ollama/fs/ggml/type.go

327 lines
6.9 KiB
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package ggml
import (
"fmt"
"log/slog"
"strings"
)
// FileType is the Go equivalent to llama_ftype used for gguf file typing
type FileType uint32
const (
FileTypeF32 FileType = iota
FileTypeF16
fileTypeQ4_0
fileTypeQ4_1
gpt-oss (#11672) * bf16 * tests * gpt-oss * enable gptoss for engine * rough estimate * convert to mxfp4 * handle safetensors U8 * clamp glu/linear * update tokenizer * MXFP4 support This implements the Open Compute Microscaling (MX) FP4 format as a tensor type with backend implementations focusing on mulmat and mulmatid on CPU, CUDA, and Metal. * Unit tests for MXFP4 support This exercises various operations and shapes on both CPU and GPU (if detected on the system) * cuda graph * unit test adjustments * cuda: optimize memory access Read 4 bytes at a time (8 elements) when performing mul_mat_vec_mxfp4 * mac: fix crash on old macos versions cblas_sgemm is only supported on v13.3 and up, however bf16 is only supported on v14+ so we were falling back to ggml-blas and crashing on bf16 tensors. Checking for the function being null seems to be the simplest way to condittionally avoid registering the backend. * server: Minimum context length for gptoss This model requires a minimum context length of 8192 to function effectively. Users can set higher values through all normal mechanisms but lower values will be silently reset. * ggml: Multiply by numParallel for gptoss sliding window When computing the graph size estimate, the context size is already multiplied by numParallel so estimates reflect that. However, since sliding window models use a smaller, fixed context size, they need to manually take numParallel into account. * gpt-oss integration includes harmony parser and thinking levels, etc. * fix sync * fix tests * fix lint --------- Co-authored-by: Daniel Hiltgen <daniel@ollama.com> Co-authored-by: Jesse Gross <jesse@ollama.com> Co-authored-by: Devon Rifkin <drifkin@drifkin.net>
2025-08-06 03:21:16 +08:00
fileTypeMXFP4 // originally fileTypeQ4_1_F16 // unused by GGML
fileTypeQ4_2 // unused by GGML
fileTypeQ4_3 // unused by GGML
FileTypeQ8_0
fileTypeQ5_0
fileTypeQ5_1
fileTypeQ2_K
fileTypeQ3_K_S
fileTypeQ3_K_M
fileTypeQ3_K_L
FileTypeQ4_K_S
FileTypeQ4_K_M
fileTypeQ5_K_S
fileTypeQ5_K_M
fileTypeQ6_K
fileTypeIQ2_XXS
fileTypeIQ2_XS
fileTypeQ2_K_S
fileTypeIQ3_XS
fileTypeIQ3_XXS
fileTypeIQ1_S
fileTypeIQ4_NL
fileTypeIQ3_S
fileTypeIQ3_M
fileTypeIQ2_S
fileTypeIQ2_M
fileTypeIQ4_XS
fileTypeIQ1_M
FileTypeBF16
fileTypeQ4_0_4_4 // unused by GGML
fileTypeQ4_0_4_8 // unused by GGML
fileTypeQ4_0_8_8 // unused by GGML
fileTypeTQ1_0
fileTypeTQ2_0
FileTypeUnknown = 1024
)
// ParseFileType parses the provided GGUF file type
// Only Ollama supported types are considered valid
func ParseFileType(s string) (FileType, error) {
switch s {
case "F32":
return FileTypeF32, nil
case "F16":
return FileTypeF16, nil
case "Q8_0":
return FileTypeQ8_0, nil
case "Q4_K_S":
return FileTypeQ4_K_S, nil
case "Q4_K_M", "Q4_K":
return FileTypeQ4_K_M, nil
case "BF16":
return FileTypeBF16, nil
default:
supportedFileTypes := []FileType{
FileTypeF32,
FileTypeF16,
FileTypeQ4_K_S,
FileTypeQ4_K_M,
FileTypeQ8_0,
// fsggml.FileTypeBF16, // TODO
}
strs := make([]string, len(supportedFileTypes))
for i := range supportedFileTypes {
strs[i] = supportedFileTypes[i].String()
}
