logger.info(f"Layer {key} cos_sim_avg = {metric_val}") + result_dict["cos_sim_avg"][key] = cos_sim_avg if not math.isnan(cos_sim_avg) else 0.0 + logger.info(f"Layer {key} cos_sim_avg = {cos_sim_avg}") json_output_path = args.output_path if args.output_path else file_base f_result_path = os.path.join(json_output_path, f"{model}-thresholds.json") From fbafe1d048b327bd5c9ad20f6d34e84202f32a5d Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 9 Jul 2025 15:42:43 -0300 Subject: [PATCH 46/60] Fix avg and mean dict Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 22 ++++++++++------------ 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 3e77adc..a3e80f0 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -87,8 +87,6 @@ def load_metric_file(file_path, layer_header): for model in models: result_dict = {"model_id": model} - if "cos_sim" in metrics: - result_dict.update({"cos_sim_mean":{},"cos_sim_avg":{}}) for metric in metrics: path = os.path.join(file_base, f"{model}*{metric}*.csv") metric_files = glob.glob(path) @@ -112,18 +110,18 @@ def load_metric_file(file_path, layer_header): for l in layers: for key in l.keys(): - tmp = {} if "abs_diff" in metric: - metric_val = abs_diff_linalg_norm(l[key]) + metric_val = abs_diff_linalg_norm(l[key]) if not math.isnan(abs_diff_linalg_norm(l[key])) else 0.0 logger.info(f"Layer {key} abs_diff_linalg_norm = {metric_val}") - result_dict[metric][key] = metric_val if not math.isnan(metric_val) else 0.0 - if "cos_sim" in metric: - cos_sim_mean = list_mean(l[key]) - result_dict["cos_sim_mean"][key] = cos_sim_mean if not math.isnan(cos_sim_mean) else 0.0 - logger.info(f"Layer {key} cos_sim_mean = {cos_sim_mean}") - cos_sim_avg = list_avg(l[key]) - result_dict["cos_sim_avg"][key] = cos_sim_avg if not math.isnan(cos_sim_avg) else 0.0 - logger.info(f"Layer {key} cos_sim_avg = {cos_sim_avg}") + result_dict[metric][key] = metric_val + elif "avg" in metric: + metric_avg = list_avg(l[key]) if not math.isnan(list_avg(l[key])) else 0.0 + logger.info(f"Layer {key} {metric} = {metric_avg}") + result_dict[metric][key] = metric_avg + elif "mean" in metric: + metric_mean = list_mean(l[key]) if not math.isnan(list_mean(l[key])) else 0.0 + logger.info(f"Layer {key} {metric} = {metric_mean}") + result_dict[metric][key] = metric_mean json_output_path = args.output_path if args.output_path else file_base f_result_path = os.path.join(json_output_path, f"{model}-thresholds.json") From 96ed494aa1973f5b1f43cd44e50e86d626a14f04 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 9 Jul 2025 15:56:16 -0300 Subject: [PATCH 47/60] Fix find files with cos sim Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index a3e80f0..a0a27ff 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -32,7 +32,7 @@ default=[], nargs='+', required=True, - help="List of metrics separated by space. Eg.: diff_mean ce", + help="List of metrics separated by space. Eg. for full model mode: diff_mean ce | Eg. for layers mode: abs_diff cos_sim_avg cos_sim_mean", ) parser.add_argument( "--file_base", @@ -88,7 +88,8 @@ def load_metric_file(file_path, layer_header): for model in models: result_dict = {"model_id": model} for metric in metrics: - path = os.path.join(file_base, f"{model}*{metric}*.csv") + metric_name = "_".join(metric.split("_")[:2]) if layer_mode else metric + path = os.path.join(file_base, f"{model}*{metric_name}*.csv") metric_files = glob.glob(path) result_dict[metric] = {} metric_list = [] From 5ae39a14777232762ad3bc4700e0c56978823179 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 9 Jul 2025 16:01:22 -0300 Subject: [PATCH 48/60] Fix layer names in json Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index a0a27ff..4e3f2a6 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -102,7 +102,7 @@ def load_metric_file(file_path, layer_header): layers = [] for metric_file in metric_files: layer_dict = {} - layer_name = metric_file.split("--")[-1].replace(".{}".format(metric), "") + layer_name = metric_file.split("--")[-1].replace(".{}".format(metric_name), "") layer_name = layer_name.replace(".csv","") metric_layer_list = load_metric_file(metric_file, layer_mode) layer_dict[layer_name] = metric_layer_list From b10ed9dbaa69a33665bd0896279bbf0df0d10e82 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Wed, 9 Jul 2025 16:12:50 -0300 Subject: [PATCH 49/60] Updates sample result JSON Signed-off-by: Flavia Beo --- tests/LAYERS.md | 40 +- tests/resources/sample_layer_th.json | 4372 +++++++++----------------- 2 files changed, 1480 insertions(+), 2932 deletions(-) diff --git a/tests/LAYERS.md b/tests/LAYERS.md index 1bbead4..f8732a2 100644 --- a/tests/LAYERS.md +++ b/tests/LAYERS.md @@ -97,28 +97,32 @@ To get the second step of the flow and get the thresholds by layer, run: ```bash cd /aiu-fms-testing-utils/tests/resources -python3 get_thresholds.py --models ibm-granite/granite-3.2-8b-instruct --metrics abs_diff cos_sim --file_base /tmp/output --layer_io +python3 get_thresholds.py --models ibm-granite/granite-3.2-8b-instruct --metrics abs_diff cos_sim_avg cos_sim_men --file_base /tmp/output --layer_io ``` It should print the metric of each layer: ```bash -Layer model.base_model.layers25.attn.in_proj.query avg abs_diff = 2.079996666484281 -Layer model.base_model.layers25.attn.in_proj.key avg abs_diff = 1.2256532914682756 -Layer model.base_model.layers25.attn.in_proj.value avg abs_diff = 0.8446561344670284 -Layer model.base_model.layers25.attn.in_proj avg abs_diff = 0.0 -Layer model.base_model.layers25.attn.dense avg abs_diff = 0.23142293885894077 -Layer model.base_model.layers25.ff_ln avg abs_diff = 0.9550253005897409 -Layer model.base_model.layers25.ff_sub_layer.wg avg abs_diff = 1.2256491705546648 -Layer model.base_model.layers25.ff_sub_layer.a avg abs_diff = 0.5235781749861929 -Layer model.base_model.layers25.ff_sub_layer.w1 avg abs_diff = 1.2707070667436549 -Layer model.base_model.layers25.ff_sub_layer.w2 avg abs_diff = 0.5201997339672954 -Layer model.base_model.layers25.ff_sub_layer avg abs_diff = 0.5201997339672954 -Layer model.base_model.layers26.ln avg abs_diff = 0.04852477119171675 +2025-07-09 19:02:40,657 found 484 layers metric files +2025-07-09 19:02:40,674 Layer model.base_model.embedding abs_diff_linalg_norm = 1.7258892434335918e-07 +2025-07-09 19:02:40,690 Layer model.base_model.layers0.ln abs_diff_linalg_norm = 0.4083323414747196 +2025-07-09 19:02:40,707 Layer model.base_model.layers0.attn.in_proj.query abs_diff_linalg_norm = 0.7099368339133884 +2025-07-09 19:02:40,712 Layer model.base_model.layers0.attn.in_proj.key abs_diff_linalg_norm = 0.40915828503373886 +2025-07-09 19:02:40,716 Layer model.base_model.layers0.attn.in_proj.value abs_diff_linalg_norm = 0.12381335209555287 +2025-07-09 19:02:40,721 Layer model.base_model.layers0.attn.in_proj abs_diff_linalg_norm = 0.12381335209555287 [...] -Layer model.base_model.layers39.attn.in_proj.query avg cos_sim = 0.999176025390625 -Layer model.base_model.layers39.attn.in_proj.key avg cos_sim = 0.9991455078125 -Layer model.base_model.layers39.attn.in_proj.value avg cos_sim = 0.9986572265625 -Layer model.base_model.layers39.attn.in_proj avg cos_sim = 0.0 -Layer model.base_model.layers39.attn.dense avg cos_sim = 0.9987258911132812 +2025-07-09 19:03:27,029 Layer model.base_model.layers39.attn.in_proj.value cos_sim_avg = 0.9999685110524297 +2025-07-09 19:03:27,029 Layer model.base_model.layers39.attn.in_proj cos_sim_avg = 0.9999685110524297 +2025-07-09 19:03:27,029 Layer model.base_model.layers39.attn.dense cos_sim_avg = 0.9999954961240292 +2025-07-09 19:03:27,029 Layer model.base_model.layers39.ff_ln cos_sim_avg = 1.0000354265794158 +2025-07-09 19:03:27,029 Layer model.base_model.layers39.ff_sub_layer.wg cos_sim_avg = 1.0000474276021123 +2025-07-09 19:03:27,029 Layer model.base_model.layers39.ff_sub_layer.a cos_sim_avg = 1.0000188555568457 +[...] +2025-07-09 19:03:27,055 Layer model.base_model.layers0.attn.in_proj.query cos_sim_mean = 0.9999569654464722 +2025-07-09 19:03:27,055 Layer model.base_model.layers0.attn.in_proj.key cos_sim_mean = 1.000030318275094 +2025-07-09 19:03:27,055 Layer model.base_model.layers0.attn.in_proj.value cos_sim_mean = 0.9999886471778154 +2025-07-09 19:03:27,055 Layer model.base_model.layers0.attn.in_proj cos_sim_mean = 0.9999886471778154 +2025-07-09 19:03:27,055 Layer model.base_model.layers0.attn.dense cos_sim_mean = 1.0000049602240324 +2025-07-09 19:03:27,055 Layer model.base_model.layers0.ff_ln cos_sim_mean = 0.9999961135908961 + ``` Also, a JSON file is saved to the same output dir. A sample file can be found at: [sample_layer_th.json](https://github.com/flaviabeo/aiu-fms-testing-utils/blob/generate_metrics_layers/tests/resources/sample_layer_th.json) diff --git a/tests/resources/sample_layer_th.json b/tests/resources/sample_layer_th.json index b5eb184..30777a5 100644 --- a/tests/resources/sample_layer_th.json +++ b/tests/resources/sample_layer_th.json @@ -1,2917 +1,1461 @@ { "model_id": "ibm-granite--granite-3.2-8b-instruct", - "abs_diff": [ - { - "model.base_model.embedding": 1.7258892434335918e-07 - }, - { - "model.base_model.layers0.ln": 0.4083323414747196 - }, - { - "model.base_model.layers0.attn.in_proj.query": 0.7099368339133884 - }, - { - "model.base_model.layers0.attn.in_proj.key": 0.40915828503373886 - }, - { - "model.base_model.layers0.attn.in_proj.value": 0.12381335209555287 - }, - { - "model.base_model.layers0.attn.in_proj": 0.0 - }, - { - "model.base_model.layers0.attn.dense": 0.20848351243216826 - }, - { - "model.base_model.layers0.ff_ln": 0.341461752477386 - }, - { - "model.base_model.layers0.ff_sub_layer.wg": 0.567641596277766 - }, - { - "model.base_model.layers0.ff_sub_layer.a": 0.22434598762517147 - }, - { - "model.base_model.layers0.ff_sub_layer.w1": 0.6067234563084546 - }, - { - "model.base_model.layers0.ff_sub_layer.w2": 0.26350909056410365 - }, - { - "model.base_model.layers0.ff_sub_layer": 0.26350909056410365 - }, - { - "model.base_model.layers1.ln": 0.8336723703984307 - }, - { - "model.base_model.layers1.attn.in_proj.query": 0.8916629422172441 - }, - { - "model.base_model.layers1.attn.in_proj.key": 0.9832392219817886 - }, - { - "model.base_model.layers1.attn.in_proj.value": 0.19266429901472298 - }, - { - "model.base_model.layers1.attn.in_proj": 0.0 - }, - { - "model.base_model.layers1.attn.dense": 0.22844734196163022 - }, - { - "model.base_model.layers1.ff_ln": 0.5521520840910448 - }, - { - "model.base_model.layers1.ff_sub_layer.wg": 0.7689933213289677 - }, - { - "model.base_model.layers1.ff_sub_layer.a": 0.21604948169231603 - }, - { - "model.base_model.layers1.ff_sub_layer.w1": 0.6776186104315166 - }, - { - "model.base_model.layers1.ff_sub_layer.w2": 0.28674962163968093 - }, - { - "model.base_model.layers1.ff_sub_layer": 0.28674962163968093 - }, - { - "model.base_model.layers2.ln": 0.8847133906331596 - }, - { - "model.base_model.layers2.attn.in_proj.query": 0.9420112644015102 - }, - { - "model.base_model.layers2.attn.in_proj.key": 0.97354122886582 - }, - { - "model.base_model.layers2.attn.in_proj.value": 0.22630204790296457 - }, - { - "model.base_model.layers2.attn.in_proj": 0.0 - }, - { - "model.base_model.layers2.attn.dense": 0.18332329740252973 - }, - { - "model.base_model.layers2.ff_ln": 0.525927330521208 - }, - { - "model.base_model.layers2.ff_sub_layer.wg": 0.7853817552255021 - }, - { - "model.base_model.layers2.ff_sub_layer.a": 0.2126492684120587 - }, - { - "model.base_model.layers2.ff_sub_layer.w1": 0.7100478846115829 - }, - { - "model.base_model.layers2.ff_sub_layer.w2": 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b/scripts/generate_layers_metrics.py index 06ae3e3..48bda3b 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -177,7 +177,7 @@ def __register_call_layers(model, batch_size, device, seq_length, max_new_tokens Returns: list: A list of tuples containing the name and output of each layer in the model. """ - layer_stack = [] + layer_stack = {} pt_compile_model_time = time.time() module_depth = {} @@ -228,8 +228,10 @@ def pre_hook_fn(module, input): def post_hook_fn(module, input, output): layer_name = module_name.get(module, 0) # Save inputs and outputs + tmp = {} if hasattr(module, '_debug_input'): - layer_stack.append((layer_name, output)) + tmp[layer_name] = output + layer_stack.update(tmp) # Clean up delattr(module, '_debug_input') @@ -346,71 +348,73 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): seq_length=seq_length, max_new_tokens=max_new_tokens, tokenizer=tokenizer) - assert len(layer_stack_cuda) == len(layer_stack_cpu) + assert len(layer_stack_cuda.keys()) == len(layer_stack_cpu.keys()) - for layer, cuda_output in layer_stack_cuda: + for layer_key, output_val in layer_stack_cuda.items(): tensor_cpu_out = None tensor_cuda_out = None - for cpu_layer, cpu_output in layer_stack_cpu: - if cpu_layer == layer: - logger.info("CPU Layer {} GPU Layer {}".format(cpu_layer, layer)) - - if type(cpu_output) is tuple and type(cuda_output) is tuple: - cos_sim = [] - abs_diff = [] - if len(cpu_layer) < 2 and len(cpu_layer[-1]) == 1: - # Some layers have tuple outputs, with more than 2 tensors - this path compares this type of output; - # In case of head layers, the last item of the tuple is a list of tensors with the same lenght as the - # number of layers in the model. The else path compares this other case. - tensor_cuda_out = cuda_output[-1] - tensor_cpu_out = cpu_layer[-1] - for i in range(len(cpu_layer)): - logger.debug(f"inputs: {cuda_output[i].shape} {cpu_output[i].to('cuda').shape}") - cos_sim.append(tensor_cos_sim(cuda_output[i], cpu_output[i].to('cuda'))) - logger.debug(f"cos_sim output:{tensor_cos_sim(cuda_output[i], cpu_output[i].to('cuda')).shape}") - abs_diff.append(tensor_abs_diff(cuda_output[i], cpu_output[i].to('cuda'))) - else: - head_tensor_cpu = cpu_output[-1] - head_tensor_gpu = cuda_output[-1] - for i in range(len(head_tensor_gpu)): - if isinstance(head_tensor_gpu[i], tuple): - for j in range(len(head_tensor_gpu[i])): - tensor_cuda_out = head_tensor_gpu[i][j] - tensor_cpu_out = head_tensor_cpu[i][j] - logger.debug(f"inputs: {head_tensor_gpu[i][j].shape} {head_tensor_cpu[i][j].to('cuda').shape}") - cos_sim.append(tensor_cos_sim(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j])) - logger.debug(f"cos_sim output:{tensor_cos_sim(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j]).shape}") - abs_diff.append(tensor_abs_diff(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j])) - else: - tensor_cuda_out = head_tensor_gpu[i] - tensor_cpu_out = head_tensor_cpu[i] - logger.debug(f"inputs: {head_tensor_gpu[i].shape} {head_tensor_cpu[i].to('cuda').shape}") - cos_sim.append(tensor_cos_sim(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i])) - logger.debug(f"cos_sim output:{tensor_cos_sim(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i]).shape}") - abs_diff.append(tensor_abs_diff(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i])) + + if layer_key in layer_stack_cpu.keys(): + cpu_output = layer_stack_cpu[layer_key] + cuda_output = output_val + logger.info(f"Comparing CPU and GPU Layer {layer_key} output") + + if type(cpu_output) is tuple and type(cuda_output) is tuple: + cos_sim = [] + abs_diff = [] + if len(cpu_output) < 2 and len(cpu_output[-1]) == 1: + # Some layers have tuple outputs, with more than 2 tensors - this path compares this type of output; + # In case of head layers, the last item of the tuple is a list of tensors with the same lenght as the + # number of layers in the model. The else path compares this other case. + tensor_cuda_out = cuda_output[-1] + tensor_cpu_out = cpu_output[-1] + for i in range(len(cpu_output)): + logger.debug(f"inputs: {cuda_output[i].shape} {cpu_output[i].to('cuda').shape}") + cos_sim.append(tensor_cos_sim(cuda_output[i], cpu_output[i].to('cuda'))) + logger.debug(f"cos_sim output:{tensor_cos_sim(cuda_output[i], cpu_output[i].to('cuda')).shape}") + abs_diff.append(tensor_abs_diff(cuda_output[i], cpu_output[i].to('cuda'))) else: - tensor_cpu_out = cpu_output.to('cuda') - tensor_cuda_out = cuda_output - abs_diff = tensor_abs_diff(tensor_cpu_out, cuda_output) - cos_sim = tensor_cos_sim(tensor_cpu_out, cuda_output) + head_tensor_cpu = cpu_output[-1] + head_tensor_gpu = cuda_output[-1] + for i in range(len(head_tensor_gpu)): + if isinstance(head_tensor_gpu[i], tuple): + for j in range(len(head_tensor_gpu[i])): + tensor_cuda_out = head_tensor_gpu[i][j] + tensor_cpu_out = head_tensor_cpu[i][j] + logger.debug(f"inputs: {head_tensor_gpu[i][j].shape} {head_tensor_cpu[i][j].to('cuda').shape}") + cos_sim.append(tensor_cos_sim(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j])) + logger.debug(f"cos_sim output:{tensor_cos_sim(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j]).shape}") + abs_diff.append(tensor_abs_diff(head_tensor_cpu[i][j].to('cuda'), head_tensor_gpu[i][j])) + else: + tensor_cuda_out = head_tensor_gpu[i] + tensor_cpu_out = head_tensor_cpu[i] + logger.debug(f"inputs: {head_tensor_gpu[i].shape} {head_tensor_cpu[i].to('cuda').shape}") + cos_sim.append(tensor_cos_sim(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i])) + logger.debug(f"cos_sim output:{tensor_cos_sim(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i]).shape}") + abs_diff.append(tensor_abs_diff(head_tensor_cpu[i].to('cuda'), head_tensor_gpu[i])) + else: + tensor_cpu_out = cpu_output.to('cuda') + tensor_cuda_out = cuda_output + abs_diff = tensor_abs_diff(tensor_cpu_out, cuda_output) + 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') - layer_name = str(layer).replace('[','').replace(']', '') + prefix = get_default_validation_prefix(model_path, max_new_token, batch_size, seq_length, 'float16') + layer_name = str(layer_key).replace('[','').replace(']', '') - abs_diff_path = os.path.join(output_path, f"{prefix}--{layer_name}.abs_diff.csv") - cos_sim_path = os.path.join(output_path, f"{prefix}--{layer_name}.cos_sim.csv") + abs_diff_path = os.path.join(output_path, f"{prefix}--{layer_name}.abs_diff.csv") + cos_sim_path = os.path.join(output_path, f"{prefix}--{layer_name}.cos_sim.csv") - cos_sim_res, cos_shape = get_metric_values(cos_sim) - abs_diff_res, abs_diff_shape = get_metric_values(abs_diff) + cos_sim_res, cos_shape = get_metric_values(cos_sim) + abs_diff_res, abs_diff_shape = get_metric_values(abs_diff) - if not os.path.exists(abs_diff_path): - logger.debug("saving abs_diff files") - write_csv(abs_diff_res, abs_diff_path, "abs_diff", tensor_cuda_out.shape, tensor_cpu_out.shape, abs_diff_shape) - if not os.path.exists(cos_sim_path): - logger.debug("saving cos_sim files") - write_csv(cos_sim_res, cos_sim_path, "cos_sim", tensor_cuda_out.shape, tensor_cpu_out.shape, cos_shape) + if not os.path.exists(abs_diff_path): + logger.debug("saving abs_diff files") + write_csv(abs_diff_res, abs_diff_path, "abs_diff", tensor_cuda_out.shape, tensor_cpu_out.shape, abs_diff_shape) + if not os.path.exists(cos_sim_path): + logger.debug("saving cos_sim files") + write_csv(cos_sim_res, cos_sim_path, "cos_sim", tensor_cuda_out.shape, tensor_cpu_out.shape, cos_shape) - logger.info(f"Completed {model_path} layers' metrics generation") + logger.info(f"Completed {model_path} layers' metrics generation with {mode} mode") for model_id, batch_size, sequence_length, max_new_token in common_shapes: logger.info(f"testing model_id-{model_id}, max_new_tokens-{max_new_token}, batch_size-{batch_size}, seq_length-{sequence_length}") From a6f84bc9a7dda2c30abbcdf0d1d77269b418879f Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 10 Jul 2025 09:34:53 -0300 Subject: [PATCH 51/60] Adds zero values handling Signed-off-by: Flavia Beo --- aiu_fms_testing_utils/utils/metrics_utils.py | 19 ++++++------------- scripts/generate_layers_metrics.py | 3 ++- tests/resources/get_thresholds.py | 7 +++---- 3 files changed, 11 insertions(+), 18 deletions(-) diff --git a/aiu_fms_testing_utils/utils/metrics_utils.py b/aiu_fms_testing_utils/utils/metrics_utils.py index 4e4f546..334721e 100644 --- a/aiu_fms_testing_utils/utils/metrics_utils.py +++ b/aiu_fms_testing_utils/utils/metrics_utils.py @@ -27,17 +27,6 @@ def list_mean(val_list): """ return np.mean(val_list) -def list_avg(val_list): - """ - Calculates the average for all the values in a given list. - Args: - val_list (list): The list of values - - Returns: - float: average value calculated. - """ - return np.average(val_list) - def tensor_abs_diff(tensor1, tensor2): """ Calculate the absolute difference between two tensors. @@ -55,7 +44,9 @@ def tensor_abs_diff(tensor1, tensor2): >>> abs_diff(tensor1, tensor2) torch.tensor([3, 3, 3]) """ - return torch.abs(tensor1 - tensor2) + abs_diff = torch.abs(tensor1 - tensor2) + abs_diff[abs_diff == 0.0] = 1e-6 + return abs_diff def tensor_cos_sim(tensor1, tensor2): """ @@ -76,4 +67,6 @@ def tensor_cos_sim(tensor1, tensor2): >>> print(sim) """ cos = nn.CosineSimilarity(dim=-1) - return cos(tensor1, tensor2) \ No newline at end of file + cos_sim = cos(tensor1, tensor2) + cos_sim[cos_sim == 0.0] = 1e-6 + return cos_sim \ No newline at end of file diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 48bda3b..4355497 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -363,7 +363,8 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): cos_sim = [] abs_diff = [] if len(cpu_output) < 2 and len(cpu_output[-1]) == 1: - # Some layers have tuple outputs, with more than 2 tensors - this path compares this type of output; + # Projection layers (often called "query," "key," and "value" projections) are used to transform the input embeddings + # into separate query, key, and value vectors. They have tuple outputs, with more than 2 tensors - this path compares this type of output; # In case of head layers, the last item of the tuple is a list of tensors with the same lenght as the # number of layers in the model. The else path compares this other case. tensor_cuda_out = cuda_output[-1] diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 4e3f2a6..2c8ce66 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -2,7 +2,6 @@ import numpy as np import argparse import os -import math import logging @@ -112,15 +111,15 @@ def load_metric_file(file_path, layer_header): for l in layers: for key in l.keys(): if "abs_diff" in metric: - metric_val = abs_diff_linalg_norm(l[key]) if not math.isnan(abs_diff_linalg_norm(l[key])) else 0.0 + metric_val = abs_diff_linalg_norm(l[key]) logger.info(f"Layer {key} abs_diff_linalg_norm = {metric_val}") result_dict[metric][key] = metric_val elif "avg" in metric: - metric_avg = list_avg(l[key]) if not math.isnan(list_avg(l[key])) else 0.0 + metric_avg = np.average(l[key]) logger.info(f"Layer {key} {metric} = {metric_avg}") result_dict[metric][key] = metric_avg elif "mean" in metric: - metric_mean = list_mean(l[key]) if not math.isnan(list_mean(l[key])) else 0.0 + metric_mean = list_mean(l[key]) logger.info(f"Layer {key} {metric} = {metric_mean}") result_dict[metric][key] = metric_mean From 8b5a64f3815ff0807364c2f9582266a5a091daf0 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 10 Jul 2025 10:01:20 -0300 Subject: [PATCH 52/60] Fix infer method docstring Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 4355497..b204793 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -118,7 +118,7 @@ def __infer_layer(model, max_len, device, max_new_tokens, batch_size, tokenizer) tokenizer (Tokenizer): The tokenizer to use for encoding inputs. Returns: - torch.Tensor: The inferred layer output. + torch.Tensor: The inferred model's layers output. """ do_sample = False From b6bf42dda989ac267684760f673a4bd113e0d48d Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 10 Jul 2025 14:48:57 -0300 Subject: [PATCH 53/60] Adds model path and saves all generate iteractions Signed-off-by: Flavia Beo --- aiu_fms_testing_utils/utils/__init__.py | 1 - scripts/generate_layers_metrics.py | 37 +++++++++++++++++++------ tests/LAYERS.md | 28 +++++++++---------- 3 files changed, 41 insertions(+), 25 deletions(-) diff --git a/aiu_fms_testing_utils/utils/__init__.py b/aiu_fms_testing_utils/utils/__init__.py index 79dcbda..cdc106f 100644 --- a/aiu_fms_testing_utils/utils/__init__.py +++ b/aiu_fms_testing_utils/utils/__init__.py @@ -177,7 +177,6 @@ def prepare_inputs(batch_size, seq_length, tokenizer, sharegpt_path, seed=0): seq_length, seed, ) - ## TODO: for each prompt prompt_list = [] for prompt, _ in prompts_and_sizes: prompt_list.append(ids_for_prompt(prompt, tokenizer)) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index b204793..2154acf 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -24,14 +24,21 @@ parser = argparse.ArgumentParser( description="Script to generate the model's metrics by layer" ) - parser.add_argument( - "--models", + "--architecture", type=str, - default=[], - nargs='+', - required=True, - help="List of models id separated by space. Eg.: ibm-granite/granite-3.3-8b-instruct mistralai/Mistral-7B-Instruct-v0.3" + help="The model architecture Eg.: hf_pretrained", +) +parser.add_argument( + "--variant", + type=str, + default=None, + help="The model variants (configuration) to benchmark. E.g. ibm-granite/granite-3.2-8b-instruct", +) +parser.add_argument( + "--model_path", + type=str, + help="Paths to the directory containing model's weights (.pth files sharded by tensor parallel rank, not HF weights)", ) parser.add_argument( "--mode", @@ -79,7 +86,9 @@ output_path = args.output_path sharegpt_path = args.sharegpt_path -common_model_paths = [model for model in args.models] +common_model_paths = args.model_path if args.model_path else args.variant +if isinstance(common_model_paths, str): + common_model_paths = [str(bs) for bs in common_model_paths.split(",")] # pass custom common batch sizes as a comma separated str of ints common_batch_sizes = args.batch_sizes @@ -105,6 +114,8 @@ ) ) +generate_iters = 0 + def __infer_layer(model, max_len, device, max_new_tokens, batch_size, tokenizer): """ Infer a model with registered layer hooks using generated inputs. @@ -230,6 +241,9 @@ def post_hook_fn(module, input, output): # Save inputs and outputs tmp = {} if hasattr(module, '_debug_input'): + global generate_iters + generate_iters += 1 + layer_name = f"{layer_name}{generate_iters}" if layer_name