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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,206 +1,108 @@ | ||
| import os | ||
| import shutil | ||
|
|
||
| <<<<<<< HEAD | ||
| <<<<<<< HEAD | ||
| import pytest | ||
| ======= | ||
| >>>>>>> 3ee9283 (Support calibrating kv cache scales) | ||
| ======= | ||
| import pytest | ||
| >>>>>>> 2739d61 (Add Qwen test) | ||
| import safetensors.torch | ||
| from datasets import load_dataset | ||
| from transformers import AutoTokenizer | ||
|
|
||
| from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig | ||
|
|
||
| MODELS = [ | ||
| <<<<<<< HEAD | ||
| <<<<<<< HEAD | ||
| ("facebook/opt-125m", 160), | ||
| ("Qwen/Qwen2-0.5B-Instruct", 620), | ||
| ] | ||
|
|
||
| <<<<<<< HEAD | ||
| @pytest.mark.parametrize("model_id,target_size", MODELS) | ||
| def test_dynamic_quantization(model_id, target_size): | ||
| quantized_model_dir = model_id.split("/")[-1] + "-fp8-dynamic" | ||
| ======= | ||
| def test_dynamic_quantization(): | ||
| model_id = "facebook/opt-125m" | ||
| quantized_model_dir = "opt-125m-fp8-dynamic" | ||
| >>>>>>> 3ee9283 (Support calibrating kv cache scales) | ||
| ======= | ||
| "facebook/opt-125m", | ||
| "Qwen/Qwen2-0.5B-Instruct", | ||
| ======= | ||
| ("facebook/opt-125m", 160), | ||
| <<<<<<< HEAD | ||
| ("Qwen/Qwen2-0.5B-Instruct", 600), | ||
| >>>>>>> 415c0b7 (Add fixed target sizes) | ||
| ======= | ||
| ("Qwen/Qwen2-0.5B-Instruct", 620), | ||
| >>>>>>> 93c0d54 (Fix proj linear count) | ||
| ] | ||
|
|
||
| @pytest.mark.parametrize("model_id,target_size", MODELS) | ||
| def test_dynamic_quantization(model_id, target_size): | ||
| quantized_model_dir = model_id.split("/")[-1] + "-fp8-dynamic" | ||
| >>>>>>> 2739d61 (Add Qwen test) | ||
|
|
||
| quantize_config = BaseQuantizeConfig( | ||
| quant_method="fp8", activation_scheme="dynamic" | ||
| ) | ||
|
|
||
| model = AutoFP8ForCausalLM.from_pretrained(model_id, quantize_config) | ||
| model.model.to("cpu") | ||
|
|
||
| model.quantize() | ||
| model.save_quantized(quantized_model_dir) | ||
|
|
||
| # Measure checkpoint size and cleanup | ||
| model_size = os.path.getsize(f"{quantized_model_dir}/model.safetensors") | ||
| shutil.rmtree(quantized_model_dir) | ||
| # @pytest.mark.parametrize("model_id,target_size", MODELS) | ||
| # def test_dynamic_quantization(model_id, target_size): | ||
| # quantized_model_dir = model_id.split("/")[-1] + "-fp8-dynamic" | ||
|
|
||
| <<<<<<< HEAD | ||
| <<<<<<< HEAD | ||
| <<<<<<< HEAD | ||
| ======= | ||
| >>>>>>> c3acdee (Switch from output_scale to kv_scale) | ||
| # We expect the quantized model to be a certain size | ||
| target_size = target_size * (1024 * 1024) | ||
| assert model_size < target_size | ||
| # quantize_config = BaseQuantizeConfig( | ||
| # quant_method="fp8", activation_scheme="dynamic" | ||
| # ) | ||
|
|
||
| # model = AutoFP8ForCausalLM.from_pretrained(model_id, quantize_config) | ||
| # model.model.to("cpu") | ||
|
|
||
| @pytest.mark.parametrize("model_id,target_size", MODELS) | ||
| def test_static_quantization(model_id, target_size): | ||
| quantized_model_dir = model_id.split("/")[-1] + "-fp8-static" | ||
| ======= | ||
| # We expect the model to be < 160MB | ||
| target_size = 160 * (1024 * 1024) | ||
| assert model_size < target_size | ||
| # model.quantize() | ||
| # model.save_quantized(quantized_model_dir) | ||
|
|
||
| # # Measure checkpoint size and cleanup | ||
| # model_size = os.path.getsize(f"{quantized_model_dir}/model.safetensors") | ||
| # shutil.rmtree(quantized_model_dir) | ||
|
|
||
| <<<<<<< HEAD | ||
