feat(server): Add inference support for GPTQ (llama + falcon tested) + Quantization script (#438)

Let's start discussing implementation.

- Need to expose the quantization scripts (either included here or add
doc on how to use https://github.com/qwopqwop200/GPTQ-for-LLaMa)
- Make sure GPTQ works for multiple models (priority to Falcon).

Currently it means that every place we use `get_{tensor|sharded}` to
check for quantization.

My idea is to reintegrate as much as possible into `utils/layer.py` by
expanding `load_multi` to be a bit more generic.
This might require some thinking, but ultimately the
`qweight,qzeros,scales,g_idx` should be in a single place, and
independant of bias presence.

# What does this PR do?

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Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
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---------

Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>

Dockerfile

@@ -159,6 +159,11 @@ COPY --from=builder /usr/src/target/release/text-generation-router /usr/local/bi
 # Install launcher
 COPY --from=builder /usr/src/target/release/text-generation-launcher /usr/local/bin/text-generation-launcher
 
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
+        build-essential \
+        g++ \
+        && rm -rf /var/lib/apt/lists/*
+
 # AWS Sagemaker compatbile image
 FROM base as sagemaker
 

server/requirements.txt

@@ -1,21 +1,21 @@
 backoff==2.2.1 ; python_version >= "3.9" and python_version < "4.0"
-bitsandbytes==0.38.1 ; python_version >= "3.9" and python_version < "4.0"
 certifi==2023.5.7 ; python_version >= "3.9" and python_version < "4.0"
 charset-normalizer==3.1.0 ; python_version >= "3.9" and python_version < "4.0"
 click==8.1.3 ; python_version >= "3.9" and python_version < "4.0"
-colorama==0.4.6 ; python_version >= "3.9" and python_version < "4.0" and sys_platform == "win32" or python_version >= "3.9" and python_version < "4.0" and platform_system == "Windows"
+colorama==0.4.6 ; python_version >= "3.9" and python_version < "4.0" and (sys_platform == "win32" or platform_system == "Windows")
-deprecated==1.2.13 ; python_version >= "3.9" and python_version < "4.0"
+deprecated==1.2.14 ; python_version >= "3.9" and python_version < "4.0"
-filelock==3.12.0 ; python_version >= "3.9" and python_version < "4.0"
+filelock==3.12.2 ; python_version >= "3.9" and python_version < "4.0"
-fsspec==2023.5.0 ; python_version >= "3.9" and python_version < "4.0"
+fsspec==2023.6.0 ; python_version >= "3.9" and python_version < "4.0"
-googleapis-common-protos==1.59.0 ; python_version >= "3.9" and python_version < "4.0"
+googleapis-common-protos==1.59.1 ; python_version >= "3.9" and python_version < "4.0"
 grpc-interceptor==0.15.2 ; python_version >= "3.9" and python_version < "4.0"
-grpcio-reflection==1.55.0 ; python_version >= "3.9" and python_version < "4.0"
+grpcio-reflection==1.54.2 ; python_version >= "3.9" and python_version < "4.0"
-grpcio-status==1.55.0 ; python_version >= "3.9" and python_version < "4.0"
+grpcio-status==1.54.2 ; python_version >= "3.9" and python_version < "4.0"
-grpcio==1.55.0 ; python_version >= "3.9" and python_version < "4.0"
+grpcio==1.54.2 ; python_version >= "3.9" and python_version < "4.0"
 hf-transfer==0.1.3 ; python_version >= "3.9" and python_version < "4.0"
 huggingface-hub==0.14.1 ; python_version >= "3.9" and python_version < "4.0"
-idna==3.4 ; python_version >= "3.9" and python_version < "4"
+idna==3.4 ; python_version >= "3.9" and python_version < "4.0"
 loguru==0.6.0 ; python_version >= "3.9" and python_version < "4.0"
+numpy==1.24.3 ; python_version >= "3.9" and python_version < "4.0"
 opentelemetry-api==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
 opentelemetry-exporter-otlp-proto-grpc==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
 opentelemetry-exporter-otlp-proto-http==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
@@ -26,17 +26,18 @@ opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
 opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
 opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "4.0"
 packaging==23.1 ; python_version >= "3.9" and python_version < "4.0"
-protobuf==4.23.1 ; python_version >= "3.9" and python_version < "4.0"
+protobuf==4.23.2 ; python_version >= "3.9" and python_version < "4.0"
 pyyaml==6.0 ; python_version >= "3.9" and python_version < "4.0"
+regex==2023.6.3 ; python_version >= "3.9" and python_version < "4.0"
 requests==2.31.0 ; python_version >= "3.9" and python_version < "4.0"
 safetensors==0.3.1 ; python_version >= "3.9" and python_version < "4.0"
 sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "4.0"
 setuptools==67.8.0 ; python_version >= "3.9" and python_version < "4.0"
 tokenizers==0.13.3 ; python_version >= "3.9" and python_version < "4.0"
-transformers==4.29.2 ; python_version >= "3.9" and python_version < "4.0"
 tqdm==4.65.0 ; python_version >= "3.9" and python_version < "4.0"
+transformers==4.30.2 ; python_version >= "3.9" and python_version < "4.0"
 typer==0.6.1 ; python_version >= "3.9" and python_version < "4.0"
-typing-extensions==4.6.0 ; python_version >= "3.9" and python_version < "4.0"
+typing-extensions==4.6.3 ; python_version >= "3.9" and python_version < "4.0"
-urllib3==2.0.2 ; python_version >= "3.9" and python_version < "4.0"
+urllib3==2.0.3 ; python_version >= "3.9" and python_version < "4.0"
 win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "4.0" and sys_platform == "win32"
 wrapt==1.15.0 ; python_version >= "3.9" and python_version < "4.0"

server/text_generation_server/cli.py

@@ -151,5 +151,37 @@ def download_weights(
         utils.convert_files(local_pt_files, local_st_files)
 
 
+@app.command()
+def quantize(
+    model_id: str,
+    output_dir: str,
+    revision: Optional[str] = None,
+    logger_level: str = "INFO",
+    json_output: bool = False,
+    trust_remote_code: bool = False,
+    upload_to_model_id: Optional[str] = None,
+    percdamp: float = 0.01,
+    act_order: bool = False,
+):
+    download_weights(
+        model_id=model_id,
+        revision=revision,
+        logger_level=logger_level,
+        json_output=json_output,
+    )
+    from text_generation_server.utils.gptq.quantize import quantize
+
+    quantize(
+        model_id=model_id,
+        bits=4,
+        groupsize=128,
+        output_dir=output_dir,
+        trust_remote_code=trust_remote_code,
+        upload_to_model_id=upload_to_model_id,
+        percdamp=percdamp,
+        act_order=act_order,
+    )
+
+
 if __name__ == "__main__":
     app()

server/text_generation_server/models/__init__.py

@@ -246,6 +246,10 @@ def get_model(
 
     if sharded:
         raise ValueError("sharded is not supported for AutoModel")
+    if quantize == "gptq":
+        raise ValueError(
+            "gptq quantization is not supported for AutoModel, you can try to quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
+        )
 
     if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
         return CausalLM(

server/text_generation_server/models/custom_modeling/flash_neox_modeling.py

@@ -42,7 +42,8 @@ from text_generation_server.utils.layers import (
 
 
 def load_row(config, prefix: str, weights, bias: bool):
-    weight = weights.get_sharded(f"{prefix}.weight", dim=1)
+    weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
+
     if bias and weights.process_group.rank() == 0:
         # Rank is only on the first rank process
         bias = weights.get_tensor(f"{prefix}.bias")
@@ -57,9 +58,9 @@ def load_row(config, prefix: str, weights, bias: bool):
 
 
 def load_qkv(config, prefix: str, weights, num_heads, head_size, hidden_size):
-    weight = weights.get_sharded(f"{prefix}.weight", dim=0)
+    weight = weights.get_multi_weights_col([prefix], quantize=config.quantize, dim=0)
-    bias = weights.get_sharded(f"{prefix}.bias", dim=0)
+    if isinstance(weight, torch.Tensor):
-
+        # Only on non quantized versions
         weight = (
             weight.view(
                 num_heads,
@@ -70,6 +71,8 @@ def load_qkv(config, prefix: str, weights, num_heads, head_size, hidden_size):
             .permute(1, 0, 2, 3)
             .reshape(-1, hidden_size)
         )
+
+    bias = weights.get_sharded(f"{prefix}.bias", dim=0)
     bias = bias.view(num_heads, 3, head_size).permute(1, 0, 2).reshape(-1)
 
     linear = get_linear(weight, bias, config.quantize)

