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tests/quantization/finegrained_fp8/test_fp8.py

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+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import gc
+import tempfile
+import unittest
+from contextlib import ExitStack, contextmanager
+from unittest.mock import patch
+
+from parameterized import parameterized
+
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, FineGrainedFP8Config, OPTForCausalLM
+from transformers.quantizers.quantizer_finegrained_fp8 import FineGrainedFP8HfQuantizer
+from transformers.testing_utils import (
+    backend_empty_cache,
+    get_device_properties,
+    require_accelerate,
+    require_torch_accelerator,
+    require_torch_multi_accelerator,
+    slow,
+    torch_device,
+)
+from transformers.utils import is_torch_available
+
+
+if is_torch_available():
+    import torch
+
+
+@contextmanager
+def _patch_no_accelerator():
+    with ExitStack() as stack:
+        stack.enter_context(patch("torch.cuda.is_available", return_value=False))
+        if hasattr(torch, "xpu"):
+            stack.enter_context(patch("torch.xpu.is_available", return_value=False))
+            stack.enter_context(
+                patch("transformers.quantizers.quantizer_finegrained_fp8.is_torch_xpu_available", return_value=False)
+            )
+        yield
+
+
+@require_torch_accelerator
+class FineGrainedFP8ConfigTest(unittest.TestCase):
+    def test_to_dict(self):
+        """
+        Simple test that checks if one uses a config and converts it to a dict, the dict is the same as the config object
+        """
+        quantization_config = FineGrainedFP8Config()
+        config_to_dict = quantization_config.to_dict()
+
+        for key in config_to_dict:
+            self.assertEqual(getattr(quantization_config, key), config_to_dict[key])
+
+    def test_from_dict(self):
+        """
+        Simple test that checks if one uses a dict and converts it to a config object, the config object is the same as the dict
+        """
+        dict = {"modules_to_not_convert": ["lm_head.weight"], "quant_method": "fp8"}
+        quantization_config = FineGrainedFP8Config.from_dict(dict)
+
+        self.assertEqual(dict["modules_to_not_convert"], quantization_config.modules_to_not_convert)
+        self.assertEqual(dict["quant_method"], quantization_config.quant_method)
+
+
+@slow
+@require_accelerate
+@require_torch_accelerator
+@unittest.skipIf(
+    get_device_properties()[0] == "cuda"
+    and (get_device_properties()[1] < 8 or (get_device_properties()[1] == 8 and get_device_properties()[2] < 9)),
+    "Skipping FP8QuantizerTest because it is not supported on GPU with capability < 8.9",
+)
+class FP8QuantizerTest(unittest.TestCase):
+    model_name = "meta-llama/Llama-3.2-1B"
+    quantized_model_name = "hf-internal-testing/Llama-3.2-1B-Instruct-fp8"
+    input_text = "Once upon a time"
+    max_new_tokens = 10
+    EXPECTED_OUTPUTS = {
+        "Once upon a time, there was a little girl who loved to play",
+        "Once upon a time, there was a man who was very rich.",
+    }
+    EXPECTED_DEQUANTIZED_OUTPUT = "Once upon a time, in a small village nestled in the rolling hills"
+    device_map = torch_device
+    offload_device_map = {
+        "model.embed_tokens": 0,
+        "model.layers.0": 0,
+        "model.layers.1": 0,
+        "model.layers.2": 0,
+        "model.layers.3": 0,
+        "model.layers.4": 0,
+        "model.layers.5": 0,
+        "model.layers.6": 0,
+        "model.layers.7": "cpu",
+        "model.layers.8": "cpu",
+        "model.layers.9": "cpu",
+        "model.layers.10": "cpu",
+        "model.layers.11": "cpu",
+        "model.layers.12": "cpu",
+        "model.layers.13": "cpu",
+        "model.layers.14": "cpu",
+        "model.layers.15": "cpu",
+        "model.rotary_emb": "cpu",
+        "model.norm": "cpu",
+        "lm_head": 0,
+    }
+
+    @classmethod
+    def setUpClass(cls):
+        """
+        Setup quantized model
+        """
+        cls.quantization_config = FineGrainedFP8Config()
+        cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
+        cls.quantized_model = AutoModelForCausalLM.from_pretrained(
+            cls.model_name, device_map=cls.device_map, quantization_config=cls.quantization_config
+        )
+
+    def setup(self):
+        """
+        Clear also on each setup (e.g. if a different model is used than the base cls one)
+        """
+        gc.collect()
+        backend_empty_cache(torch_device)
+        gc.collect()
+
+    def tearDown(self):
+        gc.collect()
+        backend_empty_cache(torch_device)
+        gc.collect()
+
+    @parameterized.expand(
+        [
+            "hf-internal-testing/tiny-random-Qwen3MoeForCausalLM",
+            "hf-internal-testing/tiny-random-MixtralForCausalLM",
+        ]
+    )
+    def test_moe_conversion_doesnt_raise(self, model_id):
+        quantization_config = FineGrainedFP8Config(weight_block_size=(32, 32))
+        AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config)
+
+    def test_quantized_model_conversion(self):
