Unify GPTQ dataloader with fixed/unfixed length data (#1212)

Signed-off-by: YIYANGCAI <yiyang.cai@intel.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: YIYANGCAI

examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_weight_only/run-gptq-llm.py

@@ -6,6 +6,7 @@
 import torch
 import torch.nn as nn
 from torch.utils.data import DataLoader
+from torch.nn.functional import pad
 
 import transformers
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -218,13 +219,6 @@ def skip(*args, **kwargs):
     model.eval()
 
     # dataset
-    # original method of loading data, only load the sequence whose length > model.seqlen
-    # ================================================
-    # dataloader, testloader = get_loaders(
-    #     args.dataset, nsamples=args.nsamples, seed=args.seed, model=args.model_name_or_path, seqlen=model.seqlen
-    # )
-    # dataloader = INCDataloader(dataloader)
-    # ================================================
     tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=True)
     calib_dataset = load_dataset(args.dataset, split="train") # default
     # calib_dataset = datasets.load_from_disk('/your/local/pile-10k/') # use this if trouble with connecting to HF
@@ -244,7 +238,6 @@ def skip(*args, **kwargs):
 
     model = model.to(DEV)
 
-    print('Starting ...')
     if args.sym:
         sym_opt = "sym"
     else:
@@ -276,7 +269,8 @@ def skip(*args, **kwargs):
     #             'act_order':args.act_order, 
     #             'block_size': args.block_size, 
     #             'nsampeles': args.nsamples,
-    #             'use_max_length': args.use_max_length
+    #             'use_max_length': args.use_max_length,
+    #             'pad_max_length': args.pad_max_length
     #         },
     #     },
     # )
@@ -296,7 +290,8 @@ def skip(*args, **kwargs):
         weight_config=conf, 
         dataloader=calib_dataloader, 
         nsamples = args.nsamples, 
-        use_max_length = args.use_max_length
+        use_max_length = args.use_max_length,
+        pad_max_length = args.pad_max_length
     )
 
     results = lm_evaluate(

neural_compressor/adaptor/pytorch.py

@@ -4597,9 +4597,15 @@ def gptq_quantize(self, model, tune_cfg, dataloader):
                 }
         nsamples = self.recipes["gptq_args"].get("nsamples", 128)
         use_max_length = self.recipes["gptq_args"].get("use_max_length", False)
+        pad_max_length = self.recipes["gptq_args"].get("pad_max_length", 2048)
+        if use_max_length and "pad_max_length" not in self.recipes["gptq_args"]:
+            logger.warning(
+                "You choose to use unified sequence length for calibration, \
+            but you have not set length value. Default sequence length is 2048 and this might cause inference error!"
+            )
         # tune_cfg => weight_config
         model, quantization_perm = gptq_quantize(
-            model, weight_config, dataloader, nsamples, use_max_length, self.device
+            model, weight_config, dataloader, nsamples, use_max_length, pad_max_length, self.device
         )
         return model, quantization_perm
 

neural_compressor/adaptor/torch_utils/gptq.py

@@ -166,7 +166,16 @@ class GPTQuantizer(object):
     url: https://arxiv.org/abs/2210.17323
     """
 
-    def __init__(self, model, weight_config={}, dataloader=None, nsamples=128, use_max_length=True, device=None):
+    def __init__(
+        self,
+        model,
+        weight_config={},
+        dataloader=None,
+        nsamples=128,
+        use_max_length=True,
+        pad_max_length=2048,
+        device=None,
+    ):
         """
         Args:
             model: the fp32 model to quantize
@@ -211,44 +220,29 @@ def __init__(self, model, weight_config={}, dataloader=None, nsamples=128, use_m
 
         # dataloader
         self.use_max_length = use_max_length
+        self.pad_max_length = pad_max_length
         self.dataloader_original = dataloader
         self.dataloader = []
         self.nsamples = nsamples
         self.prepare_dataloader()
 
