awq fallback to exllama

server/text_generation_server/utils/awq/pack_utils.py

@@ -0,0 +1,146 @@
+import torch
+from typing import List
+
+
+AWQ_PACK_ORDER = [0, 2, 4, 6, 1, 3, 5, 7]
+REVERSE_AWQ_PACK_ORDER = [0, 4, 1, 5, 2, 6, 3, 7]
+
+
+def pack(imatrix: torch.Tensor, direction: str = "column"):
+    """
+    Packs a 4-bit integer matrix into a packed 32-bit integer matrix.
+    Args:
+        imatrix (torch.Tensor): matrix of integers
+        direction (str): direction of packing, either "column" or "row"
+    Returns:
+        qmatrix (torch.Tensor): packed matrix of integers
+    """
+    shifts = torch.arange(0, 32, 4, device=imatrix.device)
+
+    imatrix = imatrix.to(torch.int8)
+    imatrix = torch.bitwise_and(imatrix, 0x0F)  # eventually correct overflow
+
+    if direction == "column":
+        imatrix = imatrix.view(-1, imatrix.shape[1] // (32 // 4), (32 // 4))
+        qmatrix = torch.bitwise_left_shift(imatrix, shifts[None, None, :]).sum(dim=-1)
+
+    elif direction == "row":
+        imatrix = imatrix.view(imatrix.shape[0] // (32 // 4), (32 // 4), -1)
+        qmatrix = torch.bitwise_left_shift(imatrix, shifts[None, :, None]).sum(dim=1)
+
+    qmatrix = qmatrix.to(torch.int32)
+
+    return qmatrix
+
+
+def unpack(qmatrix: torch.Tensor, direction: str = "column"):
+    """
+    Unpacks a 32-bit packed integer matrix into a 4-bit integer matrix.
+    Args:
+        qmatrix (torch.Tensor): matrix of packed integers
+        direction (str): direction of unpacking, either "column" or "row"
+    Returns:
+        imatrix (torch.Tensor): matrix of integers
+    """
+    shifts = torch.arange(0, 32, 4, device=qmatrix.device)
+
+    if direction == "column":
+        imatrix = torch.bitwise_right_shift(
+            qmatrix[:, :, None], shifts[None, None, :]
+        ).view(qmatrix.shape[0], -1)
+
+    elif direction == "row":
+        imatrix = torch.bitwise_right_shift(
+            qmatrix[:, None, :], shifts[None, :, None]
+        ).view(-1, qmatrix.shape[-1])
+
+    imatrix = imatrix.to(torch.int8) & 0x0F  # eventually correct overflow
+
+    return imatrix
+
+
+def quantize(fmatrix, scales, zeros, group_size):
+    """
+    Quantizes a matrix of 16-bit floats into a matrix of 4-bit integers.
+    Args:
+        fmatrix (torch.Tensor): matrix of 16-bit floats
+        scales (torch.Tensor): matrix of 16-bit floats
+        zeros (torch.Tensor): matrix of 4-bit integers
+        group_size (int): group size
+    Returns:
+        imatrix (torch.Tensor): matrix of 4-bit integers
+    """
+    zeros = zeros.to(torch.int8) & 0x0F
+
+    imatrix = torch.round(
+        (
+            fmatrix / scales.repeat_interleave(group_size, dim=0)
+            + zeros.repeat_interleave(group_size, dim=0)
+        )
+    )
+
+    imatrix = imatrix.to(torch.int8) & 0x0F
+
+    return imatrix
+
+
+def dequantize(imatrix, scales, zeros, group_size):
+    """
+    Dequantizes a 4-bit integer matrix into a float matrix.
+    Args:
+        imatrix (torch.Tensor): matrix of 4-bit integers
+        scales (torch.Tensor): matrix of 16-bit floats
+        zeros (torch.Tensor): matrix of 4-bit integers
+        group_size (int): group size
+    Returns:
+        fmatrix (torch.Tensor): matrix of 16-bit floats
+    """
+    zeros = zeros.to(torch.int8) & 0x0F
+    imatrix = imatrix.to(torch.int8) & 0x0F
+
+    fmatrix = (
+        imatrix - zeros.repeat_interleave(group_size, dim=0)
+    ) * scales.repeat_interleave(group_size, dim=0)
+
+    fmatrix = fmatrix.to(torch.float16)
+
+    return fmatrix
+
+
+def apply_order(
+    imatrix: torch.Tensor,
+    direction: str = "column",
+    order: List[int] = AWQ_PACK_ORDER,
+):
+    """
+    Applies the order to a 4-bit integer matrix.
+    Args:
+        imatrix (torch.Tensor): matrix of integers
+        direction (str): direction of applying order, either "column" or "row"
+        order (List[int]): order to apply, default is AWQ_PACK_ORDER
+    Returns:
+        imatrix (torch.Tensor): matrix of integers
+    """
+    if direction == "column":
+        imatrix = imatrix.view(-1, (32 // 4))[:, order].view(imatrix.shape)
+    elif direction == "row":
+        imatrix = imatrix.view((32 // 4), -1)[order, :].view(imatrix.shape)
+
+    return imatrix
+
+
+def fast_awq_to_exllama(qweight, qzeros):
+    # awq uses column packing for both weights and zeros
+    izeros = unpack(qzeros, direction="column")
+    iweights = unpack(qweight, direction="column")
+
+    # Reverse the order of the iweight and izeros tensors
+    izeros = apply_order(izeros, direction="column", order=REVERSE_AWQ_PACK_ORDER)
+    iweights = apply_order(iweights, direction="column", order=REVERSE_AWQ_PACK_ORDER)
+    # Subtract 1 from the izeros tensor (exllama adds 1 during inference)
+    izeros = izeros - 1
+    # exllama uses row packing for weights and column packing for zeros
+    qzeros = pack(izeros, direction="column")
+    qweight = pack(iweights, direction="row")
+
+    return qweight, qzeros

server/text_generation_server/utils/layers.py

@@ -25,6 +25,7 @@ HAS_AWQ = True
 try:
     from text_generation_server.utils.awq.quantize.qmodule import WQLinear
 except ImportError:
+    from text_generation_server.utils.awq.pack_utils import fast_awq_to_exllama
     HAS_AWQ = False
 
 try:
@@ -349,14 +350,20 @@ def get_linear(weight, bias, quantize):
             raise NotImplementedError(
                 f"The passed weight is not `awq` compatible, loader needs to be updated."
             )
-        linear = WQLinear(
-            w_bit=bits,
-            group_size=groupsize,
-            qweight=qweight,
-            qzeros=qzeros,
-            scales=scales,
-            bias=bias is not None,
-        )
+        if HAS_AWQ:
+            linear = WQLinear(
+                w_bit=bits,
+                group_size=groupsize,
+                qweight=qweight,
+                qzeros=qzeros,
+                scales=scales,
+                bias=bias is not None,
+            )
+        elif HAS_EXLLAMA:
+            qweight, qzeros = fast_awq_to_exllama(qweight, qzeros)
+            linear = ExllamaQuantLinear(
+                qweight, qzeros, scales, None, bias, bits, groupsize
+            )
     else:
         raise NotImplementedError(f"Quantization `{quantize}` is not implemented yet.")
     return linear