}
return vision_config
+ def get_audio_config(self) -> dict[str, Any] | None:
+ return self.global_config.get("sound_config")
+
def set_gguf_parameters(self):
if "image_mean" not in self.preprocessor_config:
self.preprocessor_config["image_mean"] = [0.485, 0.456, 0.406]
if "image_std" not in self.preprocessor_config:
self.preprocessor_config["image_std"] = [0.229, 0.224, 0.225]
+ if self.hparams_audio is not None:
+ self.has_vision_encoder = True
+ self.has_audio_encoder = True
+ self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["num_mel_bins"])
+ self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+ self.gguf_writer.add_audio_subsampling_factor(self.hparams_audio["subsampling_factor"])
+ self.gguf_writer.add_audio_conv_kernel_size(self.hparams_audio["conv_kernel_size"])
+ self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.PARAKEET)
+ self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL)
+ else:
+ self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL)
+
super().set_gguf_parameters()
hparams = self.global_config
- self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL)
self.gguf_writer.add_vision_attention_layernorm_eps(1e-6)
self.gguf_writer.add_vision_use_gelu(True)
downsample_ratio = hparams.get("downsample_ratio", 0.5)
self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
def tensor_force_quant(self, name, new_name, bid, n_dims):
- if ".position_embd." in new_name or "pos_embed" in new_name:
- return gguf.GGMLQuantizationType.F32
+ if "sound_encoder" in name or new_name.startswith("mm.a."):
+ if "bias" in new_name or "norm" in new_name:
+ return gguf.GGMLQuantizationType.F32
+ if "conv" in new_name and "weight" in new_name:
+ return gguf.GGMLQuantizationType.F32
+
return super().tensor_force_quant(name, new_name, bid, n_dims)
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
- name, gen = item
+ if (titem := super().filter_tensors(item)) is None:
+ return None
+ name, gen = titem
if "input_conditioner" in name:
return None
if "radio_model.model.patch_generator.video_embedder" in name:
return None
- if not name.startswith("vision_model.radio_model.model.") and not name.startswith("mlp1."):
+ if not name.startswith(("vision_model.radio_model.model.", "mlp1.", "sound_encoder.", "sound_projection.")):
return None
if "patch_generator.pos_embed" in name:
if not name.endswith(".weight"):
name += ".weight"
- return super().filter_tensors((name, gen))
+ # num_batches is only used for training not inference.
+ if "conv.norm" in name and "num_batches" in name:
+ return None
+
+ return name, gen
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# RADIO's pos_embed doesn't have .weight suffix, but clip.cpp expects it
n_embd = self.hparams["hidden_size"]
data_torch = data_torch.reshape(n_embd, 3, patch_size, patch_size)
- yield from super().modify_tensors(data_torch, name, bid)
+ if "depthwise_conv.weight" in name:
+ data_torch = data_torch.unsqueeze(-1)
+ data_torch = data_torch.permute(3, 1, 0, 2).contiguous()
+
+ if "pointwise_conv" in name and name.endswith(".weight"):
+ if len(data_torch.shape) == 3 and data_torch.shape[2] == 1:
+ data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1])
+
+ if "subsampling.layers" in name and name.endswith(".bias"):
+ if len(data_torch.shape) == 1:
+ data_torch = data_torch.reshape(1, -1, 1, 1)
+
+ if "pointwise_conv" in name and name.endswith(".bias"):
+ if len(data_torch.shape) == 1:
+ data_torch = data_torch.reshape(1, -1, 1, 1)
+
+ for mapped_name, tensor in super().modify_tensors(data_torch, name, bid):
+ if name.startswith("sound_projection.") and mapped_name.startswith("mm.model.mlp."):
+ mapped_name = mapped_name.replace("mm.model.mlp.", "mm.a.mlp.")
+ yield mapped_name, tensor
@ModelBase.register("NemotronForCausalLM")
FEED_FORWARD_LENGTH = "clip.audio.feed_forward_length"
PROJECTION_DIM = "clip.audio.projection_dim"
BLOCK_COUNT = "clip.audio.block_count"
+ SUBSAMPLING_FACTOR = "clip.audio.subsampling_factor"
CHUNK_SIZE = "clip.audio.chunk_size"
CONV_KERNEL_SIZE = "clip.audio.conv_kernel_size"
MAX_POS_EMB = "clip.audio.max_pos_emb"
A_ENC_CONV_NORM = auto() # SSM conv
A_ENC_CONV_PW1 = auto()
A_ENC_CONV_PW2 = auto()
+ A_ENC_CONV_NORM_MEAN = auto() # parakeet
+ A_ENC_CONV_NORM_VAR = auto() # parakeet
+ A_ENC_MEL_FILTERS = auto() # parakeet
+ A_ENC_WINDOW = auto() # parakeet
A_CTC_OUT = auto()
A_CTC_OUT_MID = auto()
A_ENC_ATTN_REL_POS_EMB = auto()
MODEL_TENSOR.A_ENC_CONV_NORM: "a.blk.{bid}.conv_norm",
MODEL_TENSOR.A_ENC_CONV_PW1: "a.blk.{bid}.conv_pw1",
MODEL_TENSOR.A_ENC_CONV_PW2: "a.blk.{bid}.conv_pw2",
+ MODEL_TENSOR.A_ENC_CONV_NORM_MEAN: "a.blk.{bid}.conv_norm_mean",
+ MODEL_TENSOR.A_ENC_CONV_NORM_VAR: "a.blk.{bid}.conv_norm_var",
+ MODEL_TENSOR.A_ENC_MEL_FILTERS: "a.mel_filters",
+ MODEL_TENSOR.A_ENC_WINDOW: "a.window",
MODEL_TENSOR.A_CTC_OUT: "a.enc_ctc_out",
MODEL_TENSOR.A_CTC_OUT_MID: "a.enc_ctc_out_mid",
MODEL_TENSOR.A_ENC_ATTN_REL_POS_EMB: "a.blk.{bid}.attn_rel_pos_emb",
MODEL_TENSOR.A_ENC_CONV_NORM,
MODEL_TENSOR.A_ENC_CONV_PW1,
MODEL_TENSOR.A_ENC_CONV_PW2,
+ MODEL_TENSOR.A_ENC_CONV_NORM_MEAN,
+ MODEL_TENSOR.A_ENC_CONV_NORM_VAR,
+ MODEL_TENSOR.A_ENC_MEL_FILTERS,
+ MODEL_TENSOR.A_ENC_WINDOW,
MODEL_TENSOR.A_MM_INP_PROJ,
MODEL_TENSOR.A_MM_SOFT_EMB_NORM,
MODEL_TENSOR.A_MM_EMBEDDING,
YOUTUVL = "youtuvl"
NEMOTRON_V2_VL = "nemotron_v2_vl"
HUNYUANVL = "hunyuanvl"
+ PARAKEET = "parakeet" # audio
MINIMAXM3 = "minimax_m3"
MINICPMV4_6 = "minicpmv4_6"
GRANITE_SPEECH = "granite_speech" # audio
def add_audio_stack_factor(self, value: int) -> None:
self.add_uint32(Keys.ClipAudio.Projector.STACK_FACTOR, value)
+ def add_audio_subsampling_factor(self, value: int) -> None:
+ self.add_uint32(Keys.ClipAudio.SUBSAMPLING_FACTOR, value)
+
def add_audio_chunk_size(self, value: int) -> None:
self.add_uint32(Keys.ClipAudio.CHUNK_SIZE, value)
"conformer.pre_encode.conv.{bid}", # lfm2
"model.audio_tower.subsample_conv_projection.conv_{bid}.conv", # gemma3n
"conformer.subsample_conv_projection.layer{bid}.conv", # gemma4
+ "sound_encoder.encoder.subsampling.layers.{bid}", # parakeet
"encoder.conv{bid}", # mimo-audio-tokenizer
),
"conformer.layers.{bid}.self_attn.linear_q", # lfm2
"conformer.layers.{bid}.attention.attn.q_proj", # gemma3n
