Move autoround from generate.py to eval.py (#868)

* move autoround from generate to eval Signed-off-by: yiliu30 <yi4.liu@intel.com> * add llama3 back Signed-off-by: yiliu30 <yi4.liu@intel.com> * update the scripts Signed-off-by: yiliu30 <yi4.liu@intel.com> * update the scripts Signed-off-by: yiliu30 <yi4.liu@intel.com> * rename eval_acc.sh -> evals.sh Signed-off-by: yiliu30 <yi4.liu@intel.com> * update Signed-off-by: yiliu30 <yi4.liu@intel.com> * update Signed-off-by: yiliu30 <yi4.liu@intel.com> --------- Signed-off-by: yiliu30 <yi4.liu@intel.com>
pytorch · Sep 14, 2024 · d2bce6a · d2bce6a
1 parent 90c8cbd
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diff --git a/torchao/_models/llama/benchmarks.sh b/torchao/_models/llama/benchmarks.sh
@@ -49,9 +49,6 @@ 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 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
-# TODO: this is an accuracy technique with same perf as int4, should be in evaluations instead of generate.py
-python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization  autoround  # auto-round w/o quant_lm_head
-python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization  autoround-cuda-0  # auto-round w/o quant_lm_head
 
 export MODEL_REPO=meta-llama/Meta-Llama-3-8B
 python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --precision torch.float32 --write_result benchmark_results.txt
@@ -61,7 +58,4 @@ 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
-# TODO: this is an accuracy technique with same perf as int4, should be in evaluations instead of generate.py
-python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization  autoround  # auto-round w/o quant_lm_head
-python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization  autoround-cuda-0  # auto-round w/o quant_lm_head
+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
@@ -26,7 +26,7 @@
 from tokenizer import get_tokenizer
 import time
 from torchao.quantization.GPTQ import Int4WeightOnlyGPTQQuantizer
-from torchao._models.llama.model import prepare_inputs_for_model
+from torchao._models.llama.model import prepare_inputs_for_model, TransformerBlock
 from torchao.utils import TORCH_VERSION_AT_LEAST_2_5
 
 def run_evaluation(
@@ -122,6 +122,51 @@ def run_evaluation(
         else:
             if not TORCH_VERSION_AT_LEAST_2_5:
                 unwrap_tensor_subclass(model)
+        if "autoround" in quantization:
+            from torchao.prototype.autoround.autoround_llm import quantize_model_with_autoround_
+            from transformers import AutoTokenizer
+
+            _tokenizer = AutoTokenizer.from_pretrained(checkpoint_path.parent)
+            # parse args from quantization string:
+            #   autoround-<model_device>-<quant_lm_head>-<iters>-<groupsize>-<batch_size>-<seqlen>-<nsamples>
+            _quant_args = quantization.split("-")
+            _default_quant_args = [False, 200, 128, 8, 2048, 128]
+            _model_devie = _quant_args[1] if len(_quant_args) > 1 else device
+            _quant_args = _quant_args[2:]
+            quant_lm_head, iters, groupsize, batch_size, seqlen, nsamples = [
+                int(x) for x in _quant_args
+            ] + _default_quant_args[len(_quant_args) :]
+            model = model.to(_model_devie)
+            print(
+                (
+                    f"Quantizing model with autoround(iters={iters}, groupsize={groupsize}, "
+                    f"quant_lm_head={quant_lm_head}, batch_size={batch_size}, seqlen={seqlen}, nsamples={nsamples})"
+                )
+            )
+            with torch.device(_model_devie):
+                model.setup_caches(
+                    max_batch_size=batch_size, max_seq_length=seqlen, training=True
+                )
+
+            if quant_lm_head:
+                is_target_module = (
+                    lambda mod, fqn: isinstance(mod, TransformerBlock)
+                    or "output" in fqn
+                )
+            else:
+                is_target_module = lambda mod, fqn: isinstance(mod, TransformerBlock)
+            quantize_model_with_autoround_(
+                model=model,
+                tokenizer=_tokenizer,
+                is_target_module=is_target_module,
+                bits=4,
+                seqlen=seqlen,
+                bs=batch_size,
+                iters=iters,
+                nsamples=nsamples,
+            )
+            model.to(device)
+            model.reset_caches()
 
     if compile:
         model = torch.compile(model, mode="max-autotune", fullgraph=True)
@@ -145,11 +190,15 @@ def run_evaluation(
     parser.add_argument('--limit', type=int, default=None, help='Number of eval samples to evaluate')
     parser.add_argument('--precision', type=lambda x: getattr(torch, x.split(".")[-1]), default=torch.bfloat16, help='dtype precision to use')
     parser.add_argument('--device', type=str, default="cuda", help='Device to use for evaluation')
-    parser.add_argument('-q', '--quantization', type=str, 
+    parser.add_argument(
+        "-q",
+        "--quantization",
+        type=str,
         help=(
-            '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'
-        )
+            "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>"
+        ),
     )
     parser.add_argument('--compile', action='store_true', help='Whether to compile the model.')
     parser.add_argument('--max_length', type=int, default=None, help='Length of text to process at one time')