     def prepare_dataloader(self):
         if self.use_max_length:
-            # (Recommend) only take sequence whose length exceeds model.seqlen,
+            # (Recommend) only take sequence whose length exceeds self.pad_max_length,
             # which perserves calibration's tokens are all valid
             # This is GPTQ official dataloader implementation
             self.obtain_first_n_samples_fulllength()
-            # initialize buffers which are essential for gptq computation.
-            self.model_hidden_size = 2048
-            self.initialize_inp_buffersize()
-            try:
-                # Since length is unified, we can allocate a continous space to store inputs
-                self.inp = torch.zeros(
-                    (len(self.dataloader), self.model.seqlen, self.model_hidden_size),
-                    dtype=self.dtype,
-                    device=self.device,
-                )
-                self.cache = {"i": 0}
-                self.out = torch.zeros_like(self.inp)
-                self.is_ready = True
-            except:
-                logger.warning("GPTQ Quantizer initialization failed!")
-                pass
         else:
             # general selection, no padding, not GPTQ original implementation.
             self.obtain_first_n_samples()
-            try:
-                self.inp = [torch.zeros(1) for _ in range(len(self.dataloader))]
-                self.cache = {"i": 0}
-                self.out = [torch.zeros(1) for _ in range(len(self.dataloader))]
-                self.is_ready = True
-            except:
-                logger.warning("GPTQ Quantizer initialization failed!")
-                pass
+        try:
+            self.inp = [torch.zeros(1) for _ in range(len(self.dataloader))]
+            self.cache = {"i": 0}
+            self.out = [torch.zeros(1) for _ in range(len(self.dataloader))]
+            self.is_ready = True
+        except:
+            logger.warning("GPTQ Quantizer initialization failed!")
+            pass
 
     def obtain_first_n_samples(self, seed=0):
         """Get first nsample data as the real calibration dataset."""
@@ -257,12 +251,13 @@ def obtain_first_n_samples(self, seed=0):
         for batch in self.dataloader_original:
             # process data, depends on its data type.
             if len(self.dataloader) == self.nsamples:
+                logger.info(f"Successfully collect {self.nsamples} calibration samples.")
                 break
             # list, tuple
             if isinstance(batch, list) or isinstance(batch, tuple):
-                if batch[0].shape[-1] > self.model.seqlen:
-                    i = random.randint(0, batch[0].shape[-1] - self.model.seqlen - 1)
-                    j = i + self.model.seqlen
+                if batch[0].shape[-1] > self.pad_max_length:
+                    i = random.randint(0, batch[0].shape[-1] - self.pad_max_length - 1)
+                    j = i + self.pad_max_length
                     batch_final = batch[0][:, i:j]
                 else:
                     batch_final = batch[0]
@@ -274,9 +269,9 @@ def obtain_first_n_samples(self, seed=0):
                     logger.warning("Please make sure your dict'like data contains key of 'input_ids'.")
                     continue
                 batch_final = {}
-                if length > self.model.seqlen:
-                    i = random.randint(0, length - self.model.seqlen - 1)
-                    j = i + self.model.seqlen
+                if length > self.pad_max_length:
+                    i = random.randint(0, length - self.pad_max_length - 1)
+                    j = i + self.pad_max_length
                     # may have to slice every sequence related data
                     for key in batch.keys():
                         if isinstance(batch[key], torch.Tensor):
@@ -287,9 +282,9 @@ def obtain_first_n_samples(self, seed=0):
                     batch_final = batch
             # tensor
             else:
-                if batch.shape[-1] > self.model.seqlen:
-                    i = random.randint(0, batch.shape[-1] - self.model.seqlen - 1)
-                    j = i + self.model.seqlen
+                if batch.shape[-1] > self.pad_max_length:
+                    i = random.randint(0, batch.shape[-1] - self.pad_max_length - 1)
+                    j = i + self.pad_max_length
                     batch_final = batch[:, i:j]
                 else:
                     batch_final = batch
@@ -301,9 +296,10 @@ def obtain_first_n_samples(self, seed=0):
     def obtain_first_n_samples_fulllength(self, seed=0):
         self.dataloader.clear()
         random.seed(seed)
-        unified_length = self.model.seqlen
+        unified_length = self.pad_max_length
         for batch in self.dataloader_original:
             if len(self.dataloader) == self.nsamples:
+                logger.info(f"Successfully collect {self.nsamples} calibration samples.")
                 break
             # list & tuple
             if isinstance(batch, list) or isinstance(batch, tuple):
@@ -325,11 +321,11 @@ def obtain_first_n_samples_fulllength(self, seed=0):
                     logger.warning("Please make sure your dict'like data contains key of 'input_ids'.")
                     continue
                 batch_final = {}
-                if length == self.model.seqlen:
+                if length == self.pad_max_length:
                     batch_final = batch
-                elif length > self.model.seqlen:
-                    i = random.randint(0, length - self.model.seqlen - 1)
-                    j = i + self.model.seqlen
+                elif length > self.pad_max_length:
+                    i = random.randint(0, length - self.pad_max_length - 1)
+                    j = i + self.pad_max_length
                     # may have to slice every sequence related data
                     for key in batch.keys():
                         if isinstance(batch[key], torch.Tensor):
@@ -354,53 +350,9 @@ def obtain_first_n_samples_fulllength(self, seed=0):
         if len(self.dataloader) < self.nsamples:  # pragma: no cover
             logger.warning(
                 f"Trying to allocate {self.nsamples} data with fixed length {unified_length}, \
-            but only {len(self.dataloader)} samples satisfy your setting. You may choose smaller 'model.seqlen' value."
+            but only {len(self.dataloader)} samples are found. Please use smaller 'self.pad_max_length' value."
             )
 