"conformer.layers.{bid}.self_attn.q_proj", # gemma4
+ "sound_encoder.encoder.layers.{bid}.self_attn.q_proj", # parakeet
"encoder.layers.{bid}.attn.to_q", # granite_speech
"encoder.layers.{bid}.self_attn.q_proj", # mimo-audio-tokenizer
),
"conformer.layers.{bid}.self_attn.linear_k", # lfm2
"conformer.layers.{bid}.attention.attn.k_proj", # gemma3n
"conformer.layers.{bid}.self_attn.k_proj", # gemma4
+ "sound_encoder.encoder.layers.{bid}.self_attn.k_proj", # parakeet
"encoder.layers.{bid}.attn.to_k", # granite_speech (split from to_kv)
"encoder.layers.{bid}.self_attn.k_proj", # mimo-audio-tokenizer
),
"conformer.layers.{bid}.self_attn.linear_v", # lfm2
"conformer.layers.{bid}.attention.attn.v_proj", # gemma3n
"conformer.layers.{bid}.self_attn.v_proj", # gemma4
+ "sound_encoder.encoder.layers.{bid}.self_attn.v_proj", # parakeet
"encoder.layers.{bid}.attn.to_v", # granite_speech (split from to_kv)
"encoder.layers.{bid}.self_attn.v_proj", # mimo-audio-tokenizer
),
"audio_tower.layers.{bid}.self_attn_layer_norm", # ultravox
"conformer.layers.{bid}.norm_self_att", # lfm2
"conformer.layers.{bid}.attention.pre_attn_norm", # gemma3n
+ "sound_encoder.encoder.layers.{bid}.norm_self_att", # parakeet
"encoder.layers.{bid}.attn.pre_norm", # granite_speech
"encoder.layers.{bid}.self_attn_layer_norm", # mimo-audio-tokenizer
),
"conformer.layers.{bid}.self_attn.linear_out", # lfm2
"conformer.layers.{bid}.attention.post", # gemma3n
"conformer.layers.{bid}.self_attn.post", # gemma4
+ "sound_encoder.encoder.layers.{bid}.self_attn.o_proj", # parakeet
"encoder.layers.{bid}.attn.to_out", # granite_speech
"encoder.layers.{bid}.self_attn.out_proj", # mimo-audio-tokenizer
),
"audio_tower.layers.{bid}.final_layer_norm", # ultravox
"conformer.layers.{bid}.norm_out", # lfm2
"conformer.layers.{bid}.attention.post_norm", # gemma3n
+ "sound_encoder.encoder.layers.{bid}.norm_out", # parakeet
"encoder.layers.{bid}.post_norm", # granite_speech
"encoder.layers.{bid}.final_layer_norm", # mimo-audio-tokenizer
),
"conformer.layers.{bid}.norm_feed_forward1", # lfm2
"conformer.layers.{bid}.ffw_layer_start.pre_layer_norm", # gemma3n
"conformer.layers.{bid}.feed_forward1.pre_layer_norm", # gemma4
+ "sound_encoder.encoder.layers.{bid}.norm_feed_forward1", # parakeet
"encoder.layers.{bid}.ff1.pre_norm", # granite_speech
),
"conformer.layers.{bid}.feed_forward1.linear1", # lfm2
"conformer.layers.{bid}.ffw_layer_start.ffw_layer_1", # gemma3n
"conformer.layers.{bid}.feed_forward1.ffw_layer_1", # gemma4
+ "sound_encoder.encoder.layers.{bid}.feed_forward1.linear1", # parakeet
"encoder.layers.{bid}.ff1.up_proj", # granite_speech
"encoder.layers.{bid}.fc1", # mimo-audio-tokenizer
),
"conformer.layers.{bid}.feed_forward1.linear2", # lfm2
"conformer.layers.{bid}.ffw_layer_start.ffw_layer_2", # gemma3n
"conformer.layers.{bid}.feed_forward1.ffw_layer_2", # gemma4
+ "sound_encoder.encoder.layers.{bid}.feed_forward1.linear2", # parakeet
"encoder.layers.{bid}.ff1.down_proj", # granite_speech
"encoder.layers.{bid}.fc2", # mimo-audio-tokenizer
),
"conformer.layers.{bid}.feed_forward2.linear1", # lfm2
"conformer.layers.{bid}.ffw_layer_end.ffw_layer_1", # gemma3n
"conformer.layers.{bid}.feed_forward2.ffw_layer_1", # gemma4
+ "sound_encoder.encoder.layers.{bid}.feed_forward2.linear1", # parakeet
"encoder.layers.{bid}.ff2.up_proj", # granite_speech
),
"conformer.layers.{bid}.feed_forward2.linear2", # lfm2
"conformer.layers.{bid}.ffw_layer_end.ffw_layer_2", # gemma3n
"conformer.layers.{bid}.feed_forward2.ffw_layer_2", # gemma4
+ "sound_encoder.encoder.layers.{bid}.feed_forward2.linear2", # parakeet
"encoder.layers.{bid}.ff2.down_proj", # granite_speech
),
"conformer.layers.{bid}.norm_feed_forward2", # lfm2
"conformer.layers.{bid}.ffw_layer_end.pre_layer_norm", # gemma3n
"conformer.layers.{bid}.feed_forward2.pre_layer_norm", # gemma4
+ "sound_encoder.encoder.layers.{bid}.norm_feed_forward2", # parakeet
"encoder.layers.{bid}.ff2.pre_norm", # granite_speech
),
MODEL_TENSOR.A_ENC_LINEAR_POS: (
"conformer.layers.{bid}.self_attn.linear_pos", # lfm2
"conformer.layers.{bid}.attention.attn.relative_position_embedding.pos_proj", # gemma3n
+ "sound_encoder.encoder.layers.{bid}.self_attn.relative_k_proj", # parakeet
),
MODEL_TENSOR.A_ENC_POS_BIAS_U: (
"conformer.layers.{bid}.self_attn.pos_bias_u", # lfm2
+ "sound_encoder.encoder.layers.{bid}.self_attn.bias_u", # parakeet
),
MODEL_TENSOR.A_ENC_POS_BIAS_V: (
"conformer.layers.{bid}.self_attn.pos_bias_v", # lfm2
+ "sound_encoder.encoder.layers.{bid}.self_attn.bias_v", # parakeet
),
MODEL_TENSOR.A_ENC_OUT: (
"conformer.pre_encode.out", # lfm2
"model.audio_tower.subsample_conv_projection.input_proj_linear", # gemma3n (note: it should be A_ENC_INP_PROJ, this is a mistake; it should be corrected in C++ code when it's supported)
"conformer.output_proj", # gemma4
+ "sound_encoder.encoder.subsampling.linear", # parakeet
),
# note: some tensors below has "audio." pseudo-prefix, to prevent conflicts with vision tensors
"audio.multi_modal_projector.linear_{bid}", # ultravox, meralion
"audio_adapter.model.{bid}", # lfm2
"audio_tower.proj{bid}", # qwen3omni
+ "sound_projection.linear{bid}", # parakeet (linear1, linear2)
),
MODEL_TENSOR.A_MMPROJ_FC: (
MODEL_TENSOR.A_MM_NORM_PRE: (
"audio.multi_modal_projector.ln_pre", # ultravox
+ "sound_projection.norm", # parakeet
),
MODEL_TENSOR.A_MM_NORM_MID: (
MODEL_TENSOR.A_ENC_CONV_DW: (
"conformer.layers.{bid}.conv.depthwise_conv", # lfm2
"conformer.layers.{bid}.lconv1d.depthwise_conv1d", # gemma3n
+ "sound_encoder.encoder.layers.{bid}.conv.depthwise_conv", # parakeet
"encoder.layers.{bid}.conv.depth_conv.conv", # granite_speech
),
MODEL_TENSOR.A_ENC_CONV_NORM: (
"conformer.layers.{bid}.conv.batch_norm", # lfm2
"conformer.layers.{bid}.lconv1d.pre_layer_norm", # gemma3n
+ "sound_encoder.encoder.layers.{bid}.conv.norm", # parakeet
+ ),
+
+ MODEL_TENSOR.A_ENC_CONV_NORM_MEAN: (
+ "sound_encoder.encoder.layers.{bid}.conv.norm.running_mean", # parakeet
+ ),
+
+ MODEL_TENSOR.A_ENC_CONV_NORM_VAR: (
+ "sound_encoder.encoder.layers.{bid}.conv.norm.running_var", # parakeet
"encoder.layers.{bid}.conv.batch_norm", # granite_speech
),
MODEL_TENSOR.A_ENC_CONV_PW1: (
"conformer.layers.{bid}.conv.pointwise_conv1", # lfm2
