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SDNQ expand quantized_matmul_dtypes for CPU

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Disty0
2025-06-02 00:28:29 +03:00
parent b146025a5e
commit 8f1a1d7311
1 changed files with 4 additions and 2 deletions
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modules/model_quant_sdnq.py
+4 -2
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@@ -28,7 +28,9 @@ dtype_dict = {
"float8_e5m2fnuz": {"min": -57344, "max": 57344, "num_bits": 8, "target_dtype": CustomDtype.FP8, "torch_dtype": torch.float8_e5m2fnuz, "storage_dtype": torch.float8_e5m2fnuz, "is_unsigned": False, "is_integer": False},
}
quantized_matmul_dtypes = ("int8", "int6", "int4", "float8_e4m3fn")
quantized_matmul_dtypes = ("int8", "int6", "int4", "int2", "float8_e4m3fn")
if devices.backend in {"cpu", "openvino"}:
quantized_matmul_dtypes += ("float8_e5m2", "float8_e4m3fnuz", "float8_e5m2fnuz")
linear_types = ("Linear",)
conv_types = ("Conv1d", "Conv2d", "Conv3d")
@@ -71,7 +73,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
use_quantized_matmul = weights_dtype in quantized_matmul_dtypes and channel_size >= 32 and output_channel_size >= 32
if use_quantized_matmul and not dtype_dict[weights_dtype]["is_integer"]:
use_quantized_matmul = output_channel_size % 16 == 0 and channel_size % 16 == 0
use_tensorwise_fp8_matmul = devices.backend == "cpu" or (devices.backend == "cuda" and sys.platform == "win32" and float(torch.__version__[:3]) <= 2.7 and torch.cuda.get_device_capability(devices.device) == (8,9))
use_tensorwise_fp8_matmul = devices.backend in {"cpu", "openvino"} or (devices.backend == "cuda" and sys.platform == "win32" and float(torch.__version__[:3]) <= 2.7 and torch.cuda.get_device_capability(devices.device) == (8,9))
if not use_quantized_matmul and (group_size > 0 or (dtype_dict[weights_dtype]["num_bits"] < 6 and group_size != -1)):
if group_size == 0: