Dynamic Float8 benchmarking llama (#1017)

pytorch · Oct 7, 2024 · 92dd5f5 · 92dd5f5
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diff --git a/torchao/_models/llama/benchmarks.sh b/torchao/_models/llama/benchmarks.sh
@@ -9,8 +9,6 @@ python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --co
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization int4wo-64 --write_result benchmark_results.txt
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --quantization autoquant-int4 --write_result benchmark_results.txt
 
-
-
 export MODEL_REPO=meta-llama/Meta-Llama-3-8B
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --write_result benchmark_results.txt
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization int8dq --write_result benchmark_results.txt
@@ -19,6 +17,14 @@ python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --co
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization int4wo-64 --write_result benchmark_results.txt
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --quantization autoquant-int4 --write_result benchmark_results.txt
 
+export MODEL_REPO=meta-llama/Meta-Llama-3.1-8B
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --write_result benchmark_results.txt
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization int8wo --write_result benchmark_results.txt
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization int4wo-64 --write_result benchmark_results.txt
+# Runs on H100, float8 is not supported on CUDA arch < 8.9
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization float8wo --write_result benchmark_results.txt
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization float8dq-tensor --write_result benchmark_results.txt
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization float8dq-wo --write_result benchmark_results.txt
 
 # OTHER BENCHMARKS
 
@@ -58,4 +64,4 @@ python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --co
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization fp6 --write_result benchmark_results.txt --precision float16
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization sparse-marlin --precision float16 --write_result benchmark_results.txt
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization uintx-4-64 --write_result benchmark_results.txt
-python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization uintx-2-8 --write_result benchmark_results.txt
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization uintx-2-8 --write_result benchmark_results.txt
diff --git a/torchao/_models/llama/eval.py b/torchao/_models/llama/eval.py
@@ -20,13 +20,16 @@
     fpx_weight_only,
     uintx_weight_only,
     unwrap_tensor_subclass,
+    float8_weight_only,
+    float8_dynamic_activation_float8_weight,
+    float8_static_activation_float8_weight,
 )
+from torchao.quantization.observer import PerRow, PerTensor
 from torchao._models._eval import TransformerEvalWrapper, InputRecorder
+from torchao._models.llama.model import prepare_inputs_for_model
 
 from tokenizer import get_tokenizer
 import time
-from torchao.quantization.GPTQ import Int4WeightOnlyGPTQQuantizer
-from torchao._models.llama.model import prepare_inputs_for_model, TransformerBlock
 from torchao.utils import TORCH_VERSION_AT_LEAST_2_5
 
 def run_evaluation(
@@ -55,19 +58,16 @@ def run_evaluation(
     tokenizer_path = checkpoint_path.parent / "tokenizer.model"
     assert tokenizer_path.is_file(), str(tokenizer_path)
     # Load Model and Tokenizer
-
     print("Loading model ...")
     t0 = time.time()
     model = _load_model(checkpoint_path, "cpu", precision)
 
     if max_length is None:
         max_length = model.config.block_size
-
     device_sync(device=device) # MKG
     print(f"Time to load model: {time.time() - t0:.02f} seconds")
     tokenizer = get_tokenizer(tokenizer_path, checkpoint_path)
 