server/text_generation_server/models/custom_modeling/flash_rw_modeling.py

@@ -21,7 +21,8 @@ from text_generation_server.utils.layers import (
 
 
 def load_row(config, prefix: str, weights, bias: bool):
-    weight = weights.get_sharded(f"{prefix}.weight", dim=1)
+    weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
+
     if bias and weights.process_group.rank() == 0:
         # Rank is only on the first rank process
         bias = weights.get_tensor(f"{prefix}.bias")

server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py

@@ -21,6 +21,81 @@ from text_generation_server.utils.layers import (
 def load_multi_mqa(
     config, prefix: str, weights, bias: bool, head_size, num_heads, hidden_size
 ):
+
+    if config.quantize == "gptq":
+        return _load_multi_mqa_gptq(
+            config, prefix, weights, bias, head_size, num_heads, hidden_size
+        )
+    else:
+        return _load_multi_mqa(
+            config, prefix, weights, bias, head_size, num_heads, hidden_size
+        )
+
+
+def _load_multi_mqa_gptq(
+    config, prefix: str, weights, bias: bool, head_size, num_heads, hidden_size
+):
+    if any("c_attn" in k for k in weights.routing.keys()) and not config.transpose:
+        world_size = weights.process_group.size()
+        rank = weights.process_group.rank()
+
+        slice_ = weights._get_slice(f"{prefix}.c_attn.qweight")
+        shape = slice_.get_shape()
+        block_size = (shape[1] - 2 * head_size) // world_size
+        start = rank * block_size
+        stop = (rank + 1) * block_size
+        assert (shape[1] - 2 * head_size) % world_size == 0
+        q_tensor = slice_[:, start:stop]
+        kv_tensor = slice_[:, -2 * head_size :]
+        qweight = torch.cat([q_tensor, kv_tensor], dim=1)
+
+        slice_ = weights._get_slice(f"{prefix}.c_attn.scales")
+        shape = slice_.get_shape()
+        block_size = (shape[1] - 2 * head_size) // world_size
+        start = rank * block_size
+        stop = (rank + 1) * block_size
+        assert (shape[1] - 2 * head_size) % world_size == 0
+        q_tensor = slice_[:, start:stop]
+        kv_tensor = slice_[:, -2 * head_size :]
+        scales = torch.cat([q_tensor, kv_tensor], dim=1)
+
+        slice_ = weights._get_slice(f"{prefix}.c_attn.qzeros")
+        shape = slice_.get_shape()
+        block_size = (shape[1] - (2 * head_size) * 4 // 32) // world_size
+        start = rank * block_size
+        stop = (rank + 1) * block_size
+        assert 2 * head_size % (32 // 4) == 0
+        q_tensor = slice_[:, start:stop]
+        kv_tensor = slice_[:, -2 * head_size * 4 // 32 :]
+        qzeros = torch.cat([q_tensor, kv_tensor], dim=1)
+
+        g_idx = weights.get_tensor(f"{prefix}.c_attn.g_idx")
+        bits = weights.get_tensor("gptq_bits").item()
+        groupsize = weights.get_tensor("gptq_groupsize").item()
+
+        weight = (qweight, qzeros, scales, g_idx, bits, groupsize)
+
+        if bias:
+            slice_ = weights._get_slice(f"{prefix}.c_attn.bias")
+            shape = slice_.get_shape()
+            block_size = (shape[0] - 2 * head_size) // world_size
+            assert (shape[0] - 2 * head_size) % world_size == 0
+            q_tensor = slice_[start:stop]
+            start = rank * block_size
+            stop = (rank + 1) * block_size
+            q_tensor = slice_[start:stop]
+            kv_tensor = slice_[-2 * head_size :]
+            bias = torch.cat([q_tensor, kv_tensor], dim=0)
+
+        return TensorParallelColumnLinear(get_linear(weight, bias, config.quantize))
+    else:
+        raise NotImplementedError("Gptq loading with santacoder is not implemented")
+
+
+def _load_multi_mqa(
+    config, prefix: str, weights, bias: bool, head_size, num_heads, hidden_size
+):
+
     if any("c_attn" in k for k in weights.routing.keys()):
         slice_ = weights._get_slice(f"{prefix}.c_attn.weight")
         shape = slice_.get_shape()
@@ -92,7 +167,9 @@ def load_col(config, prefix: str, weights, bias: bool):
     if config.transpose:
         weight = weights.get_sharded(f"{prefix}.weight", dim=1).T
     else:
-        weight = weights.get_sharded(f"{prefix}.weight", dim=0)
+        weight = weights.get_multi_weights_col(
+            [prefix], quantize=config.quantize, dim=0
+        )
 
     if bias:
         bias = weights.get_sharded(f"{prefix}.bias", dim=0)
@@ -105,7 +182,7 @@ def load_row(config, prefix: str, weights, bias: bool):
     if config.transpose:
         weight = weights.get_sharded(f"{prefix}.weight", dim=0).T
     else:
-        weight = weights.get_sharded(f"{prefix}.weight", dim=1)
+        weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
 
     if bias and weights.process_group.rank() == 0:
         # Rank is only on the first rank process

server/text_generation_server/models/flash_llama.py

@@ -3,7 +3,7 @@ import torch.distributed
 
 from opentelemetry import trace
 from transformers import AutoConfig
-from transformers.models.llama import LlamaTokenizer
+from transformers.models.llama import LlamaTokenizer, LlamaTokenizerFast
 from typing import Optional
 
 from text_generation_server.models import FlashCausalLM
@@ -34,6 +34,7 @@ class FlashLlama(FlashCausalLM):
         else:
             raise NotImplementedError("FlashLlama is only available on GPU")
 
+        try:
             tokenizer = LlamaTokenizer.from_pretrained(
                 model_id,
                 revision=revision,
@@ -41,6 +42,14 @@ class FlashLlama(FlashCausalLM):
                 truncation_side="left",
                 trust_remote_code=trust_remote_code,
             )
+        except Exception:
+            tokenizer = LlamaTokenizerFast.from_pretrained(
+                model_id,
+                revision=revision,
+                padding_side="left",
+                truncation_side="left",
+                trust_remote_code=trust_remote_code,
+            )
 
         config = AutoConfig.from_pretrained(
             model_id, revision=revision, trust_remote_code=trust_remote_code