"conformer.layers.{bid}.lconv1d.linear_start", # gemma3n
+ "sound_encoder.encoder.layers.{bid}.conv.pointwise_conv1", # parakeet
"encoder.layers.{bid}.conv.up_conv", # granite_speech
),
MODEL_TENSOR.A_ENC_CONV_PW2: (
"conformer.layers.{bid}.conv.pointwise_conv2", # lfm2
"conformer.layers.{bid}.lconv1d.linear_end", # gemma3n
+ "sound_encoder.encoder.layers.{bid}.conv.pointwise_conv2", # parakeet
"encoder.layers.{bid}.conv.down_conv", # granite_speech
),
MODEL_TENSOR.A_ENC_NORM_CONV: (
"conformer.layers.{bid}.norm_conv", # lfm2
"conformer.layers.{bid}.lconv1d.conv_norm", # gemma3n
+ "sound_encoder.encoder.layers.{bid}.norm_conv", # parakeet
"encoder.layers.{bid}.conv.norm", # granite_speech
),
"conformer.layers.{bid}.attention.attn.per_dim_scale", # gemma4
),
+ MODEL_TENSOR.A_ENC_MEL_FILTERS: (
+ "sound_encoder.encoder.feature_extractor.featurizer.fb", # parakeet
+ ),
+
+ MODEL_TENSOR.A_ENC_WINDOW: (
+ "sound_encoder.encoder.feature_extractor.featurizer.window", # parakeet
+ ),
+
MODEL_TENSOR.A_MM_EMBEDDING: (
"model.embed_audio.embedding", # gemma3n
),
models/mobilenetv5.cpp
models/youtuvl.cpp
models/yasa2.cpp
+ models/parakeet.cpp
)
set_target_properties(mtmd PROPERTIES
#define KEY_A_ATTN_WINDOW_SIZE "clip.audio.window_size" // mimo-audio-tokenizer: sliding-window radius
#define KEY_A_LOCAL_BLOCK_COUNT "clip.audio.local_block_count" // mimo-v2.5: input_local_transformer layer count
#define KEY_A_LOCAL_GROUP_SIZE "clip.audio.local_group_size" // mimo-v2.5: input_local_transformer grouping size
+#define KEY_AUDIO_SUBSAMPLING_FACTOR "clip.audio.subsampling_factor"
//
// tensor name constants
#define TN_YASA_STAGE_DOWN_CONV "v.stage.%d.down.conv.%s"
#define TN_YASA_STAGE_BLK "v.stage.%d.blk.%d.%s.%s"
+// parakeet
+#define TN_MEL_FILTERS "a.mel_filters"
+#define TN_WINDOW "a.window"
+#define TN_CONV_NORM_MEAN "%s.blk.%d.conv_norm_mean"
+#define TN_CONV_NORM_VAR "%s.blk.%d.conv_norm_var"
+
// align x to upper multiple of n
#define CLIP_ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n))
PROJECTOR_TYPE_KIMIK25,
PROJECTOR_TYPE_NEMOTRON_V2_VL,
PROJECTOR_TYPE_HUNYUANVL,
+ PROJECTOR_TYPE_PARAKEET,
PROJECTOR_TYPE_EXAONE4_5,
PROJECTOR_TYPE_MINICPMV4_6,
PROJECTOR_TYPE_GRANITE_SPEECH,
{ PROJECTOR_TYPE_MINIMAX_M3, "minimax_m3"},
{ PROJECTOR_TYPE_GRANITE4_VISION, "granite4_vision"},
{ PROJECTOR_TYPE_MIMO_AUDIO, "mimo_audio"},
+ { PROJECTOR_TYPE_PARAKEET, "parakeet"},
};
static projector_type clip_projector_type_from_string(const std::string & str) {
// audio
int32_t n_mel_bins = 0; // whisper preprocessor
int32_t proj_stack_factor = 0; // ultravox
+ int32_t subsampling_factor = 0; // parakeet
+
int32_t audio_chunk_size = 0;
int32_t audio_conv_kernel_size = 0;
int32_t audio_max_pos_emb = 0;
int32_t audio_window_len = -1;
int32_t audio_hop_len = -1;
+ // parakeet
+ std::vector<float> mel_filters;
+ std::vector<float> window;
+
// mimo-audio-tokenizer: residual vector quantizer
int32_t rvq_num_quantizers = 0;
std::vector<int32_t> rvq_codebook_size; // per-quantizer bin count (ragged, e.g. 1024/1024/256/128x17)
ggml_tensor * norm_conv_b = nullptr;
ggml_tensor * linear_pos_w = nullptr;
- ggml_tensor * conv_norm_w = nullptr;
- ggml_tensor * conv_norm_b = nullptr;
- ggml_tensor * conv_dw_w = nullptr;
- ggml_tensor * conv_dw_b = nullptr;
- ggml_tensor * conv_pw1_w = nullptr;
- ggml_tensor * conv_pw1_b = nullptr;
- ggml_tensor * conv_pw2_w = nullptr;
- ggml_tensor * conv_pw2_b = nullptr;
+ ggml_tensor * conv_norm_w = nullptr;
+ ggml_tensor * conv_norm_b = nullptr;
+ ggml_tensor * conv_norm_mean = nullptr; // parakeet
+ ggml_tensor * conv_norm_var = nullptr; // parakeet
+ ggml_tensor * conv_dw_w = nullptr;
+ ggml_tensor * conv_dw_b = nullptr;
+ ggml_tensor * conv_pw1_w = nullptr;
+ ggml_tensor * conv_pw1_b = nullptr;
+ ggml_tensor * conv_pw2_w = nullptr;
+ ggml_tensor * conv_pw2_b = nullptr;
// gemma4 audio conformer per-layer
ggml_tensor * attn_pre_norm_w = nullptr;
{
builder = std::make_unique<clip_graph_yasa2>(ctx, img);
} break;
+ case PROJECTOR_TYPE_PARAKEET:
+ {
+ builder = std::make_unique<clip_graph_parakeet>(ctx, img);
+ } break;
case PROJECTOR_TYPE_GRANITE4_VISION:
{
builder = std::make_unique<clip_graph_granite4_vision>(ctx, img);
{
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
} break;
+ case PROJECTOR_TYPE_PARAKEET:
+ {
+ get_u32(KEY_AUDIO_SUBSAMPLING_FACTOR, hparams.subsampling_factor);
+ GGML_ASSERT(hparams.subsampling_factor == 8 &&
+ "subsampling_factor must match the conv strides in clip_graph_parakeet::build()");
+ get_u32(KEY_A_CONV_KERNEL_SIZE, hparams.audio_conv_kernel_size);
+ GGML_ASSERT(hparams.audio_conv_kernel_size > 0 && hparams.audio_conv_kernel_size % 2 == 1 &&
+ "audio_conv_kernel_size must be a positive odd integer");
+ hparams.audio_chunk_len = 0;
+ hparams.audio_sample_rate = 16000;
+ hparams.audio_n_fft = 512;
+ hparams.audio_window_len = 400;
+ hparams.audio_hop_len = 160;
+ } break;
case PROJECTOR_TYPE_IDEFICS3:
{
// use default llava-uhd preprocessing params
return cur;
};
- auto get_scalar = [&](const std::string & name, float default_val) {
+ auto get_vector = [&](const std::string & name) {
+ std::vector<float> result;
auto it = tensor_offset.find(name);
if (it == tensor_offset.end()) {
+ return result;
+ }
+
+ const int64_t idx = gguf_find_tensor(ctx_gguf.get(), name.c_str());
+ if (idx < 0) {
+ throw std::runtime_error(string_format("%s: failed to find tensor %s\n", __func__, name.c_str()));
+ }
+
+ if (const auto type = gguf_get_tensor_type(ctx_gguf.get(), idx); type != GGML_TYPE_F32) {
+ throw std::runtime_error(string_format("%s: %s must be %s, was %s\n", __func__,
+ name.c_str(), ggml_type_name(GGML_TYPE_F32), ggml_type_name(type)));
+ }
+
+ const size_t n_bytes = gguf_get_tensor_size(ctx_gguf.get(), idx);
+ if (n_bytes == 0) {
+ throw std::runtime_error(string_format("%s: tensor %s is empty\n", __func__, name.c_str()));
+ }
+
+ const size_t n_elems = n_bytes / sizeof(float);
+ result.resize(n_elems);
+ fin.seekg(it->second, std::ios::beg);
+ fin.read(reinterpret_cast<char*>(result.data()), n_bytes);
+ return result;
+ };
+
+ auto get_scalar = [&](const std::string & name, float default_val) {
+ auto v = get_vector(name);
+ if (v.empty()) {
return default_val;
}
- size_t offset = it->second;