+        """
+        Simple test that checks if the quantized model has been converted properly
+        """
+
+        from transformers.integrations import FP8Linear, replace_with_fp8_linear
+
+        model_id = "facebook/opt-350m"
+        config = AutoConfig.from_pretrained(model_id, revision="cb32f77e905cccbca1d970436fb0f5e6b58ee3c5")
+        quantization_config = FineGrainedFP8Config()
+
+        with torch.device("meta"):
+            model = OPTForCausalLM(config)
+
+        nb_linears = 0
+        for module in model.modules():
+            if isinstance(module, torch.nn.Linear):
+                nb_linears += 1
+        model = replace_with_fp8_linear(model, quantization_config=quantization_config)
+        nb_fp8_linear = 0
+        for module in model.modules():
+            if isinstance(module, FP8Linear):
+                nb_fp8_linear += 1
+        self.assertEqual(nb_linears, nb_fp8_linear)
+        with torch.device("meta"):
+            model = OPTForCausalLM(config)
+        quantization_config = FineGrainedFP8Config()
+        model = replace_with_fp8_linear(model, modules_to_not_convert=["fc1"], quantization_config=quantization_config)
+        nb_fp8_linear = 0
+        for module in model.modules():
+            if isinstance(module, FP8Linear):
+                nb_fp8_linear += 1
+        self.assertEqual(nb_linears - 24, nb_fp8_linear)
+
+    def test_quantizer_validation_no_accelerator(self):
+        """Test quantizer validation when CUDA/XPU is not available"""
+        with _patch_no_accelerator():
+            config = FineGrainedFP8Config()
+            quantizer = FineGrainedFP8HfQuantizer(config)
+            quantizer.pre_quantized = False
+
+            with self.assertRaises(RuntimeError):
+                quantizer.validate_environment()
+
+    def test_dequantization_no_accelerator(self):
+        """Test dequantization when CUDA/XPU is not available"""
+        with _patch_no_accelerator():
+            config = FineGrainedFP8Config()
+            quantizer = FineGrainedFP8HfQuantizer(config)
+            quantizer.pre_quantized = True
+            quantizer.validate_environment()
+            self.assertTrue(quantizer.quantization_config.dequantize)
+
+    def test_quantized_model(self):
+        """
+        Simple test that checks if the quantized model is working properly
+        """
+        input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(self.device_map)
+
+        output = self.quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens, do_sample=False)
+        output_tokens = self.tokenizer.decode(output[0], skip_special_tokens=True)
+        self.assertIn(output_tokens, self.EXPECTED_OUTPUTS)
+
+    def test_dequantized_model(self):
+        """
+        Simple test that checks if the dequantized model is working properly
+        """
+        quantization_config = FineGrainedFP8Config(dequantize=True)
+        dequantized_model = AutoModelForCausalLM.from_pretrained(
+            self.quantized_model_name, device_map=self.device_map, quantization_config=quantization_config
+        )
+        input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(self.device_map)
+        output = dequantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens, do_sample=False)
+        output_tokens = self.tokenizer.decode(output[0], skip_special_tokens=True)
+        self.assertEqual(output_tokens, self.EXPECTED_DEQUANTIZED_OUTPUT)
+        del dequantized_model
+
+    def test_dequantize_when_no_accelerator(self):
+        """
+        Simple test that checks if the dequantized model is working properly when no accelerator is available
+        """
+        with _patch_no_accelerator():
+            dequantized_model = AutoModelForCausalLM.from_pretrained(self.quantized_model_name, device_map="cpu")
+            input_ids = self.tokenizer(self.input_text, return_tensors="pt").to("cpu")
+            output = dequantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens, do_sample=False)
+            output_tokens = self.tokenizer.decode(output[0], skip_special_tokens=True)
+            self.assertEqual(output_tokens, self.EXPECTED_DEQUANTIZED_OUTPUT)
+            del dequantized_model
+
+    def test_save_pretrained(self):
+        """
+        Simple test that checks if the quantized model is working properly after being saved and loaded
+        """
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            self.quantized_model.save_pretrained(tmpdirname)
+
+            model = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map=self.device_map)
+
+            input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(self.device_map)
+
+            output = model.generate(**input_ids, max_new_tokens=self.max_new_tokens, do_sample=False)
+            self.assertIn(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
+
+    def test_weight_and_weight_scale_inv(self):
+        """
+        Simple test that checks if the weight and weight_scale_inv are working properly
+        """