server/text_generation_server/utils/gptq/custom_autotune.py

@@ -0,0 +1,261 @@
+# https://github.com/fpgaminer/GPTQ-triton
+"""
+Mostly the same as the autotuner in Triton, but with a few changes like using 40 runs instead of 100.
+"""
+
+import builtins
+import math
+import time
+from typing import Dict
+
+import triton
+
+
+class Autotuner(triton.KernelInterface):
+    def __init__(
+        self,
+        fn,
+        arg_names,
+        configs,
+        key,
+        reset_to_zero,
+        prune_configs_by: Dict = None,
+        nearest_power_of_two: bool = False,
+    ):
+        """
+        :param prune_configs_by: a dict of functions that are used to prune configs, fields:
+                'perf_model': performance model used to predicate running time with different configs, returns running time
+                'top_k': number of configs to bench
+                'prune_num_stages_by'(optional): a function used to prune num_stages. It take configs:List[Config] as its input, and returns pruned configs.
+                'nearest_power_of_two'(optional): whether to round key arguments to the nearest power of two when caching tuning results
+        """
+        if not configs:
+            self.configs = [triton.Config({}, num_warps=4, num_stages=2)]
+        else:
+            self.configs = configs
+        self.key_idx = [arg_names.index(k) for k in key]
+        self.nearest_power_of_two = nearest_power_of_two
+        self.cache = {}
+        # hook to reset all required tensor to zeros before relaunching a kernel
+        self.hook = lambda args: 0
+        if reset_to_zero is not None:
+            self.reset_idx = [arg_names.index(k) for k in reset_to_zero]
+
+            def _hook(args):
+                for i in self.reset_idx:
+                    args[i].zero_()
+
+            self.hook = _hook
+        self.arg_names = arg_names
+        # prune configs
+        if prune_configs_by:
+            perf_model, top_k = (
+                prune_configs_by["perf_model"],
+                prune_configs_by["top_k"],
+            )
+            if "early_config_prune" in prune_configs_by:
+                early_config_prune = prune_configs_by["early_config_prune"]
+        else:
+            perf_model, top_k, early_config_prune = None, None, None
+        self.perf_model, self.configs_top_k = perf_model, top_k
+        self.early_config_prune = early_config_prune
+        self.fn = fn
+
+    def _bench(self, *args, config, **meta):
+        # check for conflicts, i.e. meta-parameters both provided
+        # as kwargs and by the autotuner
+        conflicts = meta.keys() & config.kwargs.keys()
+        if conflicts:
+            raise ValueError(
+                f"Conflicting meta-parameters: {', '.join(conflicts)}."
+                " Make sure that you don't re-define auto-tuned symbols."
+            )
+        # augment meta-parameters with tunable ones
+        current = dict(meta, **config.kwargs)
+
+        def kernel_call():
+            if config.pre_hook:
+                config.pre_hook(self.nargs)
+            self.hook(args)
+            self.fn.run(
+                *args,
+                num_warps=config.num_warps,
+                num_stages=config.num_stages,
+                **current,
+            )
+
+        try:
+            # In testings using only 40 reps seems to be close enough and it appears to be what PyTorch uses
+            # PyTorch also sets fast_flush to True, but I didn't see any speedup so I'll leave the default
+            return triton.testing.do_bench(
+                kernel_call, percentiles=(0.5, 0.2, 0.8), rep=40
+            )
+        except triton.compiler.OutOfResources:
+            return (float("inf"), float("inf"), float("inf"))
+
+    def run(self, *args, **kwargs):
+        self.nargs = dict(zip(self.arg_names, args))
+        if len(self.configs) > 1:
+            key = tuple(args[i] for i in self.key_idx)
+
+            # This reduces the amount of autotuning by rounding the keys to the nearest power of two
+            # In my testing this gives decent results, and greatly reduces the amount of tuning required
+            if self.nearest_power_of_two:
+                key = tuple([2 ** int(math.log2(x) + 0.5) for x in key])
+
+            if key not in self.cache:
+                # prune configs
+                pruned_configs = self.prune_configs(kwargs)
+                bench_start = time.time()
+                timings = {
+                    config: self._bench(*args, config=config, **kwargs)
+                    for config in pruned_configs
+                }
+                bench_end = time.time()
+                self.bench_time = bench_end - bench_start
+                self.cache[key] = builtins.min(timings, key=timings.get)
+                self.hook(args)
+                self.configs_timings = timings
+            config = self.cache[key]
+        else:
+            config = self.configs[0]
+        self.best_config = config
+        if config.pre_hook is not None:
+            config.pre_hook(self.nargs)
+        return self.fn.run(
+            *args,
+            num_warps=config.num_warps,
+            num_stages=config.num_stages,
+            **kwargs,
+            **config.kwargs,
+        )
+
+    def prune_configs(self, kwargs):
+        pruned_configs = self.configs
+        if self.early_config_prune:
+            pruned_configs = self.early_config_prune(self.configs, self.nargs)
+        if self.perf_model:
+            top_k = self.configs_top_k
+            if isinstance(top_k, float) and top_k <= 1.0:
+                top_k = int(len(self.configs) * top_k)
+            if len(pruned_configs) > top_k:
+                est_timing = {
+                    config: self.perf_model(
+                        **self.nargs,
+                        **kwargs,
+                        **config.kwargs,
+                        num_stages=config.num_stages,
+                        num_warps=config.num_warps,
+                    )
+                    for config in pruned_configs
+                }
+                pruned_configs = sorted(est_timing.keys(), key=lambda x: est_timing[x])[
+                    :top_k
+                ]
+        return pruned_configs
+
+    def warmup(self, *args, **kwargs):
+        self.nargs = dict(zip(self.arg_names, args))
+        for config in self.prune_configs(kwargs):
+            self.fn.warmup(
+                *args,
+                num_warps=config.num_warps,
+                num_stages=config.num_stages,
+                **kwargs,
+                **config.kwargs,
+            )
+        self.nargs = None
+
+
+def autotune(
+    configs, key, prune_configs_by=None, reset_to_zero=None, nearest_power_of_two=False
+):
+    """
+    Decorator for auto-tuning a :code:`triton.jit`'d function.
+    .. highlight:: python
+    .. code-block:: python
+            @triton.autotune(configs=[
+                    triton.Config(meta={'BLOCK_SIZE': 128}, num_warps=4),
+                    triton.Config(meta={'BLOCK_SIZE': 1024}, num_warps=8),
+                    ],
+                    key=['x_size'] # the two above configs will be evaluated anytime
+                                                    # the value of x_size changes
+            )
+            @triton.jit
+            def kernel(x_ptr, x_size, **META):
+                    BLOCK_SIZE = META['BLOCK_SIZE']
+    :note: When all the configurations are evaluated, the kernel will run multiple time.
+                    This means that whatever value the kernel updates will be updated multiple times.
+                    To avoid this undesired behavior, you can use the `reset_to_zero` argument, which
+                    reset the value of the provided tensor to `zero` before running any configuration.
+    :param configs: a list of :code:`triton.Config` objects
+    :type configs: list[triton.Config]
+    :param key: a list of argument names whose change in value will trigger the evaluation of all provided configs.
+    :type key: list[str]
+    :param prune_configs_by: a dict of functions that are used to prune configs, fields:
+            'perf_model': performance model used to predicate running time with different configs, returns running time
+            'top_k': number of configs to bench
+            'early_config_prune'(optional): a function used to do early prune (eg, num_stages). It take configs:List[Config] as its input, and returns pruned configs.
+    :param reset_to_zero: a list of argument names whose value will be reset to zero before evaluating any configs.
+    :type reset_to_zero: list[str]
+    """
+
+    def decorator(fn):
+        return Autotuner(
+            fn,
+            fn.arg_names,
+            configs,
+            key,
+            reset_to_zero,
+            prune_configs_by,
+            nearest_power_of_two,
+        )
+
+    return decorator
+
+
+def matmul248_kernel_config_pruner(configs, nargs):
+    """
+    The main purpose of this function is to shrink BLOCK_SIZE_* when the corresponding dimension is smaller.
+    """
+    m = max(2 ** int(math.ceil(math.log2(nargs["M"]))), 16)
+    n = max(2 ** int(math.ceil(math.log2(nargs["N"]))), 16)
+    k = max(2 ** int(math.ceil(math.log2(nargs["K"]))), 16)
+
+    used = set()
+    for config in configs:
+        block_size_m = min(m, config.kwargs["BLOCK_SIZE_M"])
+        block_size_n = min(n, config.kwargs["BLOCK_SIZE_N"])
+        block_size_k = min(k, config.kwargs["BLOCK_SIZE_K"])
+        group_size_m = config.kwargs["GROUP_SIZE_M"]
+
+        if (
+            block_size_m,
+            block_size_n,
+            block_size_k,
+            group_size_m,
+            config.num_stages,
+            config.num_warps,
+        ) in used:
+            continue
+
+        used.add(
+            (
+                block_size_m,
+                block_size_n,
+                block_size_k,
+                group_size_m,
+                config.num_stages,
+                config.num_warps,
+            )
+        )
+        yield triton.Config(
+            {
+                "BLOCK_SIZE_M": block_size_m,
+                "BLOCK_SIZE_N": block_size_n,
+                "BLOCK_SIZE_K": block_size_k,
+                "GROUP_SIZE_M": group_size_m,
+            },
+            num_stages=config.num_stages,
+            num_warps=config.num_warps,
+        )