-
     if quantization:
         if "int8wo" in quantization:
             quantize_(model, int8_weight_only())
@@ -100,6 +100,9 @@ def run_evaluation(
             from torchao.dtypes import MarlinSparseLayoutType
             quantize_(model, int4_weight_only(layout_type=MarlinSparseLayoutType()))
         if "int4wo" in quantization and "gptq" in quantization:
+            # avoid circular imports
+            from torchao._models._eval import InputRecorder
+            from torchao.quantization.GPTQ import Int4WeightOnlyGPTQQuantizer
             groupsize=int(quantization.split("-")[-2])
             assert groupsize in [32,64,128,256], f"int4wo groupsize needs to be one of [32,64,128,256] but got {groupsize}"
             assert precision==torch.bfloat16, f"{quantization} requires precision or bfloat16 but got {precision}"
@@ -122,9 +125,24 @@ def run_evaluation(
         else:
             if not TORCH_VERSION_AT_LEAST_2_5:
                 unwrap_tensor_subclass(model)
+        if "float8wo" in quantization:
+            quantize_(model, float8_weight_only())
+        if "float8dq" in quantization:
+            granularity = str(quantization.split("-")[-1])
+            if granularity=="tensor":
+                granularity = PerTensor()
+            elif granularity=="row":
+                granularity = PerRow()
+            else:
+                if granularity=="float8dq":
+                    granularity = PerTensor()
+                else:
+                    raise ValueError(f"Unknown granularity {granularity}")
+            quantize_(model, float8_dynamic_activation_float8_weight(granularity=granularity))
         if "autoround" in quantization:
             from torchao.prototype.autoround.autoround_llm import quantize_model_with_autoround_
             from transformers import AutoTokenizer
+            from torchao._models.llama.model import TransformerBlock
 
             _tokenizer = AutoTokenizer.from_pretrained(checkpoint_path.parent)
             # parse args from quantization string:
@@ -182,6 +200,9 @@ def run_evaluation(
     if compile:
         model = torch.compile(model, mode="max-autotune", fullgraph=True)
     with torch.no_grad():
+        print("Running evaluation ...")
+        # avoid circular imports
+        from torchao._models._eval import TransformerEvalWrapper
         TransformerEvalWrapper(
             model=model.to(device),
             tokenizer=tokenizer,
@@ -209,7 +230,8 @@ def run_evaluation(
             "Which quantization techniques to apply: int8dq, int8wo, fp6, int4wo-<groupsize>, "
             "int4wo-<groupsize>-gptq, autoquant, autoquant-int4, int4wo-<groupsize>-hqq, "
             "uintx-<nbits>-<groupsize>, uintx-<nbits>-<groupsize>-hqq, sparse-marlin, "
-            "autoround-<model_device>-<quant_lm_head>-<iters>-<groupsize>-<batch_size>-<seqlen>-<nsamples>-<grad_acc_steps>-<c>"
+            "autoround-<model_device>-<quant_lm_head>-<iters>-<groupsize>-<batch_size>-<seqlen>-<nsamples>-<grad_acc_steps>-<c>, "
+            "float8wo, float8dq, float8saq"
         ),
     )
     parser.add_argument('--compile', action='store_true', help='Whether to compile the model.')

diff --git a/torchao/_models/llama/evals.sh b/torchao/_models/llama/evals.sh
@@ -11,5 +11,13 @@ python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quanti
 export MODEL_REPO=meta-llama/Meta-Llama-3.1-8B
 python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization autoround-cpu    # auto-round w/o quant_lm_head
 python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization autoround-cuda-1 # auto-round w/ quant_lm_head
-python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization autoquant --tasks 'mmlu' 'truthfulqa_mc2'
-python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization autoquant --tasks 'winogrande' 'arc_challenge'
+python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth
+python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization int8wo
+python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization int4wo-64
+python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization float8wo
+python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization float8dq-tensor
+python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization float8dq-row
+
+# Testing on additional tasks
+python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization int8wo --tasks 'winogrande' 'arc_challenge'
+python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization int8wo --tasks 'mmlu' 'truthfulqa_mc2'
diff --git a/torchao/_models/llama/generate.py b/torchao/_models/llama/generate.py
@@ -210,8 +210,11 @@ def main(
             fpx_weight_only,
             uintx_weight_only,
             autoquant,
-            unwrap_tensor_subclass
+            unwrap_tensor_subclass,
+            float8_weight_only,
+            float8_dynamic_activation_float8_weight,
         )
+        from torchao.quantization.observer import PerTensor, PerRow
         if "int8wo" in quantization:
             quantize_(model, int8_weight_only())
         if "int8dq" in quantization:
@@ -243,6 +246,17 @@ def main(
             dtype = _NBITS_TO_DTYPE[nbits]
             group_size = int(_quant_args[2])
             quantize_(model, uintx_weight_only(dtype, group_size, use_hqq=use_hqq))
+        if "float8wo" in quantization:
+            quantize_(model, float8_weight_only())
+        if "float8dq" in quantization:
+            granularity = str(quantization.split("-")[-1])
+            if granularity=="tensor":
+                granularity = PerTensor()
+            elif granularity=="row":
+                granularity = PerRow()
+            else:
+                granularity = PerTensor()
+            quantize_(model, float8_dynamic_activation_float8_weight(granularity=granularity))
         if "autoquant" in quantization:
             if "autoquant-int4" == quantization:
                 model = autoquant(model, manual=True, qtensor_class_list = torchao.quantization.DEFAULT_INT4_AUTOQUANT_CLASS_LIST)