-    @torch.no_grad()
-    def initialize_inp_buffersize(self):
-        # Run a forward and generate proper buffer tensor
-        # Thus, no need to pass hidden_states dimension parameters of model.config
-        # e.g. OPT's hidden_states dimension can be called by model.config.hidden_size
-        # but mpt's hidden_states dimension can be called by model.config.d_model
-        def forward(layer, hidden_states, **kwargs):
-            # inputs[inputs_info['idx']] = input_ids # TODO solve the problem of batchsize!=1
-            logger.info(f"The hidden_states shape along transformers blocks is {hidden_states.shape}.")
-            self.model_hidden_size = hidden_states.shape[-1]
-            raise ValueError
-
-        # Step1: fetch the embeddings and other layers before the transformer stack.
-        for embedding_name, embedding_layer in self.gptq_related_blocks["embeddings"].items():
-            embedding_layer = embedding_layer.to(self.device)
-
-        # Step2: modify the first transformer block's forward function to obtain inputs for calibration
-        self.gptq_related_blocks["transformers"][0] = self.gptq_related_blocks["transformers"][0].to(self.device)
-        forward_cache = self.gptq_related_blocks["transformers"][0].forward
-        self.gptq_related_blocks["transformers"][0].forward = partial(
-            forward, self.gptq_related_blocks["transformers"][0]
-        )
-
-        # Step3: run forward to obtain calibration datasets
-        logger.info("Collecting calibration inputs...")
-        for batch in self.dataloader:
-            batch = move_input_to_device(batch, self.device)
-            try:
-                if isinstance(batch, tuple) or isinstance(batch, list):
-                    self.model(batch[0])
-                elif isinstance(batch, dict):
-                    self.model(**batch)
-                else:
-                    self.model(batch.to(self.device))
-            except ValueError:
-                break
-
-        # Step 4: restore original forward function, relocate layers back to cpu.
-        self.gptq_related_blocks["transformers"][0].forward = forward_cache
-        self.gptq_related_blocks["transformers"][0] = self.gptq_related_blocks["transformers"][0].cpu()
-        for embedding_name, embedding_layer in self.gptq_related_blocks["embeddings"].items():
-            embedding_layer.to(self.device)
-        torch.cuda.empty_cache()
-
     def get_full_layer_name(self, sub_layer_name, block_idx):
         transformer_name = self.gptq_related_blocks["transformers_name"]
         return ".".join([transformer_name, str(block_idx), sub_layer_name])
@@ -459,18 +411,12 @@ def forward(layer, hidden_states, **kwargs):
             self.cache["i"] += 1
             for arg in kwargs:
                 # TODO: investigate include parameters
-                if self.use_max_length:
-                    if isinstance(kwargs[arg], torch.Tensor) or arg == "alibi":
-                        self.cache[arg] = kwargs[arg]
-                    else:
-                        continue
-                else:
-                    # each outputs can be different shape, hence also use list to store
-                    if isinstance(kwargs[arg], torch.Tensor) or arg == "alibi":
-                        if self.cache.get(arg, None) is None:
-                            self.cache[arg] = []
-                        self.cache[arg].append(kwargs[arg])
-                    continue
+                # each outputs can be different shape, hence also use list to store
+                if isinstance(kwargs[arg], torch.Tensor) or arg == "alibi":
+                    if self.cache.get(arg, None) is None:
+                        self.cache[arg] = []
+                    self.cache[arg].append(kwargs[arg])
+                continue
             raise ValueError
 
