feat(server): Reworking the quantization script so it's still universal (not llama specific) (#587)

but should work on more configurations (no need for 2 GPUs, less RAM
usage).


# What does this PR do?

Reworking the quantization script so it's still universal (not llama
specific)

but should work on more configurations (no need for 2 GPUs, less RAM
usage).

Still need to investigate the potential differences in quantization
results.


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Fixes # (issue)


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server/text_generation_server/cli.py

@@ -194,6 +194,8 @@ def quantize(
     percdamp: float = 0.01,
     act_order: bool = False,
 ):
+    if revision is None:
+        revision = "main"
     download_weights(
         model_id=model_id,
         revision=revision,
@@ -207,6 +209,7 @@ def quantize(
         bits=4,
         groupsize=128,
         output_dir=output_dir,
+        revision=revision,
         trust_remote_code=trust_remote_code,
         upload_to_model_id=upload_to_model_id,
         percdamp=percdamp,

server/text_generation_server/utils/gptq/quantize.py

@@ -13,6 +13,9 @@ import transformers
 from huggingface_hub import HfApi
 import numpy as np
 import torch
+from accelerate import init_empty_weights
+from text_generation_server.utils import initialize_torch_distributed, Weights
+from text_generation_server.utils.hub import weight_files
 from text_generation_server.utils.gptq.quant_linear import QuantLinear
 from loguru import logger
 from typing import Optional
@@ -38,7 +41,6 @@ class Quantizer(nn.Module):
         maxshrink=0.8,
         trits=False,
     ):
-
         self.maxq = torch.tensor(2**bits - 1)
         self.perchannel = perchannel
         self.sym = sym
@@ -600,6 +602,8 @@ def sequential(
     nsamples,
     bits,
     groupsize,
+    *,
+    hooks,
     percdamp=0.01,
     sym: bool = False,
     act_order: bool = False,
@@ -637,7 +641,7 @@ def sequential(
     layers[0] = Catcher(layers[0])
     for batch in dataloader:
         try:
-            model(batch[0])
+            model(batch[0].cuda())
         except ValueError:
             pass
     layers[0] = layers[0].module
@@ -646,6 +650,8 @@ def sequential(
     # model.model.embed_tokens = model.model.embed_tokens.cpu()
     # model.model.norm = model.model.norm.cpu()
     torch.cuda.empty_cache()
+    for hook in hooks:
+        hook.remove()
 
     outs = torch.zeros_like(inps)
 
@@ -662,10 +668,8 @@ def sequential(
         print("|       name       | weight_error | fp_inp_SNR | q_inp_SNR | time  |")
         print("+==================+==============+============+===========+=======+")
 
-        from accelerate.hooks import remove_hook_from_submodules
+        layer = layers[i]
-
+        layer.load()
-        layer = layers[i].to(dev)
-        remove_hook_from_submodules(layer)
         full = find_layers(layer)
         sequential = [list(full.keys())]
 
@@ -677,6 +681,7 @@ def sequential(
                 gptq[name].quantizer.configure(
                     bits, perchannel=True, sym=sym, mse=False
                 )
+                pass
 
             def add_batch(name):
                 def tmp(_, inp, out):
@@ -688,7 +693,6 @@ def sequential(
             for name in subset:
                 handles.append(subset[name].register_forward_hook(add_batch(name)))
             for j in range(nsamples):
-
                 outs[j] = layer(inps[j].unsqueeze(0), **extra)[0]
             for h in handles:
                 h.remove()
@@ -714,7 +718,7 @@ def sequential(
         for j in range(nsamples):
             outs[j] = layer(inps[j].unsqueeze(0), **extra)[0]
 
-        layers[i] = layer.cpu()
+        layer.unload()
         del layer
         del gptq
         torch.cuda.empty_cache()
@@ -768,24 +772,136 @@ def pack(model, quantizers, bits, groupsize):
     return model
 