return FileTypeUnknown, fmt.Errorf("unsupported quantization type %s - supported types are %s", s, strings.Join(strs, ", "))
}
}
func (t FileType) String() string {
// Note: this routine will return a broader set of file types for existing models
switch t {
case FileTypeF32:
return "F32"
case FileTypeF16:
return "F16"
case fileTypeQ4_0:
return "Q4_0"
case fileTypeQ4_1:
return "Q4_1"
gpt-oss (#11672) * bf16 * tests * gpt-oss * enable gptoss for engine * rough estimate * convert to mxfp4 * handle safetensors U8 * clamp glu/linear * update tokenizer * MXFP4 support This implements the Open Compute Microscaling (MX) FP4 format as a tensor type with backend implementations focusing on mulmat and mulmatid on CPU, CUDA, and Metal. * Unit tests for MXFP4 support This exercises various operations and shapes on both CPU and GPU (if detected on the system) * cuda graph * unit test adjustments * cuda: optimize memory access Read 4 bytes at a time (8 elements) when performing mul_mat_vec_mxfp4 * mac: fix crash on old macos versions cblas_sgemm is only supported on v13.3 and up, however bf16 is only supported on v14+ so we were falling back to ggml-blas and crashing on bf16 tensors. Checking for the function being null seems to be the simplest way to condittionally avoid registering the backend. * server: Minimum context length for gptoss This model requires a minimum context length of 8192 to function effectively. Users can set higher values through all normal mechanisms but lower values will be silently reset. * ggml: Multiply by numParallel for gptoss sliding window When computing the graph size estimate, the context size is already multiplied by numParallel so estimates reflect that. However, since sliding window models use a smaller, fixed context size, they need to manually take numParallel into account. * gpt-oss integration includes harmony parser and thinking levels, etc. * fix sync * fix tests * fix lint --------- Co-authored-by: Daniel Hiltgen <daniel@ollama.com> Co-authored-by: Jesse Gross <jesse@ollama.com> Co-authored-by: Devon Rifkin <drifkin@drifkin.net>
2025-08-06 03:21:16 +08:00
case fileTypeMXFP4:
return "MXFP4"
case FileTypeQ8_0:
return "Q8_0"
case fileTypeQ5_0:
return "Q5_0"
case fileTypeQ5_1:
return "Q5_1"
case fileTypeQ2_K:
return "Q2_K"
case fileTypeQ3_K_S:
return "Q3_K_S"
case fileTypeQ3_K_M:
return "Q3_K_M"
case fileTypeQ3_K_L:
return "Q3_K_L"
case FileTypeQ4_K_S:
return "Q4_K_S"
case FileTypeQ4_K_M:
return "Q4_K_M"
case fileTypeQ5_K_S:
return "Q5_K_S"
case fileTypeQ5_K_M:
return "Q5_K_M"
case fileTypeQ6_K:
return "Q6_K"
case fileTypeQ2_K_S:
return "Q2_K_S"
case FileTypeBF16:
return "BF16"
default:
return "unknown"
}
}
func (t FileType) Value() uint32 {
return uint32(t)
}
func (ftype FileType) ToTensorType() TensorType {
switch ftype {
case FileTypeF32:
return TensorTypeF32
case FileTypeF16:
return TensorTypeF16
case fileTypeQ4_0:
return TensorTypeQ4_0
case fileTypeQ4_1:
return TensorTypeQ4_1
gpt-oss (#11672) * bf16 * tests * gpt-oss * enable gptoss for engine * rough estimate * convert to mxfp4 * handle safetensors U8 * clamp glu/linear * update tokenizer * MXFP4 support This implements the Open Compute Microscaling (MX) FP4 format as a tensor type with backend implementations focusing on mulmat and mulmatid on CPU, CUDA, and Metal. * Unit tests for MXFP4 support This exercises various operations and shapes on both CPU and GPU (if detected on the system) * cuda graph * unit test adjustments * cuda: optimize memory access Read 4 bytes at a time (8 elements) when performing mul_mat_vec_mxfp4 * mac: fix crash on old macos versions cblas_sgemm is only supported on v13.3 and up, however bf16 is only supported on v14+ so we were falling back to ggml-blas and crashing on bf16 tensors. Checking for the function being null seems to be the simplest way to condittionally avoid registering the backend. * server: Minimum context length for gptoss This model requires a minimum context length of 8192 to function effectively. Users can set higher values through all normal mechanisms but lower values will be silently reset. * ggml: Multiply by numParallel for gptoss sliding window When computing the graph size estimate, the context size is already multiplied by numParallel so estimates reflect that. However, since sliding window models use a smaller, fixed context size, they need to manually take numParallel into account. * gpt-oss integration includes harmony parser and thinking levels, etc. * fix sync * fix tests * fix lint --------- Co-authored-by: Daniel Hiltgen <daniel@ollama.com> Co-authored-by: Jesse Gross <jesse@ollama.com> Co-authored-by: Devon Rifkin <drifkin@drifkin.net>
2025-08-06 03:21:16 +08:00
case fileTypeMXFP4:
return TensorTypeMXFP4 // Formerly unused tensorTypeQ4_2
case FileTypeQ8_0:
return TensorTypeQ8_0
case fileTypeQ5_0:
return TensorTypeQ5_0
case fileTypeQ5_1:
return TensorTypeQ5_1
case fileTypeQ2_K:
return TensorTypeQ2_K
case fileTypeQ3_K_S:
return TensorTypeQ3_K
case fileTypeQ3_K_M:
return TensorTypeQ3_K
case fileTypeQ3_K_L:
return TensorTypeQ3_K
case FileTypeQ4_K_S:
return TensorTypeQ4_K
case FileTypeQ4_K_M:
return TensorTypeQ4_K
case fileTypeQ5_K_S:
return TensorTypeQ5_K
case fileTypeQ5_K_M:
return TensorTypeQ5_K
case fileTypeQ6_K:
return TensorTypeQ6_K
case fileTypeQ2_K_S:
return TensorTypeQ2_K
case FileTypeBF16:
return TensorTypeBF16
default:
slog.Warn("unsupported file type", "type", ftype)
return 0 // F32
}
}
// TensorType is equivalent to ggml_type for individual tensor types
// Note: these are not the same as FileType
type TensorType uint32
const (
TensorTypeF32 TensorType = iota
TensorTypeF16
TensorTypeQ4_0
TensorTypeQ4_1
gpt-oss (#11672) * bf16 * tests * gpt-oss * enable gptoss for engine * rough estimate * convert to mxfp4 * handle safetensors U8 * clamp glu/linear * update tokenizer * MXFP4 support This implements the Open Compute Microscaling (MX) FP4 format as a tensor type with backend implementations focusing on mulmat and mulmatid on CPU, CUDA, and Metal. * Unit tests for MXFP4 support This exercises various operations and shapes on both CPU and GPU (if detected on the system) * cuda graph * unit test adjustments * cuda: optimize memory access Read 4 bytes at a time (8 elements) when performing mul_mat_vec_mxfp4 * mac: fix crash on old macos versions cblas_sgemm is only supported on v13.3 and up, however bf16 is only supported on v14+ so we were falling back to ggml-blas and crashing on bf16 tensors. Checking for the function being null seems to be the simplest way to condittionally avoid registering the backend. * server: Minimum context length for gptoss This model requires a minimum context length of 8192 to function effectively. Users can set higher values through all normal mechanisms but lower values will be silently reset. * ggml: Multiply by numParallel for gptoss sliding window When computing the graph size estimate, the context size is already multiplied by numParallel so estimates reflect that. However, since sliding window models use a smaller, fixed context size, they need to manually take numParallel into account. * gpt-oss integration includes harmony parser and thinking levels, etc. * fix sync * fix tests * fix lint --------- Co-authored-by: Daniel Hiltgen <daniel@ollama.com> Co-authored-by: Jesse Gross <jesse@ollama.com> Co-authored-by: Devon Rifkin <drifkin@drifkin.net>
2025-08-06 03:21:16 +08:00
TensorTypeMXFP4 // Formerly unused tensorTypeQ4_2
tensorTypeQ4_3 // unused by GGML
TensorTypeQ5_0
TensorTypeQ5_1
TensorTypeQ8_0
TensorTypeQ8_1
TensorTypeQ2_K
TensorTypeQ3_K
TensorTypeQ4_K
TensorTypeQ5_K
TensorTypeQ6_K
TensorTypeQ8_K
tensorTypeIQ2_XXS // not supported by ollama
tensorTypeIQ2_XS // not supported by ollama
tensorTypeIQ3_XXS // not supported by ollama
tensorTypeIQ1_S // not supported by ollama
tensorTypeIQ4_NL // not supported by ollama
tensorTypeIQ3_S // not supported by ollama
tensorTypeIQ2_S // not supported by ollama
tensorTypeIQ4_XS // not supported by ollama
TensorTypeI8
TensorTypeI16
TensorTypeI32
TensorTypeI64
TensorTypeF64
tensorTypeIQ1_M // not supported by ollama
TensorTypeBF16
tensorTypeQ4_0_4_4 // unused by GGML
tensorTypeQ4_0_4_8 // unused by GGML
tensorTypeQ4_0_8_8 // unused by GGML
tensorTypeTQ1_0 // not supported by ollama
tensorTypeTQ2_0 // not supported by ollama
tensorTypeIQ4_NL_4_4 // unused by GGML
tensorTypeIQ4_NL_4_8 // unused by GGML
tensorTypeIQ4_NL_8_8 // unused by GGML
)
// ParseFileType parses the provided GGUF file type
// Only Ollama supported types are considered valid
func ParseTensorType(s string) (TensorType, error) {
switch s {
case "F32":
return TensorTypeF32, nil
case "F16":
return TensorTypeF16, nil
case "Q4_0":
return TensorTypeQ4_0, nil
case "Q4_1":
return TensorTypeQ4_1, nil
case "Q5_0":
return TensorTypeQ5_0, nil
case "Q5_1":
return TensorTypeQ5_1, nil
case "Q8_0":
return TensorTypeQ8_0, nil
case "Q8_1":
return TensorTypeQ8_1, nil
case "Q2_K":
return TensorTypeQ2_K, nil
case "Q3_K":
return TensorTypeQ3_K, nil
case "Q4_K":
return TensorTypeQ4_K, nil
case "Q5_K":
return TensorTypeQ5_K, nil
case "Q6_K":
return TensorTypeQ6_K, nil
case "Q8_K":
return TensorTypeQ8_K, nil
case "F64":
return TensorTypeF64, nil
case "BF16":
return TensorTypeBF16, nil
gpt-oss (#11672) * bf16 * tests * gpt-oss * enable gptoss for engine * rough estimate * convert to mxfp4 * handle safetensors U8 * clamp glu/linear * update tokenizer * MXFP4 support This implements the Open Compute Microscaling (MX) FP4 format as a tensor type with backend implementations focusing on mulmat and mulmatid on CPU, CUDA, and Metal. * Unit tests for MXFP4 support This exercises various operations and shapes on both CPU and GPU (if detected on the system) * cuda graph * unit test adjustments * cuda: optimize memory access Read 4 bytes at a time (8 elements) when performing mul_mat_vec_mxfp4 * mac: fix crash on old macos versions cblas_sgemm is only supported on v13.3 and up, however bf16 is only supported on v14+ so we were falling back to ggml-blas and crashing on bf16 tensors. Checking for the function being null seems to be the simplest way to condittionally avoid registering the backend. * server: Minimum context length for gptoss This model requires a minimum context length of 8192 to function effectively. Users can set higher values