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glm 4.7 flash support on experimental engine (#13838)
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@@ -13,7 +13,11 @@ import (
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// quantizeTensor loads a tensor from safetensors format, quantizes it,
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// and returns safetensors data for the quantized weights, scales, and biases.
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// Supported quantization types: "fp8" (affine 8-bit)
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// Supported quantization types:
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// - "q4": affine 4-bit, group_size=32 (with qbiases)
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// - "nvfp4": NVIDIA FP4, group_size=16 (no qbiases, E4M3 scales)
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// - "q8": affine 8-bit, group_size=64 (with qbiases)
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// - "mxfp8": Microsoft MX FP8, group_size=32 (no qbiases, E4M3 scales)
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// Uses MLX's native SaveSafetensors to ensure correct dtype handling (especially uint32 for quantized weights).
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func quantizeTensor(r io.Reader, name, dtype string, shape []int32, quantize string) (qweightData, scalesData, qbiasData []byte, qweightShape, scalesShape, qbiasShape []int32, err error) {
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tmpDir := ensureTempDir()
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@@ -54,12 +58,18 @@ func quantizeTensor(r io.Reader, name, dtype string, shape []int32, quantize str
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// Quantize based on quantization type
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var qweight, scales, qbiases *mlx.Array
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switch quantize {
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case "fp4":
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// affine mode: group_size=32, bits=4
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case "q4":
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// affine mode: group_size=32, bits=4 (with qbiases for zero-point offset)
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qweight, scales, qbiases = mlx.Quantize(arr, 32, 4, "affine")
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case "fp8":
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// affine mode: group_size=32, bits=8
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qweight, scales, qbiases = mlx.Quantize(arr, 32, 8, "affine")
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case "nvfp4":
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// NVIDIA FP4: group_size=16, bits=4 (no qbiases, E4M3 scales)
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qweight, scales, qbiases = mlx.Quantize(arr, 16, 4, "nvfp4")
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case "q8":
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// affine mode: group_size=64, bits=8 (with qbiases for zero-point offset)
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qweight, scales, qbiases = mlx.Quantize(arr, 64, 8, "affine")
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case "mxfp8":
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// Microsoft MX FP8: group_size=32, bits=8, E4M3 scales (no qbiases)
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qweight, scales, qbiases = mlx.Quantize(arr, 32, 8, "mxfp8")
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default:
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return nil, nil, nil, nil, nil, nil, fmt.Errorf("unsupported quantization type: %s", quantize)
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}
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