         # Step1: fetch the embeddings and other layers before the transformer stack.
@@ -572,13 +518,9 @@ def tmp(_, inp, out):
                 handles.append(sub_layers[layer_name].register_forward_hook(add_batch(layer_name)))
             idx = self.cache.pop("i")
             for j in range(len(self.dataloader)):
-                if self.use_max_length:
-                    # self.inp[j] shape: [seq_len, hidden_size]
-                    self.out[j] = transformer_block(self.inp[j].unsqueeze(0), **self.cache)[0]
-                else:
-                    # self.inp[j] shape: [1, seq_len, hidden_size] (batchsize is 1 by default)
-                    cache_batch = self.gather_single_batch_from_dict(self.cache, j)
-                    self.out[j] = transformer_block(self.inp[j], **cache_batch)[0]
+                # self.inp[j] shape: [1, seq_len, hidden_size] (batchsize is 1 by default)
+                cache_batch = self.gather_single_batch_from_dict(self.cache, j)
+                self.out[j] = transformer_block(self.inp[j], **cache_batch)[0]
             self.cache["i"] = idx
             for h in handles:
                 h.remove()
@@ -607,13 +549,9 @@ def tmp(_, inp, out):
             # Step 2.5: replace output data with quantized weights
             idx = self.cache.pop("i")
             for j in range(len(self.dataloader)):
-                if self.use_max_length:
-                    # self.inp[j] shape: [seq_len, hidden_size]
-                    self.out[j] = transformer_block(self.inp[j].unsqueeze(0), **self.cache)[0]
-                else:
-                    # self.inp[j] shape: [1, seq_len, hidden_size] (batchsize is 1 by default)
-                    cache_batch = self.gather_single_batch_from_dict(self.cache, j)
-                    self.out[j] = transformer_block(self.inp[j], **cache_batch)[0]
+                # self.inp[j] shape: [1, seq_len, hidden_size] (batchsize is 1 by default)
+                cache_batch = self.gather_single_batch_from_dict(self.cache, j)
+                self.out[j] = transformer_block(self.inp[j], **cache_batch)[0]
             self.cache["i"] = idx
             self.gptq_related_blocks["transformers"][block_idx] = transformer_block.cpu()
             del gptq_for_this_block

neural_compressor/adaptor/torch_utils/weight_only.py

@@ -469,13 +469,15 @@ def rtn_quantize(
     return model
 
 
-def gptq_quantize(model, weight_config={}, dataloader=None, nsamples=128, use_max_length=True, device=None):
+def gptq_quantize(
+    model, weight_config={}, dataloader=None, nsamples=128, use_max_length=True, pad_max_length=2048, device=None
+):
     """Run weight-only quantization with."""
     # TODO: unify weight_config keys, add docstring, and support default config
     assert isinstance(model, torch.nn.Module), "only support torch module"
     from .gptq import GPTQuantizer
 
-    gptq_quantizer = GPTQuantizer(model, weight_config, dataloader, nsamples, use_max_length, device)
+    gptq_quantizer = GPTQuantizer(model, weight_config, dataloader, nsamples, use_max_length, pad_max_length, device)
     fp32_modified_model, gptq_config = gptq_quantizer.execute_quantization()
     logger.info("GPTQ quantizing done.")
     return fp32_modified_model, gptq_config

test/adaptor/pytorch_adaptor/test_weight_only_adaptor.py

@@ -68,7 +68,6 @@ def setUpClass(self):
         self.gptj_no_jit = transformers.AutoModelForCausalLM.from_pretrained(
             "hf-internal-testing/tiny-random-GPTJForCausalLM",
         )
-        self.gptj.seqlen = 512
         self.llm_dataloader = LLMDataLoader()
         self.lm_input = torch.ones([1, 10], dtype=torch.long)
 
@@ -502,7 +501,7 @@ def __iter__(self):
                 },
             },
             recipes={
-                "gptq_args": {"percdamp": 0.01, "act_order": False},
+                "gptq_args": {"percdamp": 0.01, "act_order": False, "use_max_length": True, "pad_max_length": 512},
             },
         )
 
@@ -608,7 +607,7 @@ def __iter__(self):
                 },
             },
             recipes={
-                "gptq_args": {"percdamp": 0.01, "act_order": False, "use_max_length": True},
+                "gptq_args": {"percdamp": 0.01, "act_order": False, "use_max_length": False, "pad_max_length": 512},
             },
         )