+        weight = self.quantized_model.model.layers[0].self_attn.q_proj.weight
+        weight_scale_inv = self.quantized_model.model.layers[0].self_attn.q_proj.weight_scale_inv
+        self.assertEqual(weight.dtype, torch.float8_e4m3fn)
+        self.assertEqual(weight_scale_inv.dtype, torch.float32)
+        self.assertEqual(weight.shape, (weight_scale_inv.shape[0] * 128, weight_scale_inv.shape[1] * 128))
+
+    def test_block_size(self):
+        """
+        Simple test that checks if the block size is working properly
+        """
+        self.assertEqual(self.quantized_model.config.quantization_config.weight_block_size, (128, 128))
+        quantization_config = FineGrainedFP8Config(weight_block_size=(32, 32))
+        quantized_model = AutoModelForCausalLM.from_pretrained(
+            self.model_name, device_map=self.device_map, quantization_config=quantization_config
+        )
+        self.assertEqual(quantized_model.config.quantization_config.weight_block_size, (32, 32))
+
+    @require_torch_multi_accelerator
+    def test_quantized_model_multi_accelerators(self):
+        """
+        Simple test that checks if the quantized model is working properly with multiple accelerators
+        set CUDA_VISIBLE_DEVICES=0,1 if you have more than 2 GPUs; or set ZE_AFFINITY_MASK=0,1 if you
+        have more than 2 XPUs.
+        """
+        input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(self.device_map)
+        quantization_config = FineGrainedFP8Config()
+        # need to empty cache or set max_memory, otherwise we will use the reserved memory that was not allocated when computing max-memory
+        # this will lead to put the entire model to device 0.
+        quantized_model = AutoModelForCausalLM.from_pretrained(
+            self.model_name,
+            device_map="auto",
+            quantization_config=quantization_config,
+            max_memory={0: "1GB", 1: "10GB"},
+        )
+        self.assertTrue(set(quantized_model.hf_device_map.values()) == {0, 1})
+
+        output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens, do_sample=False)
+        self.assertIn(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
+
+    @require_torch_multi_accelerator
+    def test_save_pretrained_multi_accelerators(self):
+        """
+        Simple test that checks if the quantized model is working properly after being saved and loaded
+        """
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            self.quantized_model.save_pretrained(tmpdirname)
+            # need to empty cache or set max_memory, otherwise we will use the reserved memory that was not allocated when computing max-memory
+            # this will lead to put the entire model to device 0.
+            model = AutoModelForCausalLM.from_pretrained(
+                tmpdirname, device_map="auto", max_memory={0: "1GB", 1: "10GB"}
+            )
+            self.assertTrue(set(model.hf_device_map.values()) == {0, 1})
+
+            input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(self.device_map)
+
+            output = model.generate(**input_ids, max_new_tokens=self.max_new_tokens, do_sample=False)
+            self.assertIn(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
+
+    def test_quantized_model_offload(self):
+        """
+        Simple test that checks if the quantized model returns an error when loading with cpu/disk offloaded
+        """
+        with self.assertRaisesRegex(
+            ValueError, "You are attempting to load an FP8 model with a device_map that contains a cpu/disk device."
+        ):
+            AutoModelForCausalLM.from_pretrained(
+                self.model_name, device_map=self.offload_device_map, quantization_config=self.quantization_config
+            )
+
+    def test_save_pretrained_offload(self):
+        """
+        Simple test that checks if the saved quantized model is working properly cpu/disk offload
+        """
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            self.quantized_model.save_pretrained(tmpdirname)
+
+            input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(self.device_map)
+
+            quantized_model = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map=self.offload_device_map)
+            output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens, do_sample=False)
+            self.assertIn(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
+
+    def test_compute_module_sizes(self):
+        r"""
+        Test if we compute the right module sizes needed to generate the device map.
+        Also test if we get the right values for `total_byte_count` in `caching_allocator_warmup`.
+        """
+        from transformers.integrations import FP8Linear
+        from transformers.integrations.accelerate import compute_module_sizes
+        from transformers.modeling_utils import expand_device_map, get_total_byte_count
+        from transformers.quantizers import AutoHfQuantizer
+
+        # we need to preprocess the model like that because device_map calculation happens before we load the weights inside the model.