 
+def setdeepattr(module, full_name, tensor):
+    current = module
+    tokens = full_name.split(".")
+    for token in tokens[:-1]:
+        current = getattr(current, token)
+    setattr(current, tokens[-1], tensor)
+
+
+def getdeepattr(module, full_name):
+    current = module
+    tokens = full_name.split(".")
+    for token in tokens:
+        current = getattr(current, token)
+    return current
+
+
+def load_weights_pre_hook(module_name, weights, recursive=False):
+    def inner(module, args):
+        print(f"Pre hook {module_name}")
+        local_params = {}
+        for k, v in module.named_parameters():
+            if not recursive and k.count(".") != 1:
+                continue
+            local_params[k] = v
+        for k, v in module.named_buffers():
+            if not recursive and k.count(".") != 1:
+                continue
+            local_params[k] = v
+
+        for local_param in local_params:
+            current_tensor = getdeepattr(module, local_param)
+            if current_tensor.device == torch.device("meta"):
+                # print(f"Loading {local_param}")
+                if module_name:
+                    tensor_name = f"{module_name}.{local_param}"
+                else:
+                    tensor_name = local_param
+                tensor = weights.get_tensor(tensor_name)
+                setdeepattr(module, local_param, nn.Parameter(tensor))
+            else:
+                setdeepattr(
+                    module,
+                    local_param,
+                    nn.Parameter(current_tensor.to(device=torch.device("cuda:0"))),
+                )
+
+    return inner
+
+
+def load_weights_post_hook(module_name, weights, recursive=False):
+    def inner(module, args, output):
+        print(f"Post hook {module_name}")
+        local_params = {}
+        for k, v in module.named_parameters():
+            if not recursive and k.count(".") != 1:
+                continue
+            local_params[k] = v
+        for k, v in module.named_buffers():
+            if not recursive and k.count(".") != 1:
+                continue
+            local_params[k] = v
+        for local_param in local_params:
+            # print(f"Unloading {local_param}")
+            current_tensor = getdeepattr(module, local_param)
+            setdeepattr(
+                module,
+                local_param,
+                nn.Parameter(current_tensor.to(device=torch.device("cpu"))),
+            )
+        return output
+
+    return inner
+
+
 def quantize(
     model_id: str,
     bits: int,
     groupsize: int,
     output_dir: str,
+    revision: str,
     trust_remote_code: bool,
     upload_to_model_id: Optional[str],
     percdamp: float,
     act_order: bool,
 ):
     print("loading model")
-    model = AutoModelForCausalLM.from_pretrained(
+    config = AutoConfig.from_pretrained(
         model_id,
-        torch_dtype=torch.float16,
-        device_map="balanced_low_0",
         trust_remote_code=trust_remote_code,
     )
+
+    with init_empty_weights():
+        model = AutoModelForCausalLM.from_config(config, torch_dtype=torch.float16)
+    model = model.eval()
+
     print("LOADED model")
+    files = weight_files(model_id, revision, extension=".safetensors")
+    process_group, _, _ = initialize_torch_distributed()
+    weights = Weights(
+        files,
+        device=torch.device("cuda:0"),
+        dtype=torch.float16,
+        process_group=process_group,
+        aliases={"embed_tokens.weight": ["lm_head.weight"]},
+    )
+    hooks = []
+    for name, module in model.named_modules():
+
+        def load(module, name):
+            def _load():
+                load_weights_pre_hook(name, weights, recursive=True)(module, None)
+
+            return _load
+
+        def unload(module, name):
+            def _unload():
+                load_weights_post_hook(name, weights, recursive=True)(
+                    module, None, None
+                )
+
+            return _unload
+
+        module.load = load(module, name)
+        module.unload = unload(module, name)
+        hooks.append(
+            module.register_forward_pre_hook(load_weights_pre_hook(name, weights))
+        )
+        hooks.append(
+            module.register_forward_hook(load_weights_post_hook(name, weights))
+        )
     model.seqlen = 2048
 
     dataset = "wikitext2"
@@ -806,6 +922,7 @@ def quantize(
         groupsize,
         percdamp=percdamp,
         act_order=act_order,
+        hooks=hooks,
     )
     print(time.time() - tick)
 
@@ -858,7 +975,6 @@ def quantize(
     logger.info("Saved tokenizer")
 
     if upload_to_model_id:
-
         api = HfApi()
 
         api.upload_folder(