in layer_stack.keys() else layer_name tmp[layer_name] = output layer_stack.update(tmp) # Clean up @@ -310,8 +324,8 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): if "HF_HOME" not in os.environ: os.environ["HF_HOME"] = "/tmp/models/hf_cache" - model_path_kwargs = {"variant": model_path} - micro_model_kwargs = {"architecture": "hf_pretrained"} + model_path_kwargs = {"variant": model_path} if args.variant else {"model_path": model_path} + micro_model_kwargs = {"architecture": args.architecture} get_model_kwargs = { **model_path_kwargs, @@ -342,6 +356,10 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): seq_length=seq_length, max_new_tokens=max_new_tokens, tokenizer=tokenizer) + global generate_iters + generate_iters = 0 + logger.info(f"Finished registering CPU layers") + layer_stack_cuda = __register_call_layers(model=validation_model_cuda, batch_size=batch_size, device="cuda", @@ -351,6 +369,7 @@ def generate_layers_metrics(model_path, batch_size, seq_length, max_new_tokens): assert len(layer_stack_cuda.keys()) == len(layer_stack_cpu.keys()) for layer_key, output_val in layer_stack_cuda.items(): + tensor_cpu_out = None tensor_cuda_out = None diff --git a/tests/LAYERS.md b/tests/LAYERS.md index f8732a2..ae1ccde 100644 --- a/tests/LAYERS.md +++ b/tests/LAYERS.md @@ -19,27 +19,25 @@ The idea is to run, the prompts through the model with the pre- and post-hooks a The script [generate_layers_metrics.py](../scripts/generate_layers_metrics.py) requires the following arguments to be run: ```bash -usage: generate_layers_metrics.py [-h] --models MODELS [MODELS ...] --mode - {generate,model-forward} --batch_sizes BATCH_SIZES - [BATCH_SIZES ...] --seq_lengths SEQ_LENGTHS [SEQ_LENGTHS ...] - --max_new_tokens MAX_NEW_TOKENS [MAX_NEW_TOKENS ...] - [--output_path OUTPUT_PATH] [--sharegpt_path SHAREGPT_PATH] +usage: generate_layers_metrics.py [-h] [--architecture ARCHITECTURE] [--variant VARIANT] [--model_path MODEL_PATH] --mode {generate,model-forward} --batch_sizes BATCH_SIZES --seq_lengths SEQ_LENGTHS --max_new_tokens MAX_NEW_TOKENS [--output_path OUTPUT_PATH] [--sharegpt_path SHAREGPT_PATH] Script to generate the model's metrics by layer options: -h, --help show this help message and exit - --models MODELS [MODELS ...] - List of models id separated by space. Eg.: ibm-granite/granite-20b-code- - instruct-8k /tmp/models/granite-20b-code-cobol-v1 + --architecture ARCHITECTURE + The model architecture Eg.: hf_pretrained + --variant VARIANT The model variants (configuration) to benchmark. E.g. ibm-granite/granite-3.2-8b-instruct + --model_path MODEL_PATH + Paths to the directory containing model's weights (.pth files sharded by tensor parallel rank, not HF weights) --mode {generate,model-forward} Sets the output generation mode. - --batch_sizes BATCH_SIZES [BATCH_SIZES ...] - Batch sizes separated by comma. - --seq_lengths SEQ_LENGTHS [SEQ_LENGTHS ...] - Sequence lengths separated by comma. - --max_new_tokens MAX_NEW_TOKENS [MAX_NEW_TOKENS ...] - Max number of generated tokens separated by comma. + --batch_sizes BATCH_SIZES + Batch sizes separated by comma. Eg.: 1,2 + --seq_lengths SEQ_LENGTHS + Sequence lengths separated by comma. Eg.: 64,2048 + --max_new_tokens MAX_NEW_TOKENS + Max number of generated tokens separated by comma. Eg.: 64,128 --output_path OUTPUT_PATH Path to save output files --sharegpt_path SHAREGPT_PATH @@ -81,7 +79,7 @@ cd aiu-fms-testing-utils/tests/resources mkdir /tmp/output -python3 generate_layers_metrics.py --mode model-forward --models ibm-granite/granite-3.2-8b-instruct --batch_sizes 1 --seq_lengths 64 --max_new_tokens 128 +python3 generate_layers_metrics.py --mode model-forward --variant ibm-granite/granite-3.2-8b-instruct --architecture hf_pretrained --batch_sizes 1 --seq_lengths 64 --max_new_tokens 128 ``` The files should get created at `/tmp/output` dir: ```bash From cc42d7d71ef0fd8b7d3b0347e79f8b7bc3f99b3a Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 10 Jul 2025 15:59:28 -0300 Subject: [PATCH 54/60] Save iters and read by layers Signed-off-by: Flavia Beo --- scripts/generate_layers_metrics.py | 2 +- tests/resources/get_thresholds.py | 17 ++++++++++++----- 2 files changed, 13 insertions(+), 6 deletions(-) diff --git a/scripts/generate_layers_metrics.py b/scripts/generate_layers_metrics.py index 2154acf..48d41f6 100644 --- a/scripts/generate_layers_metrics.py +++ b/scripts/generate_layers_metrics.py @@ -243,7 +243,7 @@ def post_hook_fn(module, input, output): if hasattr(module, '_debug_input'): global generate_iters generate_iters += 1 - layer_name = f"{layer_name}{generate_iters}" if layer_name in layer_stack.keys() else layer_name + layer_name = f"{layer_name}.iter-{generate_iters}" if layer_name in layer_stack.keys() else layer_name tmp[layer_name] = output layer_stack.update(tmp) # Clean up diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 2c8ce66..bf0a694 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -2,6 +2,7 @@ import numpy as np import argparse import os +import re import logging @@ -58,18 +59,19 @@ file_base = args.file_base layer_mode = args.file_base if args.file_base else False -def load_metric_file(file_path, layer_header): + +def load_metric_file(file_path, layer_header, values): """ Loads a metric file and returns its values as a list of floats. Args: file_path (str): The path to the metric file. layer_header (bool): Whether to skip the first three lines of the file. Default is False. + values (list): Metrics values list Returns: list[float]: A list of metric values read from the file. """ - values = [] try: with open(file_path, "r") as file: if layer_header: @@ -91,19 +93,24 @@ def load_metric_file(file_path, layer_header): path = os.path.join(file_base, f"{model}*{metric_name}*.csv") metric_files = glob.glob(path) result_dict[metric] = {} - metric_list = [] if not layer_mode: + metric_list = [] for metric_file in metric_files: - metric_list = load_metric_file(metric_file, layer_mode) + metric_list = load_metric_file(metric_file, layer_mode, metric_list) logger.info(f"found {len(metric_files)} metric files") logger.info(model, metric, np.percentile(metric_list, 99.0)) else: layers = [] for metric_file in metric_files: layer_dict = {} + metric_layer_list = [] layer_name = metric_file.split("--")[-1].replace(".{}".format(metric_name), "") layer_name = layer_name.replace(".csv","") - metric_layer_list = load_metric_file(metric_file, layer_mode) + layer_name = re.sub(r"\.(iter-)([0-9]+)", "", layer_name) + path = os.path.join(file_base, f"{model}*{layer_name}*{metric_name}*.csv") + metric_layer_files = glob.glob(path) + for file_path in metric_layer_files: + metric_layer_list = load_metric_file(file_path, layer_mode, metric_layer_list) layer_dict[layer_name] = metric_layer_list layers.append(layer_dict) logger.info(f"found {len(layers)} layers metric files") From 40e59248c1173f98ce3a3073115e9ef37596983b Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 10 Jul 2025 16:18:33 -0300 Subject: [PATCH 55/60] Removes unused import Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index bf0a694..062e4b0 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -8,7 +8,7 @@ import json -from aiu_fms_testing_utils.utils.metrics_utils import abs_diff_linalg_norm, list_mean, list_avg +from aiu_fms_testing_utils.utils.metrics_utils import abs_diff_linalg_norm, list_mean logger = logging.getLogger(__name__) LOG_LEVEL = os.environ.get('LOG_LEVEL', 'INFO').upper() From 12946a0534a6bee34fcd2beb29085d4a26ca6611 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 10 Jul 2025 17:06:33 -0300 Subject: [PATCH 56/60] Changes metric list to all generate iters Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 062e4b0..0abcd42 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -58,6 +58,7 @@ metrics = [metric for metric in args.metrics] file_base = args.file_base layer_mode = args.file_base if args.file_base else False +generate_mode_pattern = r"\.(iter-)([0-9]+)" def load_metric_file(file_path, layer_header, values): @@ -106,12 +107,12 @@ def load_metric_file(file_path, layer_header, values): metric_layer_list = [] layer_name = metric_file.split("--")[-1].replace(".{}".format(metric_name), "") layer_name = layer_name.replace(".csv","") - layer_name = re.sub(r"\.(iter-)([0-9]+)", "", layer_name) - path = os.path.join(file_base, f"{model}*{layer_name}*{metric_name}*.csv") - metric_layer_files = glob.glob(path) - for file_path in metric_layer_files: - metric_layer_list = load_metric_file(file_path, layer_mode, metric_layer_list) - layer_dict[layer_name] = metric_layer_list + metric_layer_list = load_metric_file(metric_file, layer_mode, metric_layer_list) + if re.search(generate_mode_pattern, layer_name): + layer_name = re.sub(generate_mode_pattern, "", layer_name) + layer_dict[layer_name].extend(metric_layer_list) + else: + layer_dict[layer_name] = metric_layer_list layers.append(layer_dict) logger.info(f"found {len(layers)} layers metric files") From 526c6d56f4a604e5577ecfde1e6838356aba117d Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 10 Jul 2025 18:40:43 -0300 Subject: [PATCH 57/60] Improves layers th data structure Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 38 ++++++++++++++++--------------- 1 file changed, 20 insertions(+), 18 deletions(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index 0abcd42..a9f15bd 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -101,7 +101,7 @@ def load_metric_file(file_path, layer_header, values): logger.info(f"found {len(metric_files)} metric files") logger.info(model, metric, np.percentile(metric_list, 99.0)) else: - layers = [] + layers = {} for metric_file in metric_files: layer_dict = {} metric_layer_list = [] @@ -110,26 +110,28 @@ def load_metric_file(file_path, layer_header, values): metric_layer_list = load_metric_file(metric_file, layer_mode, metric_layer_list) if re.search(generate_mode_pattern, layer_name): layer_name = re.sub(generate_mode_pattern, "", layer_name) - layer_dict[layer_name].extend(metric_layer_list) + layers[layer_name].extend(metric_layer_list) + logger.debug(f"Output layer with generate mode {layer_name}") else: layer_dict[layer_name] = metric_layer_list - layers.append(layer_dict) - logger.info(f"found {len(layers)} layers metric files") + logger.debug(f"Output layer {layer_name}") + layers.update(layer_dict) + logger.info(f"found {len(metric_files)} layers metric files") - for l in layers: - for key in l.keys(): - if "abs_diff" in