- fin.seekg(offset, std::ios::beg);
- float value;
- fin.read(reinterpret_cast<char*>(&value), sizeof(float));
- return value;
+ if (v.size() != 1) {
+ throw std::runtime_error(string_format("%s: expected scalar tensor '%s' but got %d elements\n",
+ __func__, name.c_str(), (int) v.size()));
+ }
+
+ return v[0];
};
model.class_embedding = get_tensor(TN_CLASS_EMBD, false);
layer.conv_pw2_b = get_tensor(string_format(TN_CONV_PW2, prefix, il, "bias"));
}
} break;
+ case PROJECTOR_TYPE_PARAKEET:
+ {
+
+ hparams.mel_filters = get_vector(TN_MEL_FILTERS);
+ hparams.window = get_vector(TN_WINDOW);
+
+ // Subsampling layers (conv1d)
+ for (int i : {0, 2, 3, 5, 6}) {
+ model.pre_encode_conv_X_w[i] = get_tensor(string_format(TN_CONV1D, i, "weight"));
+ model.pre_encode_conv_X_b[i] = get_tensor(string_format(TN_CONV1D, i, "bias"));
+ }
+ model.pre_encode_out_w = get_tensor(string_format(TN_PRE_ENCODE_OUT, "weight"));
+ model.pre_encode_out_b = get_tensor(string_format(TN_PRE_ENCODE_OUT, "bias"));
+
+ // Projection layers
+ model.mm_norm_pre_w = get_tensor(string_format(TN_MM_NORM_PRE, "weight"), false);
+ model.mm_0_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight"), false);
+ model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight"), false);
+
+ // Encoder layers
+ for (int il = 0; il < hparams.n_layer; ++il) {
+ auto & layer = model.layers[il];
+
+ // Attention (from shared above)
+
+ // Relative position encoding
+ layer.linear_pos_w = get_tensor(string_format(TN_LINEAR_POS, prefix, il, "weight"));
+ layer.pos_bias_u = get_tensor(string_format(TN_POS_BIAS_U, prefix, il));
+ layer.pos_bias_v = get_tensor(string_format(TN_POS_BIAS_V, prefix, il));
+
+ // Convolution module
+ layer.conv_pw1_w = get_tensor(string_format(TN_CONV_PW1, prefix, il, "weight"));
+ layer.conv_pw1_b = get_tensor(string_format(TN_CONV_PW1, prefix, il, "bias"), false);
+ layer.conv_dw_w = get_tensor(string_format(TN_CONV_DW, prefix, il, "weight"));
+ layer.conv_dw_b = get_tensor(string_format(TN_CONV_DW, prefix, il, "bias"), false);
+ layer.conv_norm_w = get_tensor(string_format(TN_CONV_NORM, prefix, il, "weight"));
+ layer.conv_norm_b = get_tensor(string_format(TN_CONV_NORM, prefix, il, "bias"));
+ layer.conv_norm_mean = get_tensor(string_format(TN_CONV_NORM_MEAN, prefix, il));
+ layer.conv_norm_var = get_tensor(string_format(TN_CONV_NORM_VAR, prefix, il));
+ layer.conv_pw2_w = get_tensor(string_format(TN_CONV_PW2, prefix, il, "weight"));
+ layer.conv_pw2_b = get_tensor(string_format(TN_CONV_PW2, prefix, il, "bias"), false);
+
+ // Feed-forward networks
+ layer.ff_norm_w = get_tensor(string_format(TN_FFN_NORM, prefix, il, "weight"));
+ layer.ff_norm_b = get_tensor(string_format(TN_FFN_NORM, prefix, il, "bias"));
+
+ layer.ff_norm_1_w = get_tensor(string_format(TN_FFN_NORM_1, prefix, il, "weight"));
+ layer.ff_norm_1_b = get_tensor(string_format(TN_FFN_NORM_1, prefix, il, "bias"));
+ layer.ff_up_1_w = get_tensor(string_format(TN_FFN_UP_1, prefix, il, "weight"));
+ layer.ff_up_1_b = get_tensor(string_format(TN_FFN_UP_1, prefix, il, "bias"), false);
+ layer.ff_down_1_w = get_tensor(string_format(TN_FFN_DOWN_1, prefix, il, "weight"));
+ layer.ff_down_1_b = get_tensor(string_format(TN_FFN_DOWN_1, prefix, il, "bias"), false);
+
+ // Layer norms
+ layer.norm_conv_w = get_tensor(string_format(TN_NORM_CONV, prefix, il, "weight"));
+ layer.norm_conv_b = get_tensor(string_format(TN_NORM_CONV, prefix, il, "bias"));
+ }
+
+ model.mm_model_mlp_1_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "weight"));
+ model.mm_model_mlp_2_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "weight"));
+ model.mm_model_mlp_3_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "weight"));
+ } break;
case PROJECTOR_TYPE_GRANITE_SPEECH:
{
model.inp_proj_w = get_tensor(string_format(TN_INP_PROJ, "weight"));
}
n_patches = n;
} break;
+ case PROJECTOR_TYPE_PARAKEET:
+ {
+ n_patches = (img->nx() + (params.subsampling_factor - 1)) / params.subsampling_factor;
+ } break;
case PROJECTOR_TYPE_GEMMA4UA:
{
n_patches = img->nx(); // no downsampling: one token per raw waveform frame
}
set_input_f32("pos_emb", pos_emb);
} break;
+ case PROJECTOR_TYPE_PARAKEET:
+ {
+ GGML_ASSERT(imgs.entries.size() == 1);
+ struct ggml_tensor * attn_mask = ggml_graph_get_tensor(gf, "attn_mask");
+ const int n_q = attn_mask->ne[1];
+ const int n_k = attn_mask->ne[0];
+ const int n_frames = imgs.entries.front().nx();
+ const int n_tokens_real = (n_frames + hparams.subsampling_factor-1) / hparams.subsampling_factor;
+ const float mask_value = -1e30f;
+
+ std::vector<float> mask_data(n_q * n_k);
+ if (n_k == n_q) {
+ // full attention: mask keys that are padding
+ for (int q = 0; q < n_q; ++q) {
+ for (int k = 0; k < n_k; ++k) {
+ mask_data[q * n_k + k] = (k >= n_tokens_real) ? mask_value : 0.0f;
+ }
+ }
+ } else {
+ // local attention: mask keys outside the valid window
+ const int att_left = n_k / 2;
+ for (int q = 0; q < n_q; ++q) {
+ for (int k = 0; k < n_k; ++k) {
+ const int key = q - att_left + k;
+ mask_data[q * n_k + k] = (key >= 0 && key < n_tokens_real) ? 0.0f : mask_value;
+ }
+ }
+ }
+ set_input_f32(attn_mask->name, mask_data);
+
+ // local attention skew mask: zeroes out the probs that were
+ // computed for keys outside the valid sliding window.
+ if (struct ggml_tensor * local_mask = ggml_graph_get_tensor(gf, "local_mask")) {
+ const int lm_k = local_mask->ne[0];
+ const int lm_q = local_mask->ne[1];
+ const int window_size = lm_k - lm_q + 1;
+ std::vector<float> lm_data(lm_q * lm_k);
+ for (int q = 0; q < lm_q; ++q) {
+ for (int k = 0; k < lm_k; ++k) {
+ const int rel = k - q;
+ lm_data[q * lm_k + k] = (rel >= 0 && rel < window_size) ? 1.0f : 0.0f;
+ }
+ }
+ set_input_f32(local_mask->name, lm_data);
+ }
+
+ // Generate rotation frequencies for relative positional encoding.
+ {
+ const int n_state = hparams.n_embd;
+ const int d_half = n_state / 2;
+ const float log_10000 = logf(10000.0f);
+ std::vector<float> freqs(d_half);
+ for (int k = 0; k < d_half; ++k) {
+ freqs[k] = expf(-(float(k * 2) * log_10000 / float(n_state)));
+ }
+ set_input_f32("pos_freqs", freqs);
+ }
+
+ // Generate relative positional distance values which scaled by
+ // the frequency to produce the angles for sin/cos.
