build in docker

Dockerfile

@@ -113,6 +113,12 @@ RUN make build-flash-attention-v2
 FROM kernel-builder as exllama-kernels-builder
 WORKDIR /usr/src
 COPY server/exllama_kernels/ .
+
+# Build Transformers exllama kernels
+FROM kernel-builder as exllama-kernels-builder
+WORKDIR /usr/src
+COPY server/exllamav2_kernels/ .
+
 # Build specific version of transformers
 RUN TORCH_CUDA_ARCH_LIST="8.0;8.6+PTX" python setup.py build
 

server/text_generation_server/utils/gptq/exllamav2.py

@@ -0,0 +1,188 @@
+# Adapted from turboderp exllama: https://github.com/turboderp/exllamav2
+
+from logging import getLogger
+
+import torch
+import torch.nn as nn
+import math
+
+logger = getLogger(__name__)
+
+try:
+    from exllamav2_kernels import make_q_matrix, gemm_half_q_half
+except ImportError:
+    logger.error('exllamav2_kernels not installed.')
+    raise
+
+# Dummy tensor to pass instead of g_idx since there is no way to pass "None" to a C++ extension
+none_tensor = torch.empty((1, 1), device="meta")
+
+def _torch_device(idx):
+    if idx == -1: return "cpu"
+    return f"cuda:{idx}"
+
+def ext_gemm_half_q_half(x, q_handle, q4_width, force_cuda):
+    """Matrix multiplication, returns x @ q4"""
+    output_shape = x.shape[:-1] + (q4_width,)
+    x = x.view(-1, x.shape[-1])
+    output = torch.empty((x.shape[0], q4_width), dtype = torch.half, device = x.device)
+    gemm_half_q_half(x, q_handle, output, force_cuda)
+    return output.view(output_shape)
+
+def ext_make_q_matrix(w: dict, temp_dq, key: str = None):
+    """
+    Create Q matrix 
+    """
+    # EXL2
+    # won't work as the moment because the tensors are not the same. 
+    if "q_weight" in w:
+        w["q_scale_max"] /= 256
+        w["q_perm"] = w["q_perm"].short()
+        w["q_invperm"] = w["q_invperm"].short()
+        return make_q_matrix(w["q_weight"],
+                                w["q_perm"],
+                                w["q_invperm"],
+                                w["q_scale"],
+                                w["q_scale_max"],
+                                w["q_groups"],
+                                none_tensor,
+                                none_tensor,
+                                none_tensor,
+                                temp_dq)
+    # GPTQ
+    elif "qweight" in w:
+        if w["scales"].dtype == torch.float:
+            w["scales"] = w["scales"].half()
+
+        # GPTQ with g_idx (act_order)
+        if "g_idx" in w and not (w["g_idx"] == 0).all().item():
+            w["q_perm"] = torch.empty((w["qweight"].shape[0] * 8,), dtype = torch.short, device = w["qweight"].device)
+            w["q_invperm"] = torch.empty_like(w["q_perm"])
+            # 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.
+            return make_q_matrix(w["qweight"],
+                                 w["q_perm"],
+                                 w["q_invperm"],
+                                 none_tensor,
+                                 none_tensor,
+                                 none_tensor,
+                                 w["qzeros"],
+                                 w["scales"],
+                                 w["g_idx"].cpu(),
+                                 temp_dq)
+        # GPTQ without g_idx
+        else:
+            return make_q_matrix(w["qweight"],
+                                none_tensor,
+                                none_tensor,
+                                none_tensor,
+                                none_tensor,
+                                none_tensor,
+                                w["qzeros"],
+                                w["scales"],
+                                none_tensor,
+                                temp_dq)
+
+class QuantLinear(nn.Module):
+    QUANT_TYPE = "exllamav2"
+
+    """Linear layer implementation with per-group 4-bit quantization of the weights"""
+
+    def __init__(self, bits, group_size, infeatures, outfeatures, bias, trainable=False, **kwargs):
+        super().__init__()
+        if bits != 4:
+            raise ValueError(
+                f"Exllamav2 kernel supports only bits=4, requested bits={bits}. Something is wrong in the model initialization.")
+        if trainable:
+            raise NotImplementedError("Exllamav2 kernel does not support training.")
+
+        self.q_handle = None
+        self.q_tensors = None
+        self.padding = - outfeatures % 32
+        
+        self.infeatures = infeatures
+        self.outfeatures = outfeatures + self.padding
+        self.bits = bits
+        self.group_size = group_size if group_size != -1 else infeatures
+        self.trainable = trainable
+        self.maxq = 2 ** self.bits - 1
+
+        assert infeatures % 32 == 0
+        assert infeatures % self.group_size == 0
+        assert outfeatures % 32 == 0
+        
+        # I need to register the tensors, otherwise, we won't be able to load them easily using transformers ... 
+        self.register_buffer(
+            'qweight',
+            torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32)
+        )
+        self.register_buffer(
+            'qzeros',
+            torch.zeros((math.ceil(infeatures / self.group_size), outfeatures // 32 * self.bits), dtype=torch.int32)
+        )
+        self.register_buffer(
+            'scales',
+            torch.zeros((math.ceil(infeatures / self.group_size), outfeatures), dtype=torch.float16)
+        )
+        self.register_buffer(
+            'g_idx',
+            torch.tensor([i // self.group_size for i in range(infeatures)], dtype=torch.int32)
+        )
+
+        if bias:
+            self.register_buffer('bias', torch.zeros((outfeatures), dtype=torch.float16))
+        else:
+            self.bias = None
+
+    def post_init(self, temp_dq):
+        assert self.qweight.device.type == "cuda"
+        assert self.qweight.device.index is not None
+        self.q_tensors = {
+            "qweight":self.qweight,
+            "qzeros":self.qzeros,
+            "scales":self.scales,
+            "g_idx":self.g_idx
+        }
+        temp_dq = temp_dq.get_scratch_slice(self.temp_dq_size())
+        self.q_handle = ext_make_q_matrix(
+            self.q_tensors, temp_dq
+        )
+    
+    def forward(self, x, force_cuda = False):
+        output = ext_gemm_half_q_half(x, self.q_handle, self.outfeatures, force_cuda)
+
+        if self.bias is not None:
+            output.add_(self.bias)
+        return output
+    
+    def temp_dq_size(self):
+        return self.infeatures * self.outfeatures * 2 + 128
+    
+    def temp_fwd_size(self, max_input_len, max_batch_size):
+        return self.outfeatures * max_input_len * max_batch_size * 4 + 128
+    
+    def scratch_space_fixed(self, max_input_len=2048, max_batch_size=8):
+        return self.temp_dq_size() + self.temp_fwd_size(max_input_len, max_batch_size)
+               
+    
+class ExLlamaV2DeviceTensors:
+
+    device_idx: int
+    scratch_bytes: int
+    scratch_idx: int
+    scratch: torch.tensor = None
+
+    def __init__(self, device_idx, scratch_bytes):
+        self.device_idx = device_idx
+        self.scratch_bytes = scratch_bytes
+    
+    def prepare(self):
+        self.scratch = torch.empty((self.scratch_bytes // 2,), dtype = torch.half, device = _torch_device(self.device_idx))
+
+    def get_scratch_slice(self, size_bytes):
+
+        if self.scratch is None: self.prepare()
+
+        size_bytes = ((size_bytes + 127) // 128) * 128
+        size_half = size_bytes // 2
+        scratch_slice = self.scratch.narrow(0, 0, size_half)
+        return scratch_slice