Rename adept->persimmon
This commit is contained in:
Phillip Kravtsov
parent
db2181a47b
commit
3f3179996d
4 changed files with 76 additions and 160 deletions
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@ -19,7 +19,7 @@ def file_is_safetensors(path: Path) -> bool:
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return False
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return struct.unpack('<Q', first8)[0] < 16 * 1024 * 1024
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def handle_tokenizer(dir_model: Path):
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def get_tokenizer_info(dir_model: Path):
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tokenizer_path = dir_model / 'adept_vocab.model'
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print('gguf: get sentencepiece tokenizer from', tokenizer_path)
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tokenizer = SentencePieceProcessor(str(tokenizer_path))
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@ -56,21 +56,20 @@ def handle_tokenizer(dir_model: Path):
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def main(args_in: list[str] | None = None) -> None:
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parser = argparse.ArgumentParser(description="Convert an Adept model (e.g. Persimmon 8b) to a GGML compatible file")
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parser = argparse.ArgumentParser(description="Convert a Persimmon model from Adept (e.g. Persimmon 8b chat) to a GGML compatible file")
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parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model")
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parser.add_argument("--outtype", choices=["f32"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
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parser.add_argument("--outtype", choices=["f32"], help="currently only support fp32")
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parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
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parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
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parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.safetensors)")
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parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm")
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args = parser.parse_args(args_in)
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assert file_is_safetensors(args.model), 'Error: model file is not a SafeTensors file'
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model = lazy_load_safetensors_file(open(args.model, 'rb'), args.model)
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dir_model = args.model.parent
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with open(dir_model / 'config.json', 'r') as f:
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hparams = json.load(f)
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pprint(hparams)
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arch = gguf.MODEL_ARCH.ADEPT
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arch = gguf.MODEL_ARCH.PERSIMMON
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gguf_writer = gguf.GGUFWriter(args.outfile, gguf.MODEL_ARCH_NAMES[arch])
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block_count = hparams['num_layers']
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@ -90,7 +89,7 @@ def main(args_in: list[str] | None = None) -> None:
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gguf_writer.add_rope_freq_base(hparams['rotary_emb_base'])
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gguf_writer.add_layer_norm_eps(hparams['layernorm_epsilon'])
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if True:
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tokens, scores, toktypes = handle_tokenizer(dir_model)
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tokens, scores, toktypes = get_tokenizer_info(dir_model)
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gguf_writer.add_tokenizer_model('llama')
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gguf_writer.add_token_list(tokens)
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gguf_writer.add_token_scores(scores)
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@ -103,32 +102,13 @@ def main(args_in: list[str] | None = None) -> None:
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with safe_open(args.model, framework="pt") as f:
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for k in f.keys():
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tensors[k] = f.get_tensor(k)
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print(len(tensors.keys()))
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for name in tensors.keys():
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data = tensors[name]
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print(name)
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# we don't need these
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if name.endswith(".self_attention.rotary_emb.inv_freq"):
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continue
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old_dtype = data.dtype
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"""
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if 'layernorm.weight' in name or 'word_embeddings.weight' in name:
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data = data.to(torch.float32)
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else:
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if data.dtype != torch.float16 and data.dtype != torch.float32:
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data = data.to(torch.float32)
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"""
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data = data.to(torch.float32)
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# check for nans
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if torch.isnan(data).any():
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print("WARNING: tensor '" + name + "' contains NaNs")
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sys.exit()
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if torch.isinf(data).any():
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print("WARNING: tensor '" + name + "' contains infinities")
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sys.exit()
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data = data.squeeze().numpy()
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# TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
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data = data.to(torch.float32).squeeze().numpy()
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new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
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if new_name is None:
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print("Can not map tensor '" + name + "'")
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53
ggml.c
53
ggml.c
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@ -4304,49 +4304,34 @@ static void ggml_print_tensor(const struct ggml_tensor * tensor) {
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static void ggml_print_tensor_values(const struct ggml_tensor * tensor, int starts[], int dim, int nelts) {
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GGML_ASSERT(tensor->type == GGML_TYPE_F32);
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GGML_PRINT("printing values for %s[", tensor->name);
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GGML_PRINT("Printing values for tensor %s[", tensor->name);
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for (int i=0; i<tensor->n_dims; ++i) {
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if (i!=dim) {
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GGML_PRINT("%d", starts[i]);
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} else {
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if (starts[i] > 0) {
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GGML_ASSERT(starts[i] >= 0);
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if (i == dim) {
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if (starts[i] > 0) {
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GGML_PRINT("%d:%d", starts[i], starts[i]+nelts);
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} else {
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GGML_PRINT(":%d", starts[i]+nelts);
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}
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} else {
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GGML_PRINT("%d", starts[i]);
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}
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if (i<tensor->n_dims-1) {
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GGML_PRINT(",");
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}
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}
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GGML_PRINT("]\n");
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float *dataPtr = (float *) tensor->data;
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// Compute the offset into data for starts
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float *data_ptr = (float *) tensor->data;
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int offset = 0;
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for (int j = 0; j < tensor->n_dims; j++) {
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offset += (starts[j] * tensor->nb[j]) / sizeof(float); // Assuming nb[j] is in bytes, divide by sizeof(float) to get float offset.
