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https://github.com/huggingface/text-generation-inference.git
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Do not initialize scratch space when there are no ExLlamaV2 layers (#2015)
# What does this PR do? Do not attempt to allocate ExLlamaV2 scratch buffers when there are no ExLlama2 layers. Avoids a crash in warmup for models that cannot use exllama when ExLlamaV2 is installed. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil -->
This commit is contained in:
Daniël de Kok
yuanwu
parent
353a9669ba
commit
cdd120ac02
@ -1,10 +1,15 @@
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# Adapted from turboderp exllama: https://github.com/turboderp/exllamav2
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from dataclasses import dataclass
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from typing import Optional
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import torch
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import torch.nn as nn
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from loguru import logger
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from text_generation_server.layers.exl2 import Exl2Weight
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from text_generation_server.layers.gptq import GPTQWeight
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try:
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from exllamav2_kernels import make_q_matrix, gemm_half_q_half
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except ImportError:
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@ -15,6 +20,15 @@ except ImportError:
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none_tensor = torch.empty((1, 1), device="meta")
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@dataclass
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class _ExtraTensors:
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"""Additional generated quantizer tensors."""
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q_group_map: Optional[torch.Tensor] = None
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q_invperm: Optional[torch.Tensor] = None
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q_perm: Optional[torch.Tensor] = None
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def ext_gemm_half_q_half(x, q_handle, q4_width, force_cuda):
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"""Matrix multiplication, returns x @ q4"""
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output_shape = x.shape[:-1] + (q4_width,)
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@ -24,11 +38,7 @@ def ext_gemm_half_q_half(x, q_handle, q4_width, force_cuda):
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return output.view(output_shape)
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# Group map needed for irregular group sizes
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def make_group_map(q_groups, num_qrows):
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def make_group_map(q_groups: torch.Tensor, num_qrows: int):
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gr = q_groups.tolist()
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group_map = []
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num_groups = len(gr) // 2
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@ -50,72 +60,72 @@ def make_group_map(q_groups, num_qrows):
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# Create Q matrix
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def ext_make_q_matrix(w: dict, temp_dq, key: str = None):
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def ext_make_q_matrix(
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w: Exl2Weight | GPTQWeight,
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extra: _ExtraTensors,
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temp_dq,
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key: Optional[str] = None,
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):
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"""
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Create Q matrix
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"""
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# EXL2
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# won't work as the moment because the tensors are not the same.
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if "q_weight" in w:
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w["q_scale_max"] /= 256
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w["q_perm"] = w["q_perm"].short()
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w["q_invperm"] = w["q_invperm"].short()
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if "q_group_map" not in w:
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w["q_group_map"] = make_group_map(w["q_groups"], w["q_weight"].shape[0])
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if isinstance(w, Exl2Weight):
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extra.q_group_map = make_group_map(w.q_groups, w.q_weight.shape[0])
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extra.q_perm = torch.argsort(w.q_invperm).short()
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return make_q_matrix(
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w["q_weight"],
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w["q_perm"],
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w["q_invperm"],
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w["q_scale"],
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w["q_scale_max"],
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w["q_groups"],
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w["q_group_map"],
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w.q_weight,
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extra.q_perm,
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w.q_invperm,
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w.q_scale,
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w.q_scale_max,
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w.q_groups,
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extra.q_group_map,
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none_tensor,
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none_tensor,
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none_tensor,
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temp_dq,
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)
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# GPTQ
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elif "qweight" in w:
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if w["scales"].dtype == torch.float:
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w["scales"] = w["scales"].half()
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elif isinstance(w, GPTQWeight):
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if w.scales.dtype == torch.float:
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w.scales = w.scales.half()
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# GPTQ with g_idx (act_order)
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if w.get("g_idx", None) is not None and not (w["g_idx"] == 0).all().item():
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w["q_perm"] = torch.empty(
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(w["qweight"].shape[0] * 8,),
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if w.g_idx is not None and not (w.g_idx == 0).all().item():
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extra.q_perm = torch.empty(
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(w.qweight.shape[0] * 8,),
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dtype=torch.short,
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device=w["qweight"].device,
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device=w.qweight.device,
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)
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w["q_invperm"] = torch.empty_like(w["q_perm"])
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extra.q_invperm = torch.empty_like(extra.q_perm)
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# make_q4 segfaults if g_idx is not on cpu in the act-order case. In the non act-order case, None needs to be passed for g_idx.