server/text_generation_server/utils/gptq/quant_linear.py

@@ -0,0 +1,359 @@
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.cuda.amp import custom_bwd, custom_fwd
+
+try:
+    import triton
+    import triton.language as tl
+    from . import custom_autotune
+
+    # code based https://github.com/fpgaminer/GPTQ-triton
+    @custom_autotune.autotune(
+        configs=[
+            triton.Config(
+                {
+                    "BLOCK_SIZE_M": 64,
+                    "BLOCK_SIZE_N": 256,
+                    "BLOCK_SIZE_K": 32,
+                    "GROUP_SIZE_M": 8,
+                },
+                num_stages=4,
+                num_warps=4,
+            ),
+            triton.Config(
+                {
+                    "BLOCK_SIZE_M": 128,
+                    "BLOCK_SIZE_N": 128,
+                    "BLOCK_SIZE_K": 32,
+                    "GROUP_SIZE_M": 8,
+                },
+                num_stages=4,
+                num_warps=4,
+            ),
+            triton.Config(
+                {
+                    "BLOCK_SIZE_M": 64,
+                    "BLOCK_SIZE_N": 128,
+                    "BLOCK_SIZE_K": 32,
+                    "GROUP_SIZE_M": 8,
+                },
+                num_stages=4,
+                num_warps=4,
+            ),
+            triton.Config(
+                {
+                    "BLOCK_SIZE_M": 128,
+                    "BLOCK_SIZE_N": 32,
+                    "BLOCK_SIZE_K": 32,
+                    "GROUP_SIZE_M": 8,
+                },
+                num_stages=4,
+                num_warps=4,
+            ),
+            triton.Config(
+                {
+                    "BLOCK_SIZE_M": 64,
+                    "BLOCK_SIZE_N": 64,
+                    "BLOCK_SIZE_K": 32,
+                    "GROUP_SIZE_M": 8,
+                },
+                num_stages=4,
+                num_warps=4,
+            ),
+            triton.Config(
+                {
+                    "BLOCK_SIZE_M": 64,
+                    "BLOCK_SIZE_N": 128,
+                    "BLOCK_SIZE_K": 32,
+                    "GROUP_SIZE_M": 8,
+                },
+                num_stages=2,
+                num_warps=8,
+            ),
+            triton.Config(
+                {
+                    "BLOCK_SIZE_M": 64,
+                    "BLOCK_SIZE_N": 64,
+                    "BLOCK_SIZE_K": 64,
+                    "GROUP_SIZE_M": 8,
+                },
+                num_stages=3,
+                num_warps=8,
+            ),
+            triton.Config(
+                {
+                    "BLOCK_SIZE_M": 32,
+                    "BLOCK_SIZE_N": 32,
+                    "BLOCK_SIZE_K": 128,
+                    "GROUP_SIZE_M": 8,
+                },
+                num_stages=2,
+                num_warps=4,
+            ),
+        ],
+        key=["M", "N", "K"],
+        nearest_power_of_two=True,
+        prune_configs_by={
+            "early_config_prune": custom_autotune.matmul248_kernel_config_pruner,
+            "perf_model": None,
+            "top_k": None,
+        },
+    )
+    @triton.jit
+    def matmul_248_kernel(
+        a_ptr,
+        b_ptr,
+        c_ptr,
+        scales_ptr,
+        zeros_ptr,
+        g_ptr,
+        M,
+        N,
+        K,
+        bits,
+        maxq,
+        stride_am,
+        stride_ak,
+        stride_bk,
+        stride_bn,
+        stride_cm,
+        stride_cn,
+        stride_scales,
+        stride_zeros,
+        BLOCK_SIZE_M: tl.constexpr,
+        BLOCK_SIZE_N: tl.constexpr,
+        BLOCK_SIZE_K: tl.constexpr,
+        GROUP_SIZE_M: tl.constexpr,
+    ):
+        """
+        Compute the matrix multiplication C = A x B.
+        A is of shape (M, K) float16
+        B is of shape (K//8, N) int32
+        C is of shape (M, N) float16
+        scales is of shape (G, N) float16
+        zeros is of shape (G, N) float16
+        g_ptr is of shape (K) int32
+        """
+        infearure_per_bits = 32 // bits
+
+        pid = tl.program_id(axis=0)
+        num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
+        num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+        num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
+        num_pid_in_group = GROUP_SIZE_M * num_pid_n
+        group_id = pid // num_pid_in_group
+        first_pid_m = group_id * GROUP_SIZE_M
+        group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
+        pid_m = first_pid_m + (pid % group_size_m)
+        pid_n = (pid % num_pid_in_group) // group_size_m
+
+        offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+        offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+        offs_k = tl.arange(0, BLOCK_SIZE_K)
+        a_ptrs = a_ptr + (
+            offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
+        )  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
+        a_mask = offs_am[:, None] < M
+        # b_ptrs is set up such that it repeats elements along the K axis 8 times
+        b_ptrs = b_ptr + (
+            (offs_k[:, None] // infearure_per_bits) * stride_bk
+            + offs_bn[None, :] * stride_bn
+        )  # (BLOCK_SIZE_K, BLOCK_SIZE_N)
+        g_ptrs = g_ptr + offs_k
+        # shifter is used to extract the N bits of each element in the 32-bit word from B
+        scales_ptrs = scales_ptr + offs_bn[None, :]
+        zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)
+
+        shifter = (offs_k % infearure_per_bits) * bits
+        zeros_shifter = (offs_bn % infearure_per_bits) * bits
+        accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+
+        for k in range(0, num_pid_k):
+            g_idx = tl.load(g_ptrs)
+
+            # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
+            scales = tl.load(
+                scales_ptrs + g_idx[:, None] * stride_scales
+            )  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+            zeros = tl.load(
+                zeros_ptrs + g_idx[:, None] * stride_zeros
+            )  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+
+            zeros = (zeros >> zeros_shifter[None, :]) & maxq
+            zeros = zeros + 1
+
+            a = tl.load(a_ptrs, mask=a_mask, other=0.0)  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
+            b = tl.load(b_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
+
+            # Now we need to unpack b (which is N-bit values) into 32-bit values
+            b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
+            b = (b - zeros) * scales  # Scale and shift
+
+            accumulator += tl.dot(a, b)
+            a_ptrs += BLOCK_SIZE_K
+            b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
+            g_ptrs += BLOCK_SIZE_K
+
+        c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
+        c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
+        tl.store(c_ptrs, accumulator, mask=c_mask)
+
+except:
+    print("triton not installed.")
+
+
+def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
+    with torch.cuda.device(input.device):
+        output = torch.empty(
+            (input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16
+        )
+        grid = lambda META: (
+            triton.cdiv(input.shape[0], META["BLOCK_SIZE_M"])
+            * triton.cdiv(qweight.shape[1], META["BLOCK_SIZE_N"]),
+        )
+        matmul_248_kernel[grid](
+            input,
+            qweight,
+            output,
+            scales,
+            qzeros,
+            g_idx,
+            input.shape[0],
+            qweight.shape[1],
+            input.shape[1],
+            bits,
+            maxq,
+            input.stride(0),
+            input.stride(1),
+            qweight.stride(0),
+            qweight.stride(1),
+            output.stride(0),
+            output.stride(1),
+            scales.stride(0),
+            qzeros.stride(0),
+        )
+        return output
+
+
+class QuantLinearFunction(torch.autograd.Function):
+    @staticmethod
+    @custom_fwd(cast_inputs=torch.float16)
+    def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
+        output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq)
+        return output
+
+
+class QuantLinear(nn.Module):
+    def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
+        super().__init__()
+        self.register_buffer("qweight", qweight)
+        self.register_buffer("qzeros", qzeros)
+        self.register_buffer("scales", scales)
+        self.register_buffer("g_idx", g_idx)
+        if bias is not None:
+            self.register_buffer("bias", bias)
+        else:
+            self.bias = None
+        if bits not in [2, 4, 8]:
+            raise NotImplementedError("Only 2,4,8 bits are supported.")
+        self.bits = bits
+        self.maxq = 2**self.bits - 1
+        self.groupsize = groupsize
+
+        self.outfeatures = qweight.shape[1]
+        self.infeatures = qweight.shape[0] * 32 // 4
+
+    @classmethod
+    def new(cls, bits, groupsize, infeatures, outfeatures, bias):
+        if bits not in [2, 4, 8]:
+            raise NotImplementedError("Only 2,4,8 bits are supported.")
+
+        qweight = torch.zeros((infeatures // 32 * bits, outfeatures), dtype=torch.int32)
+        qzeros = torch.zeros(
+            (math.ceil(infeatures / groupsize), outfeatures // 32 * bits),
+            dtype=torch.int32,
+        )
+        scales = torch.zeros(
+            (math.ceil(infeatures / groupsize), outfeatures), dtype=torch.float16
+        )
+        g_idx = torch.tensor(
+            [i // groupsize for i in range(infeatures)], dtype=torch.int32
+        )
+        if bias:
+            bias = torch.zeros((outfeatures), dtype=torch.float16)
+        else:
+            bias = None
+        return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize)
+
+    def pack(self, linear, scales, zeros, g_idx=None):
+        self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
+
+        scales = scales.t().contiguous()
+        zeros = zeros.t().contiguous()
+        scale_zeros = zeros * scales
+        self.scales = scales.clone().half()
+        if linear.bias is not None:
+            self.bias = linear.bias.clone().half()
+
+        intweight = []
+        for idx in range(self.infeatures):
+            intweight.append(
+                torch.round(
+                    (linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]])
+                    / self.scales[self.g_idx[idx]]
+                ).to(torch.int)[:, None]
+            )
+        intweight = torch.cat(intweight, dim=1)
+        intweight = intweight.t().contiguous()
+        intweight = intweight.numpy().astype(np.uint32)
+        qweight = np.zeros(
+            (intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32
+        )
+        i = 0
+        row = 0
+        while row < qweight.shape[0]:
+            if self.bits in [2, 4, 8]:
+                for j in range(i, i + (32 // self.bits)):
+                    qweight[row] |= intweight[j] << (self.bits * (j - i))
+                i += 32 // self.bits
+                row += 1
+            else:
+                raise NotImplementedError("Only 2,4,8 bits are supported.")
+
+        qweight = qweight.astype(np.int32)
+        self.qweight = torch.from_numpy(qweight)
+
+        zeros -= 1
+        zeros = zeros.numpy().astype(np.uint32)
+        qzeros = np.zeros(
+            (zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32
+        )
+        i = 0
+        col = 0
+        while col < qzeros.shape[1]:
+            if self.bits in [2, 4, 8]:
+                for j in range(i, i + (32 // self.bits)):
+                    qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
+                i += 32 // self.bits
+                col += 1
+            else:
+                raise NotImplementedError("Only 2,4,8 bits are supported.")
+
+        qzeros = qzeros.astype(np.int32)
+        self.qzeros = torch.from_numpy(qzeros)
+
+    def forward(self, x):
+        out_shape = x.shape[:-1] + (self.outfeatures,)
+        out = QuantLinearFunction.apply(
+            x.reshape(-1, x.shape[-1]),
+            self.qweight,
+            self.scales,
+            self.qzeros,
+            self.g_idx,
+            self.bits,
+            self.maxq,
+        )
+        out = out + self.bias if self.bias is not None else out
+        return out.reshape(out_shape)