through all normal mechanisms but lower values will be silently reset. * ggml: Multiply by numParallel for gptoss sliding window When computing the graph size estimate, the context size is already multiplied by numParallel so estimates reflect that. However, since sliding window models use a smaller, fixed context size, they need to manually take numParallel into account. * gpt-oss integration includes harmony parser and thinking levels, etc. * fix sync * fix tests * fix lint --------- Co-authored-by: Daniel Hiltgen <daniel@ollama.com> Co-authored-by: Jesse Gross <jesse@ollama.com> Co-authored-by: Devon Rifkin <drifkin@drifkin.net>
2025-08-06 03:21:16 +08:00
case "MXFP4":
return TensorTypeMXFP4, nil
default:
return 0, fmt.Errorf("unsupported quantization type %s", s)
}
}
func (t TensorType) IsQuantized() bool {
switch t {
case TensorTypeF32, TensorTypeF16, TensorTypeBF16:
return false
default:
return true
}
}
func (t TensorType) RowSize(ne uint64) uint64 {
return t.TypeSize() * ne / t.BlockSize()
}
func (t TensorType) String() string {
switch t {
case TensorTypeF32:
return "F32"
case TensorTypeF16:
return "F16"
case TensorTypeQ4_0:
return "Q4_0"
case TensorTypeQ4_1:
return "Q4_1"
case TensorTypeQ5_0:
return "Q5_0"
case TensorTypeQ5_1:
return "Q5_1"
case TensorTypeQ8_0:
return "Q8_0"
case TensorTypeQ8_1:
return "Q8_1"
case TensorTypeQ2_K:
return "Q2_K"
case TensorTypeQ3_K:
return "Q3_K"
case TensorTypeQ4_K:
return "Q4_K"
case TensorTypeQ5_K:
return "Q5_K"
case TensorTypeQ6_K:
return "Q6_K"
case TensorTypeQ8_K:
return "Q8_K"
case TensorTypeF64:
return "F64"
case TensorTypeBF16:
return "BF16"
gpt-oss (#11672) * bf16 * tests * gpt-oss * enable gptoss for engine * rough estimate * convert to mxfp4 * handle safetensors U8 * clamp glu/linear * update tokenizer * MXFP4 support This implements the Open Compute Microscaling (MX) FP4 format as a tensor type with backend implementations focusing on mulmat and mulmatid on CPU, CUDA, and Metal. * Unit tests for MXFP4 support This exercises various operations and shapes on both CPU and GPU (if detected on the system) * cuda graph * unit test adjustments * cuda: optimize memory access Read 4 bytes at a time (8 elements) when performing mul_mat_vec_mxfp4 * mac: fix crash on old macos versions cblas_sgemm is only supported on v13.3 and up, however bf16 is only supported on v14+ so we were falling back to ggml-blas and crashing on bf16 tensors. Checking for the function being null seems to be the simplest way to condittionally avoid registering the backend. * server: Minimum context length for gptoss This model requires a minimum context length of 8192 to function effectively. Users can set higher values through all normal mechanisms but lower values will be silently reset. * ggml: Multiply by numParallel for gptoss sliding window When computing the graph size estimate, the context size is already multiplied by numParallel so estimates reflect that. However, since sliding window models use a smaller, fixed context size, they need to manually take numParallel into account. * gpt-oss integration includes harmony parser and thinking levels, etc. * fix sync * fix tests * fix lint --------- Co-authored-by: Daniel Hiltgen <daniel@ollama.com> Co-authored-by: Jesse Gross <jesse@ollama.com> Co-authored-by: Devon Rifkin <drifkin@drifkin.net>
2025-08-06 03:21:16 +08:00
case TensorTypeMXFP4:
return "MXFP4"
default:
return "unknown"
}
}