+ {
+ // window_size is only known after graph construction since it depends on
+ // n_time from the conv output, so we read it back from the graph tensor.
+ struct ggml_tensor * rel_pos = ggml_graph_get_tensor(gf, "rel_positions");
+ const int window_size = rel_pos->ne[1];
+ std::vector<float> pos(window_size);
+ // local attention: window is fixed at [att_left, att_right]
+ // full attention: window covers the full sequence, centered
+ if (ggml_graph_get_tensor(gf, "local_mask")) {
+ const int att_left = window_size / 2;
+ for (int t = 0; t < window_size; ++t) {
+ pos[t] = float(att_left - t);
+ }
+ } else {
+ const int n_time = (window_size + 1) / 2;
+ for (int t = 0; t < window_size; ++t) {
+ pos[t] = float(n_time - 1 - t);
+ }
+ }
+ set_input_f32(rel_pos->name, pos);
+ }
+ } break;
case PROJECTOR_TYPE_GRANITE_SPEECH:
{
const int context_size = ctx->model.hparams.audio_chunk_size;
return ctx->model.mm_ffn_down_w->ne[1];
case PROJECTOR_TYPE_MIMO_AUDIO:
return ctx->model.mm_2_w->ne[1];
+ case PROJECTOR_TYPE_PARAKEET:
+ return ctx->model.mm_1_w->ne[1];
default:
GGML_ABORT("Unknown projector type");
}
ggml_tensor * resize_position_embeddings_3d(uint32_t interpolation_mode);
};
+struct clip_graph_parakeet : clip_graph {
+ clip_graph_parakeet(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+ ggml_cgraph * build() override;
+};
+
struct clip_graph_exaone4_5 : clip_graph {
clip_graph_exaone4_5(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
--- /dev/null
+#include "models.h"
+
+static constexpr int PARAKEET_LOCAL_ATTN_THRESHOLD = 8192;
+static constexpr int PARAKEET_LOCAL_ATTN_WINDOW = 128;
+
+// conv subsampling + conformer encoder
+ggml_cgraph * clip_graph_parakeet::build() {
+
+ // Conv subsampling
+ ggml_tensor * inp = build_inp_raw(1);
+ inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
+
+ // [freq, time, channels, batch]
+ ggml_tensor * cur = ggml_conv_2d(ctx0, model.pre_encode_conv_X_w[0], inp, 2, 2, 1, 1, 1, 1);
+ cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[0]);
+ cb(cur, "pre_conv_0", -1);
+
+ cur = ggml_relu(ctx0, cur);
+ cb(cur, "pre_conv_0_relu", -1);
+
+ // [freq, time, channels, batch]
+ cur = ggml_conv_2d_dw_direct(ctx0, model.pre_encode_conv_X_w[2], cur, 2, 2, 1, 1, 1, 1);
+ cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[2]);
+ cb(cur, "pre_conv_2", -1);
+
+ // [freq, time, channels, batch]
+ cur = ggml_conv_2d(ctx0, model.pre_encode_conv_X_w[3], cur, 1, 1, 0, 0, 1, 1);
+ cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[3]);
+ cb(cur, "pre_conv_3", -1);
+
+ cur = ggml_relu(ctx0, cur);
+ cb(cur, "pre_conv_3_relu", -1);
+
+ // [freq, time, channels, batch]
+ cur = ggml_conv_2d_dw_direct(ctx0, model.pre_encode_conv_X_w[5], cur, 2, 2, 1, 1, 1, 1);
+ cb(cur, "pre_conv_5_direct", -1);
+ cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[5]);
+ cb(cur, "pre_conv_5", -1);
+
+ // [freq, time, channels, batch]
+ cur = ggml_conv_2d(ctx0, model.pre_encode_conv_X_w[6], cur, 1, 1, 0, 0, 1, 1);
+ cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[6]);
+ cb(cur, "pre_conv_6", -1);
+
+ cur = ggml_relu(ctx0, cur);
+ cb(cur, "pre_conv_6_relu", -1);
+
+ // [freq, time, chan]
+ cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
+ // [freq, chan, time]
+ cur = ggml_cont(ctx0, cur);
+
+ const int n_freq = cur->ne[0];
+ const int n_chan = cur->ne[1];
+ const int n_frames = cur->ne[2];
+
+ // [freq, time, chan, batch] -> [(freq * chan), time]
+ cur = ggml_reshape_2d(ctx0, cur, n_freq * n_chan, n_frames);
+
+ cur = build_mm(model.pre_encode_out_w, cur);
+ cur = ggml_add(ctx0, cur, model.pre_encode_out_b);
+
+ ggml_set_name(cur, "pre_enc_out");
+
+ // Encoder
+
+ const auto & hparams = model.hparams;
+ const int n_layer = hparams.n_layer;
+ const int n_state = hparams.n_embd;
+ const float fc_factor = 0.5f;
+
+ const int n_time = cur->ne[1];
+ const bool local_attn = n_time > PARAKEET_LOCAL_ATTN_THRESHOLD;
+ const int att_left = local_attn ? PARAKEET_LOCAL_ATTN_WINDOW : n_time - 1;
+ const int att_right = local_attn ? PARAKEET_LOCAL_ATTN_WINDOW : n_time - 1;
+ const int window_size = local_attn ? att_left + att_right + 1 : 2 * n_time - 1;
+ const int d_half = n_state / 2;
+ const int mask_dim = local_attn ? window_size : n_time;
+
+ // mask [key, n_time]
+ struct ggml_tensor * attn_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, mask_dim, n_time);
+ ggml_set_name(attn_mask, "attn_mask");
+ ggml_set_input(attn_mask);
+
+ struct ggml_tensor * local_mask = nullptr;
+ if (local_attn) {
+ const int chunk = att_left + att_right;
+ local_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, chunk + window_size - 1, chunk);
+ ggml_set_name(local_mask, "local_mask");
+ ggml_set_input(local_mask);
+ }
+
+ struct ggml_tensor * pos_freqs = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, d_half);
+ ggml_set_name(pos_freqs, "pos_freqs");
+ ggml_set_input(pos_freqs);
+
+ struct ggml_tensor * rel_positions = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, window_size);
+ ggml_set_name(rel_positions, "rel_positions");
+ ggml_set_input(rel_positions);
+
+ struct ggml_tensor * freqs = ggml_repeat_4d(ctx0, pos_freqs, d_half, window_size, 1, 1);
+ struct ggml_tensor * theta = ggml_mul(ctx0, freqs, rel_positions);
+
+ struct ggml_tensor * sin = ggml_reshape_3d(ctx0, ggml_sin(ctx0, theta), 1, d_half, window_size);
+ struct ggml_tensor * cos = ggml_reshape_3d(ctx0, ggml_cos(ctx0, theta), 1, d_half, window_size);
+ struct ggml_tensor * pos_emb = ggml_reshape_2d(ctx0, ggml_cont(ctx0, ggml_concat(ctx0, sin, cos, 0)), n_state, window_size);
+ ggml_set_name(pos_emb, "pos_emb");
+
+ for (int il = 0; il < n_layer; ++il) {
+ const auto & layer = model.layers[il];
+ // FFN1
+ {
+ struct ggml_tensor * residual = cur;
+ ggml_format_name(cur, "enc_%d_res", il);
+
+ // norm
+ cur = ggml_norm(ctx0, cur, hparams.eps);
+ cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.ff_norm_w), layer.ff_norm_b);
+ ggml_format_name(cur, "enc_%d_ffn_norm_1", il);
+
+ cur = build_ffn(cur, layer.ff_up_w, nullptr, nullptr, nullptr, layer.ff_down_w, nullptr, FFN_SILU, il);
+ ggml_format_name(cur, "enc_%d_ffn_1", il);
+
+ cur = ggml_add(ctx0, residual, ggml_scale(ctx0, cur, fc_factor));
+ ggml_format_name(cur, "enc_%d_res_ffn", il);
+ }
+
+ // self attention block using relative positional encoding from model.position_embedding.