+        # For normal wieghts, it's fine but for quantized weights, the tensors dtype might change during loading.
+        with torch.device("meta"):
+            config = AutoConfig.from_pretrained(self.model_name)
+            model = AutoModelForCausalLM.from_config(config, dtype=torch.bfloat16)
+            model_size, _ = compute_module_sizes(model, only_modules=False)
+
+            expected_keys = [name for name, _ in model.named_parameters()] + [
+                name for name, _ in model.named_buffers()
+            ]
+            expanded_device_map = expand_device_map({"": torch_device}, expected_keys)
+            total_byte_count = list(get_total_byte_count(model, expanded_device_map).values())[0]
+
+            # testing prequantized = False should be enough, the shape should be the same whether it is pre-quantized or not
+            hf_quantizer = AutoHfQuantizer.from_config(FineGrainedFP8Config(), pre_quantized=False)
+            hf_quantizer.preprocess_model(model=model, config=model.config)
+            quantized_model_size, _ = compute_module_sizes(model, hf_quantizer, only_modules=False)
+
+            expected_keys = [name for name, _ in model.named_parameters()] + [
+                name for name, _ in model.named_buffers()
+            ]
+            expanded_device_map = expand_device_map({"": torch_device}, expected_keys)
+            quantized_total_byte_count = list(get_total_byte_count(model, expanded_device_map, hf_quantizer).values())[
+                0
+            ]
+
+        for name, module in model.named_modules():
+            if isinstance(module, FP8Linear):
+                # from 16 bits to 8 bits
+                assert int(model_size[f"{name}.weight"] // 2) == int(quantized_model_size[f"{name}.weight"])
+
+        # check that we get the same value, as we use `compute_module_sizes` in `get_total_byte_count`
+        assert total_byte_count == model_size[""]
+        assert quantized_total_byte_count == quantized_model_size[""]
+
+        # we should at least have 1.5 times memory reduction in total
+        assert model_size[""] > quantized_model_size[""] * 1.5
+
+    @parameterized.expand(["eager", "batched_mm", "grouped_mm", "deepgemm"])
+    def test_quantized_moe_forward(self, experts_implementation):
+        """
+        Checks implicitly if the moe implementation is correct, i.e. it does not crash for cases
+        where the indices go over `top_k` as shown within the Minimax M2 model
+        """
+        model = AutoModelForCausalLM.from_pretrained(
+            "hf-internal-testing/MiniMax-M2-Tiny-FP8",  # single layer version
+            experts_implementation=experts_implementation,
+            device_map=self.device_map,
+        )
+        assert model.config._experts_implementation == experts_implementation
+
+        tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-M2")
+        messages = [
+            {"role": "user", "content": [{"type": "text", "text": "What is your favourite condiment?"}]},
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!",
+                    }
+                ],
+            },
+            {"role": "user", "content": [{"type": "text", "text": "Do you have mayonnaise recipes?"}]},
+        ]
+        model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(
+            self.device_map
+        )
+
+        # Only caring about this not crashing
+        _ = model.generate(**model_inputs, max_new_tokens=24)
+
+
+@require_torch_accelerator
+@unittest.skipIf(
+    get_device_properties()[0] == "cuda"
+    and (get_device_properties()[1] < 8 or (get_device_properties()[1] == 8 and get_device_properties()[2] < 9)),
+    "Skipping FP8LinearTest because it is not supported on GPU with capability < 8.9",
+)
+class FP8LinearTest(unittest.TestCase):
+    device = torch_device
+
+    def test_linear_preserves_shape(self):
+        """
+        Test that FP8Linear preserves shape when in_features == out_features.
+        """
+        from transformers.integrations import FP8Linear
+
+        linear = FP8Linear(256, 256, block_size=(128, 128)).to(self.device)
+        x = torch.rand((1, 5, 256)).to(self.device)
+
+        x_ = linear(x)
+        self.assertEqual(x_.shape, x.shape)
+
+    def test_linear_with_diff_feature_size_preserves_shape(self):
+        """
+        Test that FP8Linear generates the correct shape when in_features != out_features.
+        """
+        from transformers.integrations import FP8Linear
+
+        linear = FP8Linear(128, 256, block_size=(128, 128)).to(self.device)
+        x = torch.rand((1, 5, 128)).to(self.device)
+
+        x_ = linear(x)
+        self.assertEqual(x_.shape, (1, 5, 256))