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return make_q_matrix(
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w["qweight"],
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w["q_perm"],
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w["q_invperm"],
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w.qweight,
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extra.q_perm,
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extra.q_invperm,
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none_tensor,
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none_tensor,
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none_tensor,
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none_tensor,
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w["qzeros"],
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w["scales"],
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w["g_idx"].cpu(),
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w.qzeros,
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w.scales,
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w.g_idx.cpu(),
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temp_dq,
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)
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# GPTQ without g_idx
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else:
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return make_q_matrix(
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w["qweight"],
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w.qweight,
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none_tensor,
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none_tensor,
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none_tensor,
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none_tensor,
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none_tensor,
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none_tensor,
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w["qzeros"],
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w["scales"],
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w.qzeros,
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w.scales,
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none_tensor,
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temp_dq,
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)
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@ -124,7 +134,6 @@ def ext_make_q_matrix(w: dict, temp_dq, key: str = None):
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DEVICE = None
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FIXED_BYTES = 0
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LAYERS = []
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@ -134,8 +143,19 @@ def set_device(device):
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def create_exllama_buffers(max_total_tokens: int):
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global FIXED_BYTES, LAYERS, DEVICE
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temp_dq = ExLlamaV2DeviceTensors(DEVICE, FIXED_BYTES)
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global LAYERS, DEVICE
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# No need to initialize scratch space if there are no layers
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# that use ExLLamav2.
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if len(LAYERS) == 0:
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return
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# Find the size of the scratch space.
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scratch_bytes = max(
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layer.scratch_space_fixed(max_input_len=max_total_tokens, max_batch_size=1)
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for layer in LAYERS
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)
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temp_dq = ExLlamaV2DeviceTensors(DEVICE, scratch_bytes)
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for layer in LAYERS:
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layer.post_init(temp_dq)
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@ -146,49 +166,48 @@ class QuantLinear(nn.Module):
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"""Linear layer implementation with per-group 4-bit quantization of the weights"""
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# def __init__(self, bits, group_size, infeatures, outfeatures, bias, trainable=False, **kwargs):
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def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
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def __init__(
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self,
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weight: Exl2Weight | GPTQWeight,
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bias: torch.Tensor,
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):
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super().__init__()
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if bits != 4:
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raise ValueError(
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f"Exllamav2 kernel supports only bits=4, requested bits={bits}. Something is wrong in the model initialization."
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)
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self.q_handle = None
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self.q_tensors = None
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self.bits = bits
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self.maxq = 2**self.bits - 1
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self.infeatures = qweight.shape[0] // self.bits * 32
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self.outfeatures = qweight.shape[1]
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self.q_tensors = weight
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self.extra_tensors = _ExtraTensors()
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if isinstance(weight, Exl2Weight):
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self.infeatures = weight.q_invperm.shape[0]
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self.outfeatures = weight.q_weight.shape[1]
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elif isinstance(weight, GPTQWeight):
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if weight.bits != 4:
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raise ValueError(
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f"Exllamav2 kernel supports only bits=4, requested bits={weight.bits}. Something is wrong in the model initialization."
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)
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self.infeatures = weight.qweight.shape[0] // weight.bits * 32
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self.outfeatures = weight.qweight.shape[1]
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self.padding = -self.outfeatures % 32
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self.outfeatures = self.outfeatures + self.padding
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self.device = qweight.device
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self.qweight = qweight
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self.qzeros = qzeros
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self.scales = scales
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self.g_idx = g_idx
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self.device = weight.device
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self.bias = bias if bias is not None else None
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self.group_size = groupsize
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global FIXED_BYTES, LAYERS
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FIXED_BYTES = max(FIXED_BYTES, self.scratch_space_fixed())
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global LAYERS
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LAYERS.append(self)
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def post_init(self, temp_dq):
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assert self.qweight.device.type == "cuda"
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assert self.qweight.device.index is not None
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self.q_tensors = {
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"qweight": self.qweight,
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"qzeros": self.qzeros,
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"scales": self.scales,
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"g_idx": self.g_idx,
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}
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device = self.q_tensors.device
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assert device.type == "cuda"
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assert device.index is not None
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temp_dq = temp_dq.get_scratch_slice(self.temp_dq_size())
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# We NEED to keep a pointer on Python side, otherwise the garbage collector will mess with us,
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# and `Memory access fault by GPU node-2` will EAT you.
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self.temp_dq = temp_dq
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self.q_handle = ext_make_q_matrix(self.q_tensors, temp_dq)
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self.q_handle = ext_make_q_matrix(self.q_tensors, self.extra_tensors, temp_dq)
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def forward(self, x, force_cuda=False):
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output = ext_gemm_half_q_half(x, self.q_handle, self.outfeatures, force_cuda)
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@ -203,7 +222,7 @@ class QuantLinear(nn.Module):
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def temp_fwd_size(self, max_input_len, max_batch_size):
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return self.outfeatures * max_input_len * max_batch_size * 4 + 128
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def scratch_space_fixed(self, max_input_len=4096, max_batch_size=16):
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def scratch_space_fixed(self, max_input_len, max_batch_size):
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return self.temp_dq_size() + self.temp_fwd_size(max_input_len, max_batch_size)
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