server/text_generation_server/utils/gptq/quantize.py

@@ -0,0 +1,866 @@
+import argparse
+import time
+import numpy as np
+import torch
+import torch.nn as nn
+import math
+import json
+import os
+
+from texttable import Texttable
+from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
+import transformers
+from huggingface_hub import HfApi
+import numpy as np
+import torch
+from text_generation_server.utils.gptq.quant_linear import QuantLinear
+from loguru import logger
+from typing import Optional
+
+DEV = torch.device("cuda:0")
+
+
+class Quantizer(nn.Module):
+    def __init__(self, shape=1):
+        super(Quantizer, self).__init__()
+        self.register_buffer("maxq", torch.tensor(0))
+        self.register_buffer("scale", torch.zeros(shape))
+        self.register_buffer("zero", torch.zeros(shape))
+
+    def configure(
+        self,
+        bits,
+        perchannel=False,
+        sym=True,
+        mse=False,
+        norm=2.4,
+        grid=100,
+        maxshrink=0.8,
+        trits=False,
+    ):
+
+        self.maxq = torch.tensor(2**bits - 1)
+        self.perchannel = perchannel
+        self.sym = sym
+        self.mse = mse
+        self.norm = norm
+        self.grid = grid
+        self.maxshrink = maxshrink
+        if trits:
+            self.maxq = torch.tensor(-1)
+        self.scale = torch.zeros_like(self.scale)
+
+    def _quantize(self, x, scale, zero, maxq):
+        if maxq < 0:
+            return (x > scale / 2).float() * scale + (x < zero / 2).float() * zero
+        q = torch.clamp(torch.round(x / scale) + zero, 0, maxq)
+        return scale * (q - zero)
+
+    def find_params(self, x, weight=False):
+        dev = x.device
+        self.maxq = self.maxq.to(dev)
+
+        shape = x.shape
+        if self.perchannel:
+            if weight:
+                x = x.flatten(1)
+            else:
+                if len(shape) == 4:
+                    x = x.permute([1, 0, 2, 3])
+                    x = x.flatten(1)
+                if len(shape) == 3:
+                    x = x.reshape((-1, shape[-1])).t()
+                if len(shape) == 2:
+                    x = x.t()
+        else:
+            x = x.flatten().unsqueeze(0)
+
+        tmp = torch.zeros(x.shape[0], device=dev)
+        xmin = torch.minimum(x.min(1)[0], tmp)
+        xmax = torch.maximum(x.max(1)[0], tmp)
+
+        if self.sym:
+            xmax = torch.maximum(torch.abs(xmin), xmax)
+            tmp = xmin < 0
+            if torch.any(tmp):
+                xmin[tmp] = -xmax[tmp]
+        tmp = (xmin == 0) & (xmax == 0)
+        xmin[tmp] = -1
+        xmax[tmp] = +1
+
+        if self.maxq < 0:
+            self.scale = xmax
+            self.zero = xmin
+        else:
+            self.scale = (xmax - xmin) / self.maxq
+            if self.sym:
+                self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2)
+            else:
+                self.zero = torch.round(-xmin / self.scale)
+
+        if self.mse:
+            best = torch.full([x.shape[0]], float("inf"), device=dev)
+            for i in range(int(self.maxshrink * self.grid)):
+                p = 1 - i / self.grid
+                xmin1 = p * xmin
+                xmax1 = p * xmax
+                scale1 = (xmax1 - xmin1) / self.maxq
+                zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero
+                q = self._quantize(
+                    x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq
+                )
+                q -= x
+                q.abs_()
+                q.pow_(self.norm)
+                err = torch.sum(q, 1)
+                tmp = err < best
+                if torch.any(tmp):
+                    best[tmp] = err[tmp]
+                    self.scale[tmp] = scale1[tmp]
+                    self.zero[tmp] = zero1[tmp]
+        if not self.perchannel:
+            if weight:
+                tmp = shape[0]
+            else:
+                tmp = shape[1] if len(shape) != 3 else shape[2]
+            self.scale = self.scale.repeat(tmp)
+            self.zero = self.zero.repeat(tmp)
+
+        if weight:
+            shape = [-1] + [1] * (len(shape) - 1)
+            self.scale = self.scale.reshape(shape)
+            self.zero = self.zero.reshape(shape)
+            return
+        if len(shape) == 4:
+            self.scale = self.scale.reshape((1, -1, 1, 1))
+            self.zero = self.zero.reshape((1, -1, 1, 1))
+        if len(shape) == 3:
+            self.scale = self.scale.reshape((1, 1, -1))
+            self.zero = self.zero.reshape((1, 1, -1))
+        if len(shape) == 2:
+            self.scale = self.scale.unsqueeze(0)
+            self.zero = self.zero.unsqueeze(0)
+
+    def quantize(self, x):
+        if self.ready():
+            return self._quantize(x, self.scale, self.zero, self.maxq)
+
+        return x
+
+    def enabled(self):
+        return self.maxq > 0
+
+    def ready(self):
+        return torch.all(self.scale != 0)
+
+
+class GPTQ:
+    def __init__(self, layer, observe=False):
+        self.layer = layer
+        self.dev = self.layer.weight.device
+        W = layer.weight.data.clone()
+        if isinstance(self.layer, nn.Conv2d):
+            W = W.flatten(1)
+        if isinstance(self.layer, transformers.Conv1D):
+            W = W.t()
+        self.rows = W.shape[0]
+        self.columns = W.shape[1]
+        self.H = torch.zeros((self.columns, self.columns), device=self.dev)
+        self.nsamples = 0
+        self.quantizer = Quantizer()
+        self.observe = observe
+
+    def add_batch(self, inp, out):
+        # Hessian H = 2 X XT + λ I
+        if self.observe:
+            self.inp1 = inp
+            self.out1 = out
+        else:
+            self.inp1 = None
+            self.out1 = None
+
+        if len(inp.shape) == 2:
+            inp = inp.unsqueeze(0)
+        tmp = inp.shape[0]
+        if isinstance(self.layer, nn.Linear) or isinstance(
+            self.layer, transformers.Conv1D
+        ):
+            if len(inp.shape) == 3:
+                inp = inp.reshape((-1, inp.shape[-1]))
+            inp = inp.t()
+        if isinstance(self.layer, nn.Conv2d):
+            unfold = nn.Unfold(
+                self.layer.kernel_size,
+                dilation=self.layer.dilation,
+                padding=self.layer.padding,
+                stride=self.layer.stride,
+            )
+            inp = unfold(inp)
+            inp = inp.permute([1, 0, 2])
+            inp = inp.flatten(1)
+        self.H *= self.nsamples / (self.nsamples + tmp)
+        self.nsamples += tmp
+        # inp = inp.float()
+        inp = math.sqrt(2 / self.nsamples) * inp.float()
+        # self.H += 2 / self.nsamples * inp.matmul(inp.t())
+        self.H += inp.matmul(inp.t())
+
+    def print_loss(self, name, q_weight, weight_error, timecost):
+        table = Texttable()
+        length = 28
+        name = (
+            (name + " " * (length - len(name)))
+            if len(name) <= length
+            else name[:length]
+        )
+
+        table.header(["name", "weight_error", "fp_inp_SNR", "q_inp_SNR", "time"])
+
+        # assign weight
+        self.layer.weight.data = q_weight.reshape(self.layer.weight.shape).to(
+            self.layer.weight.data.dtype
+        )
+
+        if self.inp1 is not None:
+            # quantize input to int8
+            quantizer = Quantizer()
+            quantizer.configure(8, perchannel=False, sym=True, mse=False)
+            quantizer.find_params(self.inp1)
+            q_in = quantizer.quantize(self.inp1).type(torch.float16)
+            q_out = self.layer(q_in)
+
+            # get kinds of SNR
+            q_SNR = torch_snr_error(q_out, self.out1).item()
+            fp_SNR = torch_snr_error(self.layer(self.inp1), self.out1).item()
+        else:
+            q_SNR = "-"
+            fp_SNR = "-"
+
+        table.add_row([name, weight_error, fp_SNR, q_SNR, timecost])
+        print(table.draw().split("\n")[-2])
+
+    def fasterquant(
+        self, blocksize=128, percdamp=0.01, groupsize=-1, act_order=False, name=""
+    ):
+        self.layer.to(self.dev)
+
+        W = self.layer.weight.data.clone()
+        if isinstance(self.layer, nn.Conv2d):
+            W = W.flatten(1)
+        if isinstance(self.layer, transformers.Conv1D):
+            W = W.t()
+        W = W.float()
+
+        tick = time.time()
+
+        if not self.quantizer.ready():
+            self.quantizer.find_params(W, weight=True)
+
+        H = self.H
+        if not self.observe:
+            del self.H
+        dead = torch.diag(H) == 0
+        H[dead, dead] = 1
+        W[:, dead] = 0
+
+        if act_order:
+            perm = torch.argsort(torch.diag(H), descending=True)
+            W = W[:, perm]
+            H = H[perm][:, perm]
+
+        Losses = torch.zeros_like(W)
+        Q = torch.zeros_like(W)
+
+        damp = percdamp * torch.mean(torch.diag(H))
+        diag = torch.arange(self.columns, device=self.dev)
+        H[diag, diag] += damp
+        H = torch.linalg.cholesky(H)
+        H = torch.cholesky_inverse(H)