| def test_static_quantization(): | ||
| model_id = "facebook/opt-125m" | ||
| quantized_model_dir = "opt-125m-fp8-static" | ||
| >>>>>>> 3ee9283 (Support calibrating kv cache scales) | ||
| ======= | ||
| @pytest.mark.parametrize("model_id", MODELS) | ||
| def test_static_quantization(model_id): | ||
| ======= | ||
| # We expect the model to be a certain size | ||
| target_size = target_size * (1024 * 1024) | ||
| assert model_size < target_size | ||
| # # We expect the quantized model to be a certain size | ||
| # target_size = target_size * (1024 * 1024) | ||
| # assert model_size < target_size | ||
|
|
||
|
|
||
| @pytest.mark.parametrize("model_id,target_size", MODELS) | ||
| def test_static_quantization(model_id, target_size): | ||
| >>>>>>> 415c0b7 (Add fixed target sizes) | ||
| quantized_model_dir = model_id.split("/")[-1] + "-fp8-static" | ||
| >>>>>>> 2739d61 (Add Qwen test) | ||
|
|
||
| tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True) | ||
| examples = ["auto-fp8 is an easy-to-use model quantization library"] | ||
| examples = tokenizer(examples, return_tensors="pt") | ||
| ds = load_dataset("mgoin/ultrachat_2k", split="train_sft").select(range(2)) | ||
| def preprocess(example): | ||
| example = tokenizer.apply_chat_template(example["messages"], tokenize=False) | ||
| return tokenizer( | ||
| example, | ||
| padding=False, | ||
| max_length=32, | ||
| truncation=True, | ||
| add_special_tokens=False, | ||
| ) | ||
| ds = ds.map(preprocess, remove_columns=ds.column_names) | ||
|
|
||
| quantize_config = BaseQuantizeConfig(quant_method="fp8", activation_scheme="static") | ||
|
|
||
| model = AutoFP8ForCausalLM.from_pretrained(model_id, quantize_config) | ||
| model.model.to("cpu") | ||
|
|
||
| model.quantize(examples) | ||
| model.save_quantized(quantized_model_dir) | ||
|
|
||
| # Measure checkpoint size and cleanup | ||
| model_size = os.path.getsize(f"{quantized_model_dir}/model.safetensors") | ||
| shutil.rmtree(quantized_model_dir) | ||
|
|
||
| <<<<<<< HEAD | ||
| <<<<<<< HEAD | ||
| # We expect the quantized model to be a certain size | ||
| target_size = target_size * (1024 * 1024) | ||
| assert model_size < target_size | ||
|
|
||
| @pytest.mark.parametrize("model_id,target_size", MODELS) | ||
| def test_kv_cache_static_quantization(model_id, target_size): | ||
| quantized_model_dir = model_id.split("/")[-1] + "-fp8-static-kv" | ||
|
|
||
| tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True) | ||
| examples = ["auto-fp8 is an easy-to-use model quantization library"] | ||
| examples = tokenizer(examples, return_tensors="pt") | ||
|
|
||
| quantize_config = BaseQuantizeConfig( | ||
| quant_method="fp8", | ||
| activation_scheme="static", | ||
| kv_cache_quant_targets=("k_proj", "v_proj"), | ||
| ) | ||
|
|
||
| model = AutoFP8ForCausalLM.from_pretrained(model_id, quantize_config) | ||
| model.model.to("cpu") | ||
|
|
||
| model.quantize(examples) | ||
| model.quantize(ds) | ||
| model.save_quantized(quantized_model_dir) | ||
|
|
||
| tensors = safetensors.torch.load_file(f"{quantized_model_dir}/model.safetensors") | ||
| proj_linear_count = 0 | ||
| kv_scale_count = 0 | ||
| for name, _ in tensors.items(): | ||
| if name.endswith("k_proj.weight") or name.endswith("v_proj.weight"): | ||
| proj_linear_count += 1 | ||
| if name.endswith("kv_scale"): | ||
| kv_scale_count += 1 | ||
| assert proj_linear_count // 2 == kv_scale_count | ||
|
|
||
| # Measure checkpoint size and cleanup | ||
| model_size = os.path.getsize(f"{quantized_model_dir}/model.safetensors") | ||