+ {
+ // [feat, time_frames, 1, 1]
+ struct ggml_tensor * residual = cur;
+
+ cur = ggml_norm(ctx0, cur, hparams.eps);
+ cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.ln_1_w), layer.ln_1_b);
+ ggml_format_name(cur, "enc_%d_attn_norm", il);
+
+ const int n_head = hparams.n_head;
+ const int d_head = n_state / n_head;
+
+ // [feat, time_frames, 1, 1]
+ struct ggml_tensor * Q_cur = build_mm(layer.q_w, cur);
+ struct ggml_tensor * K_cur = build_mm(layer.k_w, cur);
+ struct ggml_tensor * V_cur = build_mm(layer.v_w, cur);
+
+ // [d_head, n_heads, n_time, 1]
+ Q_cur = ggml_reshape_3d(ctx0, Q_cur, d_head, n_head, n_time);
+ K_cur = ggml_reshape_3d(ctx0, K_cur, d_head, n_head, n_time);
+ V_cur = ggml_reshape_3d(ctx0, V_cur, d_head, n_head, n_time);
+
+ // [n_state, window_size]
+ struct ggml_tensor * pos = build_mm(layer.linear_pos_w, pos_emb);
+ // [feat, head, window_size, 1]
+ pos = ggml_reshape_3d(ctx0, pos, d_head, n_head, pos_emb->ne[1]);
+ // [feat, window_size, head, 1]
+ pos = ggml_cont(ctx0, ggml_permute(ctx0, pos, 0, 2, 1, 3));
+ ggml_format_name(pos, "enc_%d_attn_pos", il);
+
+ if (local_attn) {
+ const int chunk = att_left + att_right;
+ const int n_group = (n_time + chunk - 1) / chunk;
+ const int n_time_padded = n_group * chunk;
+ const int n_kv_chunk = chunk + window_size - 1;
+ const int n_kv_dense = n_kv_chunk * n_group;
+ const bool need_padding = n_time_padded > n_time;
+
+ Q_cur = ggml_cont(ctx0, ggml_permute(ctx0, Q_cur, 0, 2, 1, 3));
+ K_cur = ggml_cont(ctx0, ggml_permute(ctx0, K_cur, 0, 2, 1, 3));
+ V_cur = ggml_cont(ctx0, ggml_permute(ctx0, V_cur, 0, 2, 1, 3));
+
+ // content bias
+ struct ggml_tensor * bias_u = ggml_reshape_3d(ctx0, layer.pos_bias_u, d_head, 1, n_head);
+ struct ggml_tensor * Q_u = ggml_add(ctx0, Q_cur, bias_u);
+
+ // position bias
+ struct ggml_tensor * bias_v = ggml_reshape_3d(ctx0, layer.pos_bias_v, d_head, 1, n_head);
+ struct ggml_tensor * Q_v = ggml_add(ctx0, Q_cur, bias_v);
+
+ // right pad the time dimension
+ struct ggml_tensor * Q_u_padded = need_padding ?
+ ggml_pad_ext(ctx0, Q_u, 0, 0, 0, n_time_padded - n_time, 0, 0, 0, 0) : Q_u;
+ Q_u_padded = ggml_reshape_4d(ctx0, Q_u_padded, d_head, chunk, n_group, n_head);
+
+ // pad front and back for the first and last time frames
+ struct ggml_tensor * K_padded = ggml_pad_ext(ctx0, K_cur, 0, 0, att_left, att_right, 0, 0, 0, 0);
+ if (n_kv_dense > K_padded->ne[1]) {
+ K_padded = ggml_pad_ext(ctx0, K_padded, 0, 0, 0, n_kv_dense - K_padded->ne[1], 0, 0, 0, 0);
+ }
+
+ // sliding window view: each group spans n_kv_chunk keys but steps by chunk
+ struct ggml_tensor * K_chunk = ggml_view_4d(ctx0, K_padded,
+ d_head, n_kv_chunk, n_group, n_head,
+ K_padded->nb[1],
+ (size_t) chunk * K_padded->nb[1],
+ K_padded->nb[2],
+ 0);
+ K_chunk = ggml_cont(ctx0, K_chunk);
+
+ struct ggml_tensor * content_scores = ggml_mul_mat(ctx0, K_chunk, Q_u_padded);
+
+ // trim the dense output down to window_size scores per query
+ content_scores = ggml_view_4d(ctx0, content_scores,
+ window_size, chunk, n_group, n_head,
+ (size_t) (chunk + window_size) * content_scores->nb[0],
+ content_scores->nb[2],
+ content_scores->nb[3],
+ 0);
+ content_scores = ggml_cont(ctx0, content_scores);
+
+ // ungroup: [window_size, n_time_padded, n_head]
+ content_scores = ggml_reshape_3d(ctx0, content_scores, window_size, n_time_padded, n_head);
+ if (need_padding) {
+ content_scores = ggml_view_3d(ctx0, content_scores,
+ window_size, n_time, n_head,
+ content_scores->nb[1],
+ content_scores->nb[2],
+ 0);
+ }
+
+ // Q_v: [d_head, time, head]
+ Q_v = ggml_cont(ctx0, ggml_permute(ctx0, Q_v, 0, 2, 1, 3));
+ struct ggml_tensor * rel_pos_scores = ggml_mul_mat(ctx0, pos, Q_v);
+
+ struct ggml_tensor * attn_scores = ggml_add(ctx0, content_scores, rel_pos_scores);
+ attn_scores = ggml_soft_max_ext(ctx0, attn_scores, attn_mask, 1.0f / std::sqrt(d_head), 0.0f);
+ ggml_format_name(attn_scores, "enc_%d_attn_probs", il);
+
+ // expand probs back to n_kv_chunk width for the V matmul
+ struct ggml_tensor * probs_padded = need_padding ?
+ ggml_pad_ext(ctx0, attn_scores, 0, 0, 0, n_time_padded - n_time, 0, 0, 0, 0) : attn_scores;
+
+ probs_padded = ggml_reshape_4d(ctx0, probs_padded, window_size, chunk, n_group, n_head);
+ probs_padded = ggml_pad_ext(ctx0, probs_padded, 0, chunk, 0, 0, 0, 0, 0, 0);
+ probs_padded = ggml_view_4d(ctx0, probs_padded,
+ n_kv_chunk, chunk, n_group, n_head,
+ (size_t) n_kv_chunk * probs_padded->nb[0],
+ probs_padded->nb[2],
+ probs_padded->nb[3],
+ 0);
+ probs_padded = ggml_cont(ctx0, probs_padded);
+ probs_padded = ggml_mul(ctx0, probs_padded, local_mask);
+
+ struct ggml_tensor * V_padded = ggml_pad_ext(ctx0, V_cur, 0, 0, att_left, att_right, 0, 0, 0, 0);
+ if (n_kv_dense > V_padded->ne[1]) {
+ V_padded = ggml_pad_ext(ctx0, V_padded, 0, 0, 0, n_kv_dense - V_padded->ne[1], 0, 0, 0, 0);
+ }
+ V_padded = ggml_cont(ctx0, ggml_transpose(ctx0, V_padded));
+
+ struct ggml_tensor * V_chunk = ggml_view_4d(ctx0, V_padded,
+ n_kv_chunk, d_head, n_group, n_head,
+ V_padded->nb[1],
+ (size_t) chunk * V_padded->nb[0],
+ V_padded->nb[2],
+ 0);
+ V_chunk = ggml_cont(ctx0, V_chunk);
+
+ cur = ggml_mul_mat(ctx0, V_chunk, probs_padded);
+ cur = ggml_reshape_3d(ctx0, cur, d_head, n_time_padded, n_head);
+ if (need_padding) {
+ cur = ggml_view_3d(ctx0, cur, d_head, n_time, n_head, cur->nb[1], cur->nb[2], 0);
+ }
+ cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3));
+ cur = ggml_reshape_2d(ctx0, cur, n_state, n_time);
+ cur = build_mm(layer.o_w, cur);
+ } else {
+ // full attention
+ struct ggml_tensor * Q_u = ggml_add(ctx0, Q_cur, layer.pos_bias_u);
+ ggml_format_name(Q_u, "enc_%d_attn_q_u", il);
+
+ struct ggml_tensor * K_prep = ggml_permute(ctx0, K_cur, 0, 2, 1, 3);
+ struct ggml_tensor * Q_prep = ggml_permute(ctx0, Q_u, 0, 2, 1, 3);
+ struct ggml_tensor * content_scores = ggml_mul_mat(ctx0, K_prep, Q_prep);
+ ggml_format_name(content_scores, "enc_%d_attn_content_scores", il);
+
+ struct ggml_tensor * Q_v = ggml_add(ctx0, Q_cur, layer.pos_bias_v);