diff --git a/torchao/quantization/README.md b/torchao/quantization/README.md
@@ -21,6 +21,17 @@ Benchmarks and evaluation are run on a machine with a single NVIDIA-A100-80GB GP
 |             | int4wo-64-GPTQ          |  7.921              |  180.80       |  763.33                 |  6.88            |  4.22           |
 |             | autoquant-int4hqq       |  8.110              |  188.41       |  800.58                 |  7.14            |  4.25           |
 
+Benchmarks and evaluation for model meta-llama/Meta-Llama-3.1-8B are run on a machine with a single NVIDIA-H100 GPU using the scripts for [generation](../_models/llama/generate.py) and [eval](../_models/llama/eval.py). Evaluation was done using the lm_eval library for tasks/data.
+
+| Model         | Technique               | wikitext-perplexity | Tokens/Second | Memory Bandwidth (GB/s) | Peak Memory (GB) | Model Size (GB) |
+| -----------   | ----------------------- | ------------------- | ------------- | ----------------------- | ---------------- | --------------- |
+| Llama-3.1-8B  | Base (bfloat16)         |  7.54               |  126.90       | 1904.75                 | 16.75            | 15.01           |
+|               | int8wo                  |  7.56               |  198.85       | 1495.41                 | 11.05            |  7.52           |
+|               | int4wo-64               |  8.44               |  241.39       | 1019.14                 |  7.08            |  4.22           |
+|               | float8wo                |  7.60               |  178.46       | 1339.93                 | 12.09            |  7.51           |
+|               | float8dq (PerTensor)    |  7.62               |  116.40       |  873.58                 | 11.14            |  7.51           |
+|               | float8dq (Per Row)      |  7.61               |  154.63       | 1161.47                 | 11.14            |  7.51           |
+
 note: Int8 dynamic quantization works best on compute bound models like [SAM](https://github.com/pytorch-labs/segment-anything-fast) whereas Llama with batchsize=1 tends to be memory bound, thus the rather low performance.
 
 For int4 we make heavy use of [tinygemm](https://github.com/pytorch/ao/blob/cb3bd8c674f2123af232a0231b5e38ddafa756a8/torchao/dtypes/aqt.py#L526) of `torch.ops.aten._weight_int4pack_mm` to bitpack into a layout optimized for tensor cores
@@ -121,6 +132,15 @@ from torchao.quantization.quant_api import change_linear_weights_to_int8_dqtenso
 change_linear_weights_to_int8_dqtensors(model)
 ```
 
+#### A8W8 Float8 Dynamic Quantization
+
+```python
+# for torch 2.4+
+from torchao.quantization import quantize_, float8_dynamic_activation_float8_weight
+from torchao.quantization.observer import PerTensor
+quantize_(model, float8_dynamic_activation_float8_weight(granularity=PerTensor()))
+```
+
 #### A16W6 Floating Point WeightOnly Quantization
 
 ```python