metric: - metric_val = abs_diff_linalg_norm(l[key]) - logger.info(f"Layer {key} abs_diff_linalg_norm = {metric_val}") - result_dict[metric][key] = metric_val - elif "avg" in metric: - metric_avg = np.average(l[key]) - logger.info(f"Layer {key} {metric} = {metric_avg}") - result_dict[metric][key] = metric_avg - elif "mean" in metric: - metric_mean = list_mean(l[key]) - logger.info(f"Layer {key} {metric} = {metric_mean}") - result_dict[metric][key] = metric_mean + for key, l in layers.items(): + l = np.nan_to_num(l, nan=0.0) + if "abs_diff" in metric: + metric_val = abs_diff_linalg_norm(l) + logger.info(f"Layer {key} abs_diff_linalg_norm = {metric_val}") + result_dict[metric][key] = metric_val + elif "avg" in metric: + metric_avg = np.average(l) + logger.info(f"Layer {key} {metric} = {metric_avg}") + result_dict[metric][key] = metric_avg + elif "mean" in metric: + metric_mean = list_mean(l) + logger.info(f"Layer {key} {metric} = {metric_mean}") + result_dict[metric][key] = metric_mean json_output_path = args.output_path if args.output_path else file_base f_result_path = os.path.join(json_output_path, f"{model}-thresholds.json") From 28c44f86c4bd690c8b907af1b2df46a0069e583d Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Thu, 10 Jul 2025 22:11:55 -0300 Subject: [PATCH 58/60] Fix th json Signed-off-by: Flavia Beo --- tests/resources/get_thresholds.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tests/resources/get_thresholds.py b/tests/resources/get_thresholds.py index a9f15bd..3e82024 100644 --- a/tests/resources/get_thresholds.py +++ b/tests/resources/get_thresholds.py @@ -110,7 +110,10 @@ def load_metric_file(file_path, layer_header, values): metric_layer_list = load_metric_file(metric_file, layer_mode, metric_layer_list) if re.search(generate_mode_pattern, layer_name): layer_name = re.sub(generate_mode_pattern, "", layer_name) - layers[layer_name].extend(metric_layer_list) + if not layer_name in layers.keys(): + layers[layer_name] = metric_layer_list + else: + layers[layer_name].extend(metric_layer_list) logger.debug(f"Output layer with generate mode {layer_name}") else: layer_dict[layer_name] = metric_layer_list From d2e3d98d5e321b8967d65a5a90dd1a4216c48bd5 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Fri, 11 Jul 2025 13:44:50 -0300 Subject: [PATCH 59/60] Add configurable sample requests to prepare inputs Signed-off-by: Flavia Beo --- aiu_fms_testing_utils/utils/__init__.py | 42 +++++++++++++++++++------ 1 file changed, 33 insertions(+), 9 deletions(-) diff --git a/aiu_fms_testing_utils/utils/__init__.py b/aiu_fms_testing_utils/utils/__init__.py index cdc106f..3b3473f 100644 --- a/aiu_fms_testing_utils/utils/__init__.py +++ b/aiu_fms_testing_utils/utils/__init__.py @@ -168,15 +168,39 @@ def sample_squad_v2_qa_requests( seed, ) -def prepare_inputs(batch_size, seq_length, tokenizer, sharegpt_path, seed=0): - prompts_and_sizes = sample_sharegpt_requests( - sharegpt_path, - batch_size, - tokenizer, - int(seq_length / 2), - seq_length, - seed, - ) +def prepare_inputs(batch_size, seq_length, tokenizer, ds_path, seed=0, ds_type="sharegpt"): + """ + Prepare input IDs and padding kwargs for a batch of questions. + + Args: + batch_size (int): The number of questions in the batch. + seq_length (int): The maximum length of the input sequence. + tokenizer (Tokenizer): A tokenizer object to tokenize the questions. + ds_path (str): The path to the dataset file. + seed (int, optional): The random seed for reproducibility. Defaults to 0. + ds_type (str, optional): The type of dataset to use. Can be "sharegpt" or any other supported dataset type. Defaults to "sharegpt". + + Returns: + tuple: A tuple containing the input IDs and padding kwargs. + """ + if not "sharegpt" in ds_type: + prompts_and_sizes = sample_squad_v2_qa_requests( + ds_path, + batch_size, + tokenizer, + int(seq_length / 2), + seq_length, + seed, + ) + else: + prompts_and_sizes = sample_sharegpt_requests( + ds_path, + batch_size, + tokenizer, + int(seq_length / 2), + seq_length, + seed, + ) prompt_list = [] for prompt, _ in prompts_and_sizes: prompt_list.append(ids_for_prompt(prompt, tokenizer)) From acde4fd177ca4f268d6f6389a06a166be20ddd32 Mon Sep 17 00:00:00 2001 From: Flavia Beo Date: Mon, 14 Jul 2025 09:18:24 -0300 Subject: [PATCH 60/60] Changes 0 values to small values (avoid nan) Signed-off-by: Flavia Beo --- aiu_fms_testing_utils/utils/metrics_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/aiu_fms_testing_utils/utils/metrics_utils.py b/aiu_fms_testing_utils/utils/metrics_utils.py index 334721e..9f01178 100644 --- a/aiu_fms_testing_utils/utils/metrics_utils.py +++ b/aiu_fms_testing_utils/utils/metrics_utils.py @@ -45,7 +45,6 @@ def tensor_abs_diff(tensor1, tensor2): torch.tensor([3, 3, 3]) """ abs_diff = torch.abs(tensor1 - tensor2) - abs_diff[abs_diff == 0.0] = 1e-6 return abs_diff def tensor_cos_sim(tensor1, tensor2): @@ -67,6 +66,7 @@ def tensor_cos_sim(tensor1, tensor2): >>> print(sim) """ cos = nn.CosineSimilarity(dim=-1) + tensor1[tensor1 == 0.0] = 1e-6 + tensor2[tensor2 == 0.0] = 1e-6 cos_sim = cos(tensor1, tensor2) - cos_sim[cos_sim == 0.0] = 1e-6 return cos_sim \ No newline at end of file

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