+ ggml_format_name(Q_v, "enc_%d_attn_q_v", il);
+
+ Q_v = ggml_permute(ctx0, Q_v, 0, 2, 1, 3);
+ Q_v = ggml_cont(ctx0, Q_v);
+ ggml_format_name(Q_v, "enc_%d_attn_q_v_perm", il);
+
+ struct ggml_tensor * rel_pos_scores = ggml_mul_mat(ctx0, pos, Q_v);
+ ggml_format_name(rel_pos_scores, "enc_%d_attn_rel_pos", il);
+
+ // Relative positional shift
+ {
+ const auto pos_window = rel_pos_scores->ne[0];
+ const auto n_frame = rel_pos_scores->ne[1];
+ const auto n_head = rel_pos_scores->ne[2];
+
+ rel_pos_scores = ggml_pad(ctx0, rel_pos_scores, 1, 0, 0, 0);
+ rel_pos_scores = ggml_roll(ctx0, rel_pos_scores, 1, 0, 0, 0);
+
+ rel_pos_scores = ggml_reshape_3d(ctx0, rel_pos_scores, n_frame, pos_window + 1, n_head);
+ rel_pos_scores = ggml_cont(ctx0, rel_pos_scores);
+ ggml_format_name(rel_pos_scores, "enc_%d_attn_rel_pos_reshaped", il);
+
+ int center = pos_window / 2;
+ size_t offset = rel_pos_scores->nb[0] * (center+1);
+
+ rel_pos_scores = ggml_view_3d(ctx0, rel_pos_scores,
+ n_frame, pos_window, n_head,
+ (pos_window) * 4,
+ rel_pos_scores->nb[2],
+ offset);
+ rel_pos_scores = ggml_cont(ctx0, rel_pos_scores);
+ ggml_format_name(rel_pos_scores, "enc_%d_attn_rel_pos_shifted", il);
+
+ rel_pos_scores = ggml_view_3d(ctx0, rel_pos_scores,
+ content_scores->ne[0],
+ content_scores->ne[1],
+ rel_pos_scores->ne[2],
+ rel_pos_scores->nb[1],
+ rel_pos_scores->nb[2],
+ 0);
+ rel_pos_scores = ggml_cont(ctx0, rel_pos_scores);
+ ggml_format_name(rel_pos_scores, "enc_%d_attn_rel_pos_shifted_view", il);
+ }
+
+ struct ggml_tensor * attn_scores = ggml_add(ctx0, content_scores, rel_pos_scores);
+ ggml_format_name(attn_scores, "enc_%d_attn_scores", il);
+ attn_scores = ggml_scale(ctx0, attn_scores, 1.0f / std::sqrt(d_head));
+ attn_scores = ggml_add(ctx0, attn_scores, attn_mask);
+ ggml_format_name(attn_scores, "enc_%d_attn_scores_scaled", il);
+
+ struct ggml_tensor * probs = ggml_soft_max(ctx0, attn_scores);
+ ggml_format_name(probs, "enc_%d_attn_probs", il);
+
+ V_cur = ggml_cont(ctx0, ggml_permute(ctx0, V_cur, 1, 2, 0, 3));
+ ggml_format_name(V_cur, "enc_%d_attn_v_cur", il);
+ cur = ggml_mul_mat(ctx0, probs, V_cur);
+ ggml_format_name(cur, "enc_%d_attn_inp", il);
+
+ cur = ggml_permute(ctx0, cur, 2, 0, 1, 3);
+ cur = ggml_cont_2d(ctx0, cur, n_state, n_time);
+ cur = build_mm(layer.o_w, cur);
+ }
+ ggml_format_name(cur, "enc_%d_attn_out", il);
+
+ cur = ggml_add(ctx0, residual, cur);
+ ggml_format_name(cur, "enc_%d_attn_res", il);
+ }
+
+ // Convolution
+ {
+ struct ggml_tensor * residual = cur;
+ ggml_format_name(cur, "enc_%d_residual_conv", il);
+
+ cur = ggml_norm(ctx0, cur, hparams.eps);
+ cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.norm_conv_w), layer.norm_conv_b);
+ ggml_format_name(cur, "enc_%d_norm_conv", il);
+
+ // pointwise 1d convolution:
+ cur = build_mm(layer.conv_pw1_w, cur);
+ ggml_format_name(cur, "enc_%d_conv_pw1", il);
+
+ {
+ int64_t d = cur->ne[0] / 2;
+ struct ggml_tensor * signal = ggml_view_2d(ctx0, cur, d, cur->ne[1], cur->nb[1], 0);
+ struct ggml_tensor * gate = ggml_view_2d(ctx0, cur, d, cur->ne[1], cur->nb[1], d * cur->nb[0]);
+
+ cur = ggml_mul(ctx0, signal, ggml_sigmoid(ctx0, gate));
+ ggml_format_name(cur, "enc_%d_conv_glu", il);
+ }
+
+ cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+
+ // use ggml_ssm_conv for f32 precision
+ const int dw_pad = (hparams.audio_conv_kernel_size - 1) / 2;
+ cur = ggml_pad(ctx0, cur, dw_pad, 0, 0, 0);
+ cur = ggml_roll(ctx0, cur, dw_pad, 0, 0, 0);
+ cur = ggml_pad(ctx0, cur, dw_pad, 0, 0, 0);
+ ggml_format_name(cur, "enc_%d_conv_dw_pad", il);
+
+ cur = ggml_ssm_conv(ctx0, cur, layer.conv_dw_w);
+ ggml_format_name(cur, "enc_%d_conv_1d_dw", il);
+
+ cur = ggml_sub(ctx0, cur, layer.conv_norm_mean);
+ struct ggml_tensor * std = ggml_sqrt(ctx0, layer.conv_norm_var);
+ cur = ggml_div(ctx0, cur, std);
+ cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.conv_norm_w), layer.conv_norm_b);
+ ggml_format_name(cur, "enc_%d_conv_bn", il);
+
+ cur = ggml_silu(ctx0, cur);
+ ggml_format_name(cur, "enc_%d_conv_silu", il);
+
+ cur = build_mm(layer.conv_pw2_w, cur);
+ ggml_format_name(cur, "enc_%d_conv_pw2", il);
+
+ cur = ggml_add(ctx0, residual, cur);
+ ggml_format_name(cur, "enc_%d_conv_res", il);
+ }
+
+ // FFN2
+ {
+ struct ggml_tensor * residual = cur;
+ cur = ggml_norm(ctx0, cur, hparams.eps);
+ cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.ff_norm_1_w), layer.ff_norm_1_b);
+ ggml_format_name(cur, "enc_%d_ffn_norm_2", il);
+
+ cur = build_ffn(cur, layer.ff_up_1_w, nullptr, nullptr, nullptr, layer.ff_down_1_w, nullptr, FFN_SILU, il);
+ cur = ggml_add(ctx0, residual, ggml_scale(ctx0, cur, 0.5));
+ ggml_format_name(cur, "enc_%d_ffn_res", il);
+ }
+
+ cur = ggml_norm(ctx0, cur, hparams.eps);
+ cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.ln_2_w), layer.ln_2_b);
+ }
+
+ cb(cur, "encoder_out", -1);
+
+ cur = ggml_rms_norm(ctx0, cur, 1e-6);
+ cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w);
+ cb(cur, "sound_projection.norm", -1);
+
+ cur = build_ffn(cur, model.mm_0_w, model.mm_0_b, nullptr, nullptr, model.mm_1_w, model.mm_1_b, FFN_RELU_SQR, -1);
+ cb(cur, "projected", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+}
}
//
+// mtmd_audio_preprocessor_parakeet implementation
+//
+
+void mtmd_audio_preprocessor_parakeet::worker_thread(
+ int ith,
+ const float * window_func,
+ int window_size,
+ const std::vector<float> & samples,
+ int n_samples,
+ int frame_size,
+ int frame_step,
+ int n_threads,
+ int n_fft_bins,
+ const mtmd_audio_cache & cache,
+ mtmd_audio_mel & mel) {
+ std::vector<float> fft_in(frame_size * 2, 0.0);
+ std::vector<float> fft_out(frame_size * 2 * 2 * 2);
+
+ int n_fb = n_fft_bins;
+ int i = ith;
+
+ GGML_ASSERT(n_fb == 1 + (frame_size / 2));
+
+ const double eps = 5.960464477539063e-08;
+
+ for (; i < std::min(n_samples / frame_step + 1, (int) mel.n_len); i += n_threads) {
+ const int offset = i * frame_step;
+ const int window_pad_left = (frame_size - window_size) / 2;
+
+ // Zero-pad left.