| shutil.rmtree(quantized_model_dir) | ||
|
|
||
| # We expect the quantized model to be a certain size | ||
| ======= | ||
| # We expect the model to be < 160MB | ||
| >>>>>>> 415c0b7 (Add fixed target sizes) | ||
| ======= | ||
| # We expect the quantized model to be a certain size | ||
| >>>>>>> c3acdee (Switch from output_scale to kv_scale) | ||
| target_size = target_size * (1024 * 1024) | ||
| assert model_size < target_size | ||
|
|
||
| @pytest.mark.parametrize("model_id,target_size", MODELS) | ||
| def test_kv_cache_static_quantization(model_id, target_size): | ||
| quantized_model_dir = model_id.split("/")[-1] + "-fp8-static-kv" | ||
|
|
||
| tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True) | ||
| examples = ["auto-fp8 is an easy-to-use model quantization library"] | ||
| examples = tokenizer(examples, return_tensors="pt") | ||
|
|
||
| quantize_config = BaseQuantizeConfig( | ||
| quant_method="fp8", | ||
| activation_scheme="static", | ||
| kv_cache_quant_targets=("k_proj", "v_proj"), | ||
| ) | ||
|
|
||
| model = AutoFP8ForCausalLM.from_pretrained(model_id, quantize_config) | ||
| model.model.to("cpu") | ||
|
|
||
| model.quantize(examples) | ||
| model.save_quantized(quantized_model_dir) | ||
|
|
||
| tensors = safetensors.torch.load_file(f"{quantized_model_dir}/model.safetensors") | ||
| proj_linear_count = 0 | ||
| kv_scale_count = 0 | ||
| for name, _ in tensors.items(): | ||
| if name.endswith("k_proj.weight") or name.endswith("v_proj.weight"): | ||
| proj_linear_count += 1 | ||
| if name.endswith("kv_scale"): | ||
| kv_scale_count += 1 | ||
| assert proj_linear_count // 2 == kv_scale_count | ||
|
|
||
| # Measure checkpoint size and cleanup | ||
| model_size = os.path.getsize(f"{quantized_model_dir}/model.safetensors") | ||
| shutil.rmtree(quantized_model_dir) | ||
|
|
||
| # We expect the quantized model to be a certain size | ||
| target_size = target_size * (1024 * 1024) | ||
| assert model_size < target_size | ||
| # @pytest.mark.parametrize("model_id,target_size", MODELS) | ||
| # def test_kv_cache_static_quantization(model_id, target_size): | ||
| # quantized_model_dir = model_id.split("/")[-1] + "-fp8-static-kv" | ||
|
|
||
| # tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True) | ||
| # examples = ["auto-fp8 is an easy-to-use model quantization library"] | ||
| # examples = tokenizer(examples, return_tensors="pt") | ||
|
|
||
| # quantize_config = BaseQuantizeConfig( | ||
| # quant_method="fp8", | ||
| # activation_scheme="static", | ||
| # kv_cache_quant_targets=("k_proj", "v_proj"), | ||
| # ) | ||
|
|
||
| # model = AutoFP8ForCausalLM.from_pretrained(model_id, quantize_config) | ||
| # model.model.to("cpu") | ||
|
|
||
| # model.quantize(examples) | ||
| # model.save_quantized(quantized_model_dir) | ||
|
|
||
| # tensors = safetensors.torch.load_file(f"{quantized_model_dir}/model.safetensors") | ||
| # proj_linear_count = 0 | ||
| # kv_scale_count = 0 | ||
| # for name, _ in tensors.items(): | ||
| # if name.endswith("k_proj.weight") or name.endswith("v_proj.weight"): | ||
| # proj_linear_count += 1 | ||
| # if name.endswith("kv_scale"): | ||
| # kv_scale_count += 1 | ||
| # assert proj_linear_count // 2 == kv_scale_count | ||
|
|
||
| # # Measure checkpoint size and cleanup | ||
| # model_size = os.path.getsize(f"{quantized_model_dir}/model.safetensors") | ||
| # shutil.rmtree(quantized_model_dir) | ||
|
|
||
| # # We expect the quantized model to be a certain size | ||
| # target_size = target_size * (1024 * 1024) | ||
| # assert model_size < target_size |