+        try:
+            H = torch.linalg.cholesky(H, upper=True)
+        except Exception:
+            # Addition because Falcon fails on h_to_4h
+            H = torch.linalg.cholesky(
+                H + 1e-5 * torch.eye(H.shape[0]).to(H.device), upper=True
+            )
+        Hinv = H
+
+        g_idx = []
+        scale = []
+        zero = []
+        now_idx = 1
+
+        for i1 in range(0, self.columns, blocksize):
+            i2 = min(i1 + blocksize, self.columns)
+            count = i2 - i1
+
+            W1 = W[:, i1:i2].clone()
+            Q1 = torch.zeros_like(W1)
+            Err1 = torch.zeros_like(W1)
+            Losses1 = torch.zeros_like(W1)
+            Hinv1 = Hinv[i1:i2, i1:i2]
+
+            for i in range(count):
+                w = W1[:, i]
+                d = Hinv1[i, i]
+
+                if groupsize != -1:
+                    if (i1 + i) % groupsize == 0:
+                        self.quantizer.find_params(
+                            W[:, (i1 + i) : (i1 + i + groupsize)], weight=True
+                        )
+
+                    if ((i1 + i) // groupsize) - now_idx == -1:
+                        scale.append(self.quantizer.scale)
+                        zero.append(self.quantizer.zero)
+                        now_idx += 1
+
+                q = self.quantizer.quantize(w.unsqueeze(1)).flatten()
+                Q1[:, i] = q
+                Losses1[:, i] = (w - q) ** 2 / d**2
+
+                err1 = (w - q) / d
+                W1[:, i:] -= err1.unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0))
+                Err1[:, i] = err1
+
+            Q[:, i1:i2] = Q1
+            Losses[:, i1:i2] = Losses1 / 2
+
+            W[:, i2:] -= Err1.matmul(Hinv[i1:i2, i2:])
+
+        torch.cuda.synchronize()
+        error = torch.sum(Losses).item()
+
+        groupsize = groupsize if groupsize != -1 else self.columns
+        g_idx = [i // groupsize for i in range(self.columns)]
+        g_idx = torch.tensor(g_idx, dtype=torch.int32, device=Q.device)
+        if act_order:
+            invperm = torch.argsort(perm)
+            Q = Q[:, invperm]
+            g_idx = g_idx[invperm]
+
+        if isinstance(self.layer, transformers.Conv1D):
+            Q = Q.t()
+
+        self.print_loss(
+            name=name, q_weight=Q, weight_error=error, timecost=(time.time() - tick)
+        )
+
+        if scale == []:
+            scale.append(self.quantizer.scale)
+            zero.append(self.quantizer.zero)
+        scale = torch.cat(scale, dim=1)
+        zero = torch.cat(zero, dim=1)
+        return scale, zero, g_idx, error
+
+    def free(self):
+        self.inp1 = None
+        self.out1 = None
+        self.H = None
+        self.Losses = None
+        self.Trace = None
+        torch.cuda.empty_cache()
+
+
+def get_wikitext2(nsamples, seed, seqlen, model_id):
+    from datasets import load_dataset
+
+    traindata = load_dataset("wikitext", "wikitext-2-raw-v1", split="train")
+    testdata = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")
+
+    from transformers import AutoTokenizer
+
+    tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
+    trainenc = tokenizer("\n\n".join(traindata["text"]), return_tensors="pt")
+    testenc = tokenizer("\n\n".join(testdata["text"]), return_tensors="pt")
+
+    import random
+
+    random.seed(seed)
+    trainloader = []
+    for _ in range(nsamples):
+        i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
+        j = i + seqlen
+        inp = trainenc.input_ids[:, i:j]
+        tar = inp.clone()
+        tar[:, :-1] = -100
+        trainloader.append((inp, tar))
+    return trainloader, testenc
+
+
+def get_ptb(nsamples, seed, seqlen, model_id):
+    from datasets import load_dataset
+
+    traindata = load_dataset("ptb_text_only", "penn_treebank", split="train")
+    valdata = load_dataset("ptb_text_only", "penn_treebank", split="validation")
+
+    from transformers import AutoTokenizer
+
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
+    except:
+        tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+    trainenc = tokenizer("\n\n".join(traindata["sentence"]), return_tensors="pt")
+    testenc = tokenizer("\n\n".join(valdata["sentence"]), return_tensors="pt")
+
+    import random
+
+    random.seed(seed)
+    trainloader = []
+    for _ in range(nsamples):
+        i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
+        j = i + seqlen
+        inp = trainenc.input_ids[:, i:j]
+        tar = inp.clone()
+        tar[:, :-1] = -100
+        trainloader.append((inp, tar))
+    return trainloader, testenc
+
+
+def get_c4(nsamples, seed, seqlen, model_id):
+    from datasets import load_dataset
+
+    traindata = load_dataset(
+        "allenai/c4",
+        "allenai--c4",
+        data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
+        split="train",
+        use_auth_token=False,
+    )
+    valdata = load_dataset(
+        "allenai/c4",
+        "allenai--c4",
+        data_files={"validation": "en/c4-validation.00000-of-00008.json.gz"},
+        split="validation",
+        use_auth_token=False,
+    )
+
+    from transformers import AutoTokenizer
+
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
+    except:
+        tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+
+    import random
+
+    random.seed(seed)
+    trainloader = []
+    for _ in range(nsamples):
+        while True:
+            i = random.randint(0, len(traindata) - 1)
+            trainenc = tokenizer(traindata[i]["text"], return_tensors="pt")
+            if trainenc.input_ids.shape[1] >= seqlen:
+                break
+        i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
+        j = i + seqlen
+        inp = trainenc.input_ids[:, i:j]
+        tar = inp.clone()
+        tar[:, :-1] = -100
+        trainloader.append((inp, tar))
+
+    import random
+
+    random.seed(0)
+    valenc = []
+    for _ in range(256):
+        while True:
+            i = random.randint(0, len(valdata) - 1)
+            tmp = tokenizer(valdata[i]["text"], return_tensors="pt")
+            if tmp.input_ids.shape[1] >= seqlen:
+                break
+        i = random.randint(0, tmp.input_ids.shape[1] - seqlen - 1)
+        j = i + seqlen
+        valenc.append(tmp.input_ids[:, i:j])
+    valenc = torch.hstack(valenc)
+
+    class TokenizerWrapper:
+        def __init__(self, input_ids):
+            self.input_ids = input_ids
+
+    valenc = TokenizerWrapper(valenc)
+
+    return trainloader, valenc
+
+
+def get_ptb_new(nsamples, seed, seqlen, model_id):
+    from datasets import load_dataset
+
+    traindata = load_dataset("ptb_text_only", "penn_treebank", split="train")
+    testdata = load_dataset("ptb_text_only", "penn_treebank", split="test")
+
+    from transformers import AutoTokenizer
+
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
+    except:
+        tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+    trainenc = tokenizer(" ".join(traindata["sentence"]), return_tensors="pt")
+    testenc = tokenizer(" ".join(testdata["sentence"]), return_tensors="pt")
+
+    import random
+
+    random.seed(seed)
+    trainloader = []
+    for _ in range(nsamples):
+        i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
+        j = i + seqlen
+        inp = trainenc.input_ids[:, i:j]
+        tar = inp.clone()
+        tar[:, :-1] = -100
+        trainloader.append((inp, tar))
+    return trainloader, testenc
+
+
+def get_c4_new(nsamples, seed, seqlen, model_id):
+    from datasets import load_dataset
+
+    traindata = load_dataset(
+        "allenai/c4",
+        "allenai--c4",
+        data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
+        split="train",
+    )
+    valdata = load_dataset(
+        "allenai/c4",
+        "allenai--c4",
+        data_files={"validation": "en/c4-validation.00000-of-00008.json.gz"},
+        split="validation",
+    )
+
+    from transformers import AutoTokenizer
+
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
+    except:
+        tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+
+    import random
+
+    random.seed(seed)
+    trainloader = []
+    for _ in range(nsamples):
+        while True:
+            i = random.randint(0, len(traindata) - 1)
+            trainenc = tokenizer(traindata[i]["text"], return_tensors="pt")
+            if trainenc.input_ids.shape[1] >= seqlen:
+                break
+        i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
+        j = i + seqlen
+        inp = trainenc.input_ids[:, i:j]
+        tar = inp.clone()
+        tar[:, :-1] = -100
+        trainloader.append((inp, tar))
+
+    valenc = tokenizer(" ".join(valdata[:1100]["text"]), return_tensors="pt")
+    valenc = valenc.input_ids[:, : (256 * seqlen)]
+
+    class TokenizerWrapper:
+        def __init__(self, input_ids):
+            self.input_ids = input_ids
+
+    valenc = TokenizerWrapper(valenc)
+
+    return trainloader, valenc
+
+
+def get_loaders(name, nsamples=128, seed=0, seqlen=2048, model_id=""):
+    if "wikitext2" in name:
+        return get_wikitext2(nsamples, seed, seqlen, model_id)
+    if "ptb" in name:
+        if "new" in name:
+            return get_ptb_new(nsamples, seed, seqlen, model_id)
+        return get_ptb(nsamples, seed, seqlen, model_id)
+    if "c4" in name:
+        if "new" in name:
+            return get_c4_new(nsamples, seed, seqlen, model_id)
+        return get_c4(nsamples, seed, seqlen, model_id)
+
+
+def find_layers(module, layers=(nn.Conv2d, nn.Linear), name=""):
+    # Skip last lm_head linear
+    # Need isintance Falcon is inheriting Linear.
+    if isinstance(module, layers) and "lm_head" not in name:
+        return {name: module}
+    res = {}
+    for name1, child in module.named_children():
+        res.update(
+            find_layers(
+                child, layers=layers, name=name + "." + name1 if name != "" else name1
+            )
+        )
+    return res
+
+
+@torch.no_grad()
+def sequential(
+    model,
+    dataloader,
+    dev,
+    nsamples,
+    bits,
+    groupsize,
+    percdamp=0.01,
+    sym: bool = False,
+    act_order: bool = False,
+):
+    print("Starting ...")
+
+    use_cache = model.config.use_cache
+    model.config.use_cache = False
+    try:
+        layers = model.model.layers
+        prefix = "model.layers"
+    except Exception:
+        layers = model.transformer.h
+        prefix = "transformer.h"
+
+    dtype = next(iter(model.parameters())).dtype
+    inps = torch.zeros(
+        (nsamples, model.seqlen, model.config.hidden_size), dtype=dtype, device=dev
+    )
+
+    cache = {"i": 0}
+    extra = {}
+
+    class Catcher(nn.Module):
+        def __init__(self, module):
+            super().__init__()
+            self.module = module
+
+        def forward(self, inp, **kwargs):
+            inps[cache["i"]] = inp
+            cache["i"] += 1
+            extra.update(kwargs.copy())
+            raise ValueError
+
+    layers[0] = Catcher(layers[0])
+    for batch in dataloader:
+        try:
+            model(batch[0])
+        except ValueError:
+            pass
+    layers[0] = layers[0].module
+
+    # layers[0] = layers[0].cpu()
+    # model.model.embed_tokens = model.model.embed_tokens.cpu()
+    # model.model.norm = model.model.norm.cpu()
+    torch.cuda.empty_cache()
+
+    outs = torch.zeros_like(inps)
+
+    extra = {
+        k: v.to(dev) if isinstance(v, torch.Tensor) else v for k, v in extra.items()
+    }
+
+    print("Ready.")
+
+    quantizers = {}
+    for i in range(len(layers)):
+        print(f"Quantizing layer {i+1}/{len(layers)}..")
+        print("+------------------+--------------+------------+-----------+-------+")
+        print("|       name       | weight_error | fp_inp_SNR | q_inp_SNR | time  |")
+        print("+==================+==============+============+===========+=======+")
+
+        from accelerate.hooks import remove_hook_from_submodules
+
+        layer = layers[i].to(dev)
+        remove_hook_from_submodules(layer)
+        full = find_layers(layer)
+        sequential = [list(full.keys())]
+
+        for names in sequential:
+            subset = {n: full[n] for n in names}
+            gptq = {}
+            for name in subset:
+                gptq[name] = GPTQ(subset[name])
+                gptq[name].quantizer.configure(
+                    bits, perchannel=True, sym=sym, mse=False
+                )
+
+            def add_batch(name):
+                def tmp(_, inp, out):
+                    gptq[name].add_batch(inp[0].data, out.data)
+
+                return tmp
+
+            handles = []
+            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()
+
+            for name in subset:
+                scale, zero, g_idx, error = gptq[name].fasterquant(
+                    percdamp=percdamp,
+                    groupsize=groupsize,
+                    act_order=act_order,
+                    name=name,
+                )
+                quantizers[f"{prefix}.{i}.{name}"] = (
+                    gptq[name].quantizer.cpu(),
+                    scale.cpu(),
+                    zero.cpu(),
+                    g_idx.cpu(),
+                    bits,
+                    groupsize,
+                )
+
+                gptq[name].free()
+
+        for j in range(nsamples):
+            outs[j] = layer(inps[j].unsqueeze(0), **extra)[0]
+
+        layers[i] = layer.cpu()
+        del layer
+        del gptq
+        torch.cuda.empty_cache()
+
+        inps, outs = outs, inps
+        print("+------------------+--------------+------------+-----------+-------+")
+        print("\n")
+
+    model.config.use_cache = use_cache
+
+    return quantizers
+
+
+def make_quant_linear(module, names, bits, groupsize, name=""):
+    if isinstance(module, QuantLinear):
+        return
+    for attr in dir(module):
+        tmp = getattr(module, attr)
+        name1 = name + "." + attr if name != "" else attr
+        if name1 in names:
+            delattr(module, attr)
+            setattr(
+                module,
+                attr,
+                QuantLinear.new(
+                    bits,
+                    groupsize,
+                    tmp.in_features,
+                    tmp.out_features,
+                    tmp.bias is not None,
+                ),
+            )
+    for name1, child in module.named_children():
+        make_quant_linear(
+            child, names, bits, groupsize, name + "." + name1 if name != "" else name1
+        )
+
+
+# TODO: perform packing on GPU
+def pack(model, quantizers, bits, groupsize):
+    layers = find_layers(model)
+    layers = {n: layers[n] for n in quantizers}
+    make_quant_linear(model, quantizers, bits, groupsize)
+    qlayers = find_layers(model, (QuantLinear,))
+    print("Packing ...")
+    for name in qlayers:
+        print(name)
+        quantizers[name], scale, zero, g_idx, _, _ = quantizers[name]
+        qlayers[name].pack(layers[name], scale, zero, g_idx)
+    print("Done.")
+    return model
+
+
+def quantize(
+    model_id: str,
+    bits: int,
+    groupsize: int,
+    output_dir: str,
+    trust_remote_code: bool,
+    upload_to_model_id: Optional[str],
+    percdamp: float,
+    act_order: bool,
+):
+    print("loading model")
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16,
+        device_map="balanced_low_0",
+        trust_remote_code=trust_remote_code,
+    )
+    print("LOADED model")
+    model.seqlen = 2048
+
+    dataset = "wikitext2"
+    nsamples = 128
+    seed = None
+
+    dataloader, testloader = get_loaders(
+        dataset, nsamples=nsamples, seed=seed, model_id=model_id, seqlen=model.seqlen
+    )
+
+    tick = time.time()
+    quantizers = sequential(
+        model,
+        dataloader,
+        DEV,
+        nsamples,
+        bits,
+        groupsize,
+        percdamp=percdamp,
+        act_order=act_order,
+    )
+    print(time.time() - tick)
+
+    pack(model, quantizers, bits, groupsize)
+    from safetensors.torch import save_file
+    from transformers.modeling_utils import shard_checkpoint
+
+    state_dict = model.state_dict()
+    state_dict = {k: v.cpu().contiguous() for k, v in state_dict.items()}
+    state_dict["gptq_bits"] = torch.LongTensor([bits])
+    state_dict["gptq_groupsize"] = torch.LongTensor([groupsize])
+
+    max_shard_size = "10GB"
+    shards, index = shard_checkpoint(
+        state_dict, max_shard_size=max_shard_size, weights_name="model.safetensors"
+    )
+    os.makedirs(output_dir, exist_ok=True)
+    for shard_file, shard in shards.items():
+        save_file(
+            shard,
+            os.path.join(output_dir, shard_file),
+            metadata={
+                "format": "pt",
+                "quantized": "gptq",
+                "origin": "text-generation-inference",
+            },
+        )
+    if index is None:
+        path_to_weights = os.path.join(output_dir, "model.safetensors")
+        logger.info(f"Model weights saved in {path_to_weights}")
+    else:
+        save_index_file = "model.safetensors.index.json"
+        save_index_file = os.path.join(output_dir, save_index_file)
+        with open(save_index_file, "w", encoding="utf-8") as f:
+            content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+            f.write(content)
+        logger.info(
+            f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be "
+            f"split in {len(shards)} checkpoint shards. You can find where each parameters has been saved in the "
+            f"index located at {save_index_file}."
+        )
+    config = AutoConfig.from_pretrained(model_id, trust_remote_code=trust_remote_code)
+    config.save_pretrained(output_dir)
+    logger.info("Saved config")
+    logger.info("Saving tokenizer")
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_id, trust_remote_code=trust_remote_code
+    )
+    tokenizer.save_pretrained(output_dir)
+    logger.info("Saved tokenizer")
+
+    if upload_to_model_id:
+
+        api = HfApi()
+
+        api.upload_folder(
+            folder_path=output_dir, repo_id=upload_to_model_id, repo_type="model"
+        )