+ std::fill(fft_in.begin(), fft_in.begin() + window_pad_left, 0.0f);
+
+ // Apply windowed samples in the center.
+ const int n_to_process = std::min({window_size, n_samples - offset});
+ for (int j = 0; j < n_to_process; j++) {
+ fft_in[window_pad_left + j] = window_func[j] * samples[offset + window_pad_left + j];
+ }
+
+ // Zero-pad right.
+ std::fill(fft_in.begin() + window_pad_left + n_to_process, fft_in.begin() + frame_size, 0.0f);
+
+ // FFT.
+ fft(cache, fft_in.data(), frame_size, fft_out.data());
+
+ // Calculate modulus^2 of complex numbers.
+ for (int j = 0; j < n_fb; j++) {
+ fft_out[j] = (fft_out[2 * j + 0] * fft_out[2 * j + 0] + fft_out[2 * j + 1] * fft_out[2 * j + 1]);
+ }
+
+ // mel spectrogram.
+ for (int j = 0; j < mel.n_mel; j++) {
+ double sum = 0.0;
+ int k = 0;
+ for (k = 0; k < n_fb - 3; k += 4) {
+ sum +=
+ fft_out[k + 0] * cache.filters.data[j * n_fb + k + 0] +
+ fft_out[k + 1] * cache.filters.data[j * n_fb + k + 1] +
+ fft_out[k + 2] * cache.filters.data[j * n_fb + k + 2] +
+ fft_out[k + 3] * cache.filters.data[j * n_fb + k + 3];
+ }
+ for (; k < n_fb; k++) {
+ sum += fft_out[k] * cache.filters.data[j * n_fb + k];
+ }
+ mel.data[j * mel.n_len + i] = std::log(sum + eps);
+ }
+ }
+
+ // Otherwise fft_out are all zero.
+ const double empty_sum = std::log(eps);
+ for (; i < mel.n_len; i += n_threads) {
+ for (int j = 0; j < mel.n_mel; j++) {
+ mel.data[j * mel.n_len + i] = empty_sum;
+ }
+ }
+}
+
+void mtmd_audio_preprocessor_parakeet::initialize() {
+ cache.fill_sin_cos_table(hparams.audio_n_fft);
+
+ const size_t n_fft = hparams.audio_n_fft / 2 + 1;
+ GGML_ASSERT(hparams.mel_filters.size() == (size_t)hparams.n_mel_bins * n_fft);
+ cache.filters.n_mel = hparams.n_mel_bins;
+ cache.filters.n_fft = n_fft;
+ cache.filters.data = hparams.mel_filters;
+
+ GGML_ASSERT(hparams.window.size() == (size_t)hparams.audio_window_len);
+ GGML_ASSERT(hparams.window.size() <= (size_t) hparams.audio_n_fft);
+ cache.hann_window = hparams.window;
+}
+
+bool mtmd_audio_preprocessor_parakeet::preprocess(const float * samples,
+ size_t n_samples_in,
+ std::vector<mtmd_audio_mel> & output) {
+ if (n_samples_in == 0) {
+ return false;
+ }
+
+ filter_params params;
+ params.n_mel = hparams.n_mel_bins;
+ params.n_fft_bins = 1 + (hparams.audio_n_fft / 2);
+ params.hann_window_size = hparams.audio_window_len;
+ params.hop_length = hparams.audio_hop_len;
+ params.sample_rate = hparams.audio_sample_rate;
+
+ GGML_ASSERT(!cache.sin_vals.empty());
+ GGML_ASSERT(!cache.cos_vals.empty());
+ GGML_ASSERT(!cache.filters.data.empty());
+
+ const float * window_func = cache.hann_window.data();
+ const int window_size = params.hann_window_size;
+ const int frame_size = (params.n_fft_bins - 1) * 2;
+ const int frame_step = params.hop_length;
+
+ // Apply preemphasis filter (high-pass): x[i] = x[i] - 0.97 * x[i-1]
+ std::vector<float> samples_preprocessed(samples, samples + n_samples_in);
+ {
+ const float preemph = 0.97f;
+ for (int i = n_samples_in - 1; i > 0; i--) {
+ samples_preprocessed[i] = samples_preprocessed[i] - preemph * samples_preprocessed[i - 1];
+ }
+ }
+
+ // Parakeet uses centered constant padding
+ const size_t pad = (size_t)(frame_size / 2);
+ std::vector<float> samples_padded(n_samples_in + 2 * pad, 0.0f);
+ std::copy(samples_preprocessed.begin(), samples_preprocessed.end(), samples_padded.begin() + pad);
+
+ mtmd_audio_mel out_full;
+ out_full.n_mel = params.n_mel;
+ out_full.n_len = (samples_padded.size() - frame_size) / frame_step + 1;
+ out_full.n_len_org = out_full.n_len;
+ out_full.data.resize(out_full.n_mel * out_full.n_len);
+
+ const int n_threads = 4;
+ std::vector<std::thread> workers(n_threads - 1);
+ for (int iw = 0; iw < n_threads - 1; ++iw) {
+ workers[iw] = std::thread(
+ worker_thread, iw + 1,
+ window_func,
+ window_size,
+ std::cref(samples_padded),
+ samples_padded.size(),
+ frame_size,
+ frame_step,
+ n_threads,
+ params.n_fft_bins,
+ std::cref(cache),
+ std::ref(out_full)
+ );
+ }
+
+ worker_thread(0,
+ window_func,
+ window_size,
+ samples_padded,
+ samples_padded.size(),
+ frame_size,
+ frame_step,
+ n_threads,
+ params.n_fft_bins,
+ cache,
+ out_full);
+
+ for (int iw = 0; iw < n_threads - 1; ++iw) {
+ workers[iw].join();
+ }
+
+ // Per-feature normalization (only on valid frames)
+ {
+ const double eps = 1e-5;
+ int valid_frames = n_samples_in / frame_step;
+
+ for (int j = 0; j < out_full.n_mel; j++) {
+ double sum = 0.0;
+ double sq_diff_sum = 0.0;
+
+ // Calculate Mean ONLY on valid audio frames
+ for (int i = 0; i < valid_frames; i++) {
+ sum += (double)out_full.data[j * out_full.n_len + i];
+ }
+ double mean = sum / valid_frames;
+
+ // Calculate Variance ONLY on valid audio frames
+ for (int i = 0; i < valid_frames; i++) {
+ double diff = (double)out_full.data[j * out_full.n_len + i] - mean;
+ sq_diff_sum += diff * diff;
+ }
+
+ double std_dev = std::sqrt(sq_diff_sum / (valid_frames - 1.0));
+ double denominator = std_dev + eps;
+
+ // Apply to ALL frames (including the padded ones)
+ for (int i = 0; i < out_full.n_len; i++) {
+ out_full.data[j * out_full.n_len + i] = (float)((out_full.data[j * out_full.n_len + i] - mean) / denominator);
+ }
+ }
+ }
+
+ output.push_back(std::move(out_full));
+ return true;
+}
+
+
// mtmd_audio_preprocessor_gemma4ua
//
mtmd_audio_cache cache;
};
+struct mtmd_audio_preprocessor_parakeet : mtmd_audio_preprocessor {
+ mtmd_audio_preprocessor_parakeet(clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) { }
+ void initialize() override;
+ bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override;
+
+ private:
+ mtmd_audio_cache cache;
+
+ static void worker_thread(int ith, const float * window_func, int window_size,
+ const std::vector<float> & samples, int n_samples,
+ int frame_size, int frame_step, int n_threads,
+ int n_fft_bins,
+ const mtmd_audio_cache & cache, mtmd_audio_mel & mel);
+};
+
//
// streaming ISTFT - converts spectrogram frames back to audio one frame at a time
//
aud_end = "<audio|>";
audio_preproc = std::make_unique<mtmd_audio_preprocessor_gemma4a>(ctx_a);
} break;
+ case PROJECTOR_TYPE_PARAKEET:
+ {
+ audio_preproc = std::make_unique<mtmd_audio_preprocessor_parakeet>(ctx_a);
+ } break;
case PROJECTOR_TYPE_GEMMA4UA:
{
aud_beg = "<|audio>";
overview / pkg / ggml / sources / llama.cpp / commitdiff