diff --git a/torchao/_models/llama/evals.sh b/torchao/_models/llama/evals.sh
@@ -0,0 +1,9 @@
+export CHECKPOINT_PATH=../../../checkpoints # path to checkpoints folder
+
+export MODEL_REPO=meta-llama/Llama-2-7b-chat-hf
+python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization autoround        # 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
+
+export MODEL_REPO=meta-llama/Meta-Llama-3-8B
+python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantizatio autoround-cpu    # auto-round w/o quant_lm_head
+python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization autoround-cpu-1 # auto-round w/ quant_lm_head
diff --git a/torchao/_models/llama/generate.py b/torchao/_models/llama/generate.py
@@ -30,7 +30,7 @@ def device_sync(device):
 wd = Path(__file__).parent.parent.resolve()
 sys.path.append(str(wd))
 
-from torchao._models.llama.model import Transformer, prepare_inputs_for_model, TransformerBlock
+from torchao._models.llama.model import Transformer, prepare_inputs_for_model
 from torchao._models.llama.tokenizer import get_tokenizer
 
 def multinomial_sample_one_no_sync(probs_sort): # Does multinomial sampling without a cuda synchronization
@@ -227,53 +227,7 @@ def main(
         if "marlin" in quantization:
             from torchao.dtypes import MarlinSparseLayoutType
             quantize_(model, int4_weight_only(layout_type=MarlinSparseLayoutType()))
-        if "autoround" in quantization:
-            from torchao.prototype.autoround.autoround_llm import quantize_model_with_autoround_
-            from transformers import AutoTokenizer
-
-            _tokenizer = AutoTokenizer.from_pretrained(checkpoint_path.parent)
-            # parse args from quantization string:
-            #   autoround-<model_device>-<quant_lm_head>-<iters>-<groupsize>-<batch_size>-<seqlen>-<nsamples>
-            #   A lightweight configuration for generation benchmarking.
-            _quant_args = quantization.split("-")
-            _default_quant_args = [True, 1, 128, 1, 512, 32]
-            _model_devie = _quant_args[1] if len(_quant_args) > 1 else device
-            _quant_args = _quant_args[2:]
-            quant_lm_head, iters, groupsize, batch_size, seqlen, nsamples = [
-                int(x) for x in _quant_args
-            ] + _default_quant_args[len(_quant_args) :]
-            model = model.to(_model_devie)
-            print(
-                (
-                    f"Quantizing model with autoround(iters={iters}, groupsize={groupsize}, "
-                    f"quant_lm_head={quant_lm_head}, batch_size={batch_size}, seqlen={seqlen}, nsamples={nsamples})"
-                )
-            )
-            with torch.device(_model_devie):
-                model.setup_caches(
-                    max_batch_size=batch_size, max_seq_length=seqlen, training=True
-                )
-
-            if quant_lm_head:
-                is_target_module = (
-                    lambda mod, fqn: isinstance(mod, TransformerBlock) or "output" in fqn
-                )
-            else:
-                is_target_module = lambda mod, fqn: isinstance(mod, TransformerBlock)
-            quantize_model_with_autoround_(
-                model=model,
-                tokenizer=_tokenizer,
-                is_target_module=is_target_module,
-                bits=4,
-                seqlen=seqlen,
-                bs=batch_size,
-                iters=iters,
-                nsamples=nsamples,
-            )
-            model.to(device)
-            model.reset_caches()
-        # TODO this needs to be expanded to all of fpx so they can 
-        if "fp6" in quantization: 
+        if "fp6" in quantization:
             quantize_(model, fpx_weight_only(3, 2))
         if "uintx" in quantization:
             # uintx-nbits-groupsize, e.g. "uintx-2-64"
@@ -461,8 +415,7 @@ def callback(x):
     parser.add_argument('-q', '--quantization', type=str, 
         help=(
             'Which quantization techniques to apply: int8dq, int8wo, fp6, int4wo-<groupsize>, int4wo-<groupsize>-hqq, autoquant, '
-            +'autoquant-int4, autoround-<model_device>-<quant_lm_head>-<iters>-<groupsize>-<batch_size>-<seqlen>-<nsamples>, '
-            +'uintx-<nbits>-<groupsize>, uintx-<nbits>-<groupsize>-hqq, sparse-marlin'
+            +'autoquant-int4, uintx-<nbits>-<groupsize>, uintx-<nbits>-<groupsize>-hqq, sparse-marlin'
         )
     )
     parser.add_argument('--kv_cache_quantization', action='store_true', help='Whether to quantize the KV cache')