server/text_generation_server/utils/layers.py

@@ -15,6 +15,8 @@ except ImportError:
 
 from accelerate import init_empty_weights
 
+from text_generation_server.utils.gptq.quant_linear import QuantLinear
+
 
 # Monkey patching
 @classmethod
@@ -129,7 +131,22 @@ def get_linear(weight, bias, quantize):
         if bias is not None:
             linear.bias = nn.Parameter(bias)
     elif quantize == "gptq":
-        raise NotImplementedError("Soon")
+        try:
+            qweight, qzeros, scales, g_idx, bits, groupsize = weight
+        except Exception:
+            raise NotImplementedError(
+                f"The passed weight is not `gptq` compatible, loader needs to be updated."
+            )
+
+        linear = QuantLinear(
+            qweight,
+            qzeros,
+            scales,
+            g_idx,
+            bias,
+            bits,
+            groupsize,
+        )
     else:
         raise NotImplementedError(f"Quantization `{quantize}` is not implemented yet.")
     return linear
@@ -152,8 +169,14 @@ class TensorParallelHead(SuperLayer):
     @staticmethod
     def load(config, prefix: str, weights):
         weight = weights.get_sharded(f"{prefix}.weight", dim=0)
+
+        # GPTQ doesn't quantize heads (nor embeddings)
+        if config.quantize == "gptq":
+            quantize = None
+        else:
+            quantize = config.quantize
         return TensorParallelHead(
-            get_linear(weight, bias=None, quantize=config.quantize),
+            get_linear(weight, bias=None, quantize=quantize),
             process_group=weights.process_group,
         )
 
@@ -196,24 +219,21 @@ class TensorParallelHead(SuperLayer):
 class TensorParallelColumnLinear(SuperLayer):
     @classmethod
     def load(cls, config, prefix: str, weights, bias: bool):
-        weight = weights.get_sharded(f"{prefix}.weight", dim=0)
+        return cls.load_multi(config, [prefix], weights, bias, dim=0)
-        if bias:
-            bias = weights.get_sharded(f"{prefix}.bias", dim=0)
-        else:
-            bias = None
-        return cls(get_linear(weight, bias, config.quantize))
 
     @classmethod
     def load_multi(cls, config, prefixes: List[str], weights, bias: bool, dim: int):
-        w = [weights.get_sharded(f"{p}.weight", dim=0) for p in prefixes]
+        weight = weights.get_multi_weights_col(
-        weight = torch.cat(w, dim=dim)
+            prefixes, quantize=config.quantize, dim=dim
+        )
 
         if bias:
             b = [weights.get_sharded(f"{p}.bias", dim=0) for p in prefixes]
-            bias = torch.cat(b, dim=0)
+            bias = torch.cat(b, dim=dim)
         else:
             bias = None
-        return cls(get_linear(weight, bias, config.quantize))
+        linear = get_linear(weight, bias, config.quantize)
+        return cls(linear)
 
 
 class TensorParallelRowLinear(SuperLayer):
@@ -223,7 +243,8 @@ class TensorParallelRowLinear(SuperLayer):
 
     @classmethod
     def load(cls, config, prefix: str, weights, bias: bool):
-        weight = weights.get_sharded(f"{prefix}.weight", dim=1)
+        weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
+
         if bias and weights.process_group.rank() == 0:
             # Rank is only on the first rank process
             bias = weights.get_tensor(f"{prefix}.bias")

server/text_generation_server/utils/weights.py

@@ -1,6 +1,7 @@
 from pathlib import Path
 from typing import List, Dict, Optional
 from safetensors import safe_open
+import torch
 
 
 class Weights:
@@ -54,6 +55,9 @@ class Weights:
         filename, tensor_name = self.get_filename(tensor_name)
         f = self._get_handle(filename)
         tensor = f.get_tensor(tensor_name)
+        # Special case for gptq which shouldn't convert
+        # u4 which are disguised as int32
+        if tensor.dtype not in [torch.int32, torch.int64]:
             tensor = tensor.to(dtype=self.dtype)
         tensor = tensor.to(device=self.device)
         return tensor
@@ -80,6 +84,49 @@ class Weights:
             tensor = slice_[:, start:stop]
         else:
             raise NotImplementedError("Let's make that generic when needed")
+        # Special case for gptq which shouldn't convert
+        # u4 which are disguised as int32
+        if tensor.dtype != torch.int32:
             tensor = tensor.to(dtype=self.dtype)
         tensor = tensor.to(device=self.device)
         return tensor
+
+    def get_multi_weights_col(self, prefixes: List[str], quantize: str, dim: int):
+        if quantize == "gptq":
+            try:
+                qweight = torch.cat([self.get_sharded(f"{p}.qweight", dim=1) for p in prefixes], dim=1)
+            except RuntimeError:
+                raise RuntimeError("Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`")
+
+            qzeros = torch.cat([self.get_sharded(f"{p}.qzeros", dim=1) for p in prefixes], dim=1)
+            scales = torch.cat([self.get_sharded(f"{p}.scales", dim=1) for p in prefixes], dim=1)
+            w = [self.get_tensor(f"{p}.g_idx") for p in prefixes]
+            for w2 in w[1:]:
+                torch.testing.assert_close(w2, w[0])
+            g_idx = w[0]
+
+            bits = self.get_tensor("gptq_bits").item()
+            groupsize = self.get_tensor("gptq_groupsize").item()
+            weight = (qweight, qzeros, scales, g_idx, bits, groupsize)
+        else:
+            w = [self.get_sharded(f"{p}.weight", dim=0) for p in prefixes]
+            weight = torch.cat(w, dim=dim)
+        return weight
+
+    def get_multi_weights_row(self, prefix: str, quantize: str):
+        if quantize == "gptq":
+            try:
+                qweight = self.get_sharded(f"{prefix}.qweight", dim=0)
+            except RuntimeError:
+                raise RuntimeError("Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`")
+            qzeros = self.get_tensor(f"{prefix}.qzeros")
+            scales = self.get_tensor(f"{prefix}.scales")
+            g_idx = self.get_sharded(f"{prefix}.g_idx", dim=0)
+
+            bits = self.get_tensor("gptq_bits").item()
+            groupsize = self.get_tensor("gptq_groupsize").item()
+
+            weight = (qweight, qzeros, scales, g_idx, bits, groupsize)
+        else:
+            weight = self.get_sharded(f"{prefix}.weight", dim=1)
+        return weight