add gguf kernel support

Signed-off-by: Isotr0py <2037008807@qq.com>

src/diffusers/quantizers/gguf/utils.py

@@ -17,10 +17,11 @@ import inspect
 from contextlib import nullcontext
 
 import gguf
+from gguf import GGMLQuantizationType as WeightType
 import torch
 import torch.nn as nn
 
-from ...utils import is_accelerate_available
+from ...utils import is_accelerate_available, is_kernels_available
 
 
 if is_accelerate_available():
@@ -29,6 +30,76 @@ if is_accelerate_available():
     from accelerate.hooks import add_hook_to_module, remove_hook_from_module
 
 
+can_use_cuda_kernels = torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 7
+if can_use_cuda_kernels and is_kernels_available():
+    from kernels import get_kernel
+    ops = get_kernel("Isotr0py/ggml")
+else:
+    ops = None
+
+
+UNQUANTIZED_TYPES = {WeightType.F32, WeightType.F16, WeightType.BF16}
+STANDARD_QUANT_TYPES = {
+    WeightType.Q4_0,
+    WeightType.Q4_1,
+    WeightType.Q5_0,
+    WeightType.Q5_1,
+    WeightType.Q8_0,
+    WeightType.Q8_1,
+}
+KQUANT_TYPES = {
+    WeightType.Q2_K,
+    WeightType.Q3_K,
+    WeightType.Q4_K,
+    WeightType.Q5_K,
+    WeightType.Q6_K,
+}
+IMATRIX_QUANT_TYPES = {
+    WeightType.IQ1_M,
+    WeightType.IQ1_S,
+    WeightType.IQ2_XXS,
+    WeightType.IQ2_XS,
+    WeightType.IQ2_S,
+    WeightType.IQ3_XXS,
+    WeightType.IQ3_S,
+    WeightType.IQ4_XS,
+    WeightType.IQ4_NL,
+}
+# TODO(Isotr0py): Currently, we don't have MMQ kernel for I-Matrix quantization.
+# Consolidate DEQUANT_TYPES, MMVQ_QUANT_TYPES and MMQ_QUANT_TYPES after we add
+# MMQ kernel for I-Matrix quantization.
+DEQUANT_TYPES = STANDARD_QUANT_TYPES | KQUANT_TYPES | IMATRIX_QUANT_TYPES
+MMVQ_QUANT_TYPES = STANDARD_QUANT_TYPES | KQUANT_TYPES | IMATRIX_QUANT_TYPES
+MMQ_QUANT_TYPES = STANDARD_QUANT_TYPES | KQUANT_TYPES
+
+
+def _fused_mul_mat_gguf(x: torch.Tensor, qweight: torch.Tensor,
+                        qweight_type: int) -> torch.Tensor:
+    # there is no need to call any kernel for fp16/bf16
+    if qweight_type in UNQUANTIZED_TYPES:
+        return x @ qweight.T
+    # enable MMVQ in contiguous batching with batch_size=1
+    if qweight_type in MMVQ_QUANT_TYPES:
+        y = ops.ggml_mul_mat_vec_a8(qweight, x, qweight_type, qweight.shape[0])
+    # Use MMQ Kernel if it's available (standard + k-quants)
+    elif qweight_type in MMQ_QUANT_TYPES:
+        y = ops.ggml_mul_mat_a8(qweight, x, qweight_type, qweight.shape[0])
+    # If there is no available MMQ kernel, fallback to dequantize
+    elif qweight_type in DEQUANT_TYPES:
+        block_size, type_size = gguf.GGML_QUANT_SIZES[qweight_type]
+        shape = (qweight.shape[0], qweight.shape[1] // type_size * block_size)
+        weight = ops.ggml_dequantize(qweight, qweight_type, *shape, x.dtype)
+        y = x @ weight.T
+    else:
+        # Raise an error if the quantization type is not supported.
+        # Might be useful if llama.cpp adds a new quantization type.
+        # Wrap to GGMLQuantizationType IntEnum to make sure it's a valid type.
+        qweight_type = WeightType(qweight_type)
+        raise NotImplementedError(
+            f"Unsupported GGUF quantization type: {qweight_type}")
+    return y
+
+
 # Copied from diffusers.quantizers.bitsandbytes.utils._create_accelerate_new_hook
 def _create_accelerate_new_hook(old_hook):
     r"""
@@ -451,11 +522,22 @@ class GGUFLinear(nn.Linear):
     ) -> None:
         super().__init__(in_features, out_features, bias, device)
         self.compute_dtype = compute_dtype
+        self.device = device
 
     def forward(self, inputs):
+        if ops is not None and self.weight.is_cuda and inputs.is_cuda:
+            return self.forward_cuda(inputs)
+        return self.forward_native(inputs)
+
+    def forward_native(self, inputs):
         weight = dequantize_gguf_tensor(self.weight)
         weight = weight.to(self.compute_dtype)
         bias = self.bias.to(self.compute_dtype) if self.bias is not None else None
 
         output = torch.nn.functional.linear(inputs, weight, bias)
         return output
+
+    def forward_cuda(self, inputs):
+        quant_type = self.weight.quant_type
+        return _fused_mul_mat_gguf(inputs.to(self.compute_dtype), self.weight, quant_type)
+

src/diffusers/utils/__init__.py

@@ -76,6 +76,7 @@ from .import_utils import (
     is_hpu_available,
     is_inflect_available,
     is_invisible_watermark_available,
+    is_kernels_available,
     is_k_diffusion_available,
     is_k_diffusion_version,
     is_librosa_available,

src/diffusers/utils/import_utils.py

@@ -192,6 +192,7 @@ _torch_xla_available, _torch_xla_version = _is_package_available("torch_xla")
 _torch_npu_available, _torch_npu_version = _is_package_available("torch_npu")
 _transformers_available, _transformers_version = _is_package_available("transformers")
 _hf_hub_available, _hf_hub_version = _is_package_available("huggingface_hub")
+_kernels_available, _kernels_version = _is_package_available("kernels")
 _inflect_available, _inflect_version = _is_package_available("inflect")
 _unidecode_available, _unidecode_version = _is_package_available("unidecode")
 _k_diffusion_available, _k_diffusion_version = _is_package_available("k_diffusion")
@@ -274,6 +275,10 @@ def is_accelerate_available():
     return _accelerate_available
 
 
+def is_kernels_available():
+    return _kernels_available
+
+
 def is_k_diffusion_available():
     return _k_diffusion_available