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offset += (starts[j] * tensor->nb[j]) / ggml_type_size(GGML_TYPE_F32);
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}
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dataPtr += offset;
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data_ptr += offset;
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for (int i = 0; i < nelts; i++) {
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GGML_PRINT("%f ", *dataPtr);
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dataPtr += tensor->nb[dim] / sizeof(float); // Increment by strides for the given dimension.
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GGML_PRINT("%f ", *data_ptr);
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data_ptr += tensor->nb[dim] / ggml_type_size(GGML_TYPE_F32);
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}
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GGML_PRINT("\n");
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/*
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char * ptr = (char *)tensor->data;
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for (int j=0; j<tensor->n_dims;j++) {
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ptr += tensor->nb[j]*starts[j];
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}
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for (int i=0; i<nelts; i++) {
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GGML_PRINT("%f ", (*((float *) ptr)));
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ptr += tensor->nb[dim];
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}
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GGML_PRINT("\n");
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*/
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}
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int64_t ggml_nelements(const struct ggml_tensor * tensor) {
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@ -8883,14 +8868,14 @@ static void ggml_compute_forward_add_f32(
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}
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}
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}
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if (
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strncmp(src0->name, "printme", 7) == 0
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if ((strncmp(src0->name, "printme", 7) == 0
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||strncmp(src1->name, "printme", 7) == 0)
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&& params->ith == 0) {
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GGML_PRINT("\noutputs of add: %s + %s\n", src0->name, src1->name);
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ggml_print_tensor(src0);
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ggml_print_tensor(src1);
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ggml_print_tensor(dst);
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int starts[] = {0, 1, 0};
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int starts[] = {0, 0, 0};
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ggml_print_tensor_values(dst, starts, 0, 10);
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}
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}
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@ -10879,11 +10864,8 @@ static void ggml_compute_forward_norm_f32(
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&& params->ith == 0) {
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GGML_PRINT("\nlayernorm inputs for %s\n", src0->name);
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ggml_print_tensor(src0);
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int starts[] = {0, 1, 0};
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int starts[] = {0, 0, 0};
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ggml_print_tensor_values(src0, starts, 0, 10);
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for (int i=64; i<74; ++i) {
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GGML_PRINT("%f ", ggml_get_f32_1d(src0, i));
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}
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}
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const int ith = params->ith;
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@ -11313,15 +11295,14 @@ static void ggml_compute_forward_mul_mat(
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&& params->ith == 0) {
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GGML_PRINT("\nInputs to matmul: %s\n", src1->name);
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ggml_print_tensor(src1);
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/*
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size_t offset = 0;//(src1->ne[0] * src1->ne[1])
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for (int i=0; i < src1->ne[0] * src1->ne[1]; ++i) {
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if (i % src1->ne[0] == 0) {
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GGML_PRINT("\n");
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}
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GGML_PRINT(" %f ", ((float *)src1->data)[i + (src1->ne[0] * src1->ne[1])]);
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GGML_PRINT(" %f ", ((float *)src1->data)[i + offset]);
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}
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GGML_PRINT("\n");
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*/
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}
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GGML_TENSOR_BINARY_OP_LOCALS;
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@ -85,7 +85,7 @@ class MODEL_ARCH(IntEnum):
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GPTNEOX : int = auto()
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MPT : int = auto()
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STARCODER : int = auto()
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ADEPT : int = auto()
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PERSIMMON : int = auto()
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class MODEL_TENSOR(IntEnum):
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@ -119,7 +119,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.GPTNEOX: "gptneox",
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MODEL_ARCH.MPT: "mpt",
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MODEL_ARCH.STARCODER: "starcoder",
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MODEL_ARCH.ADEPT: "adept",
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MODEL_ARCH.PERSIMMON: "persimmon",
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}
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MODEL_TENSOR_NAMES: dict[MODEL_ARCH, dict[MODEL_TENSOR, str]] = {
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@ -189,7 +189,7 @@ MODEL_TENSOR_NAMES: dict[MODEL_ARCH, dict[MODEL_TENSOR, str]] = {
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MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
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MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
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},
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MODEL_ARCH.ADEPT: {
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MODEL_ARCH.PERSIMMON: {
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MODEL_TENSOR.TOKEN_EMBD: "token_embd",
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MODEL_TENSOR.OUTPUT: "output",
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MODEL_TENSOR.OUTPUT_NORM: "output_norm",
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@ -219,7 +219,7 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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MODEL_ARCH.ADEPT: [
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MODEL_ARCH.PERSIMMON: [
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MODEL_TENSOR.ROPE_FREQS,
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]
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}
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139
llama.cpp
139
llama.cpp
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@ -162,7 +162,7 @@ enum llm_arch {
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LLM_ARCH_GPTNEOX,
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LLM_ARCH_MPT,
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LLM_ARCH_STARCODER,
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LLM_ARCH_ADEPT,
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LLM_ARCH_PERSIMMON,
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LLM_ARCH_UNKNOWN,
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};
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@ -175,7 +175,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
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{ LLM_ARCH_MPT, "mpt" },
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{ LLM_ARCH_BAICHUAN, "baichuan" },
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{ LLM_ARCH_STARCODER, "starcoder" },
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{ LLM_ARCH_ADEPT, "adept" },
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{ LLM_ARCH_PERSIMMON, "persimmon" },
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};
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enum llm_kv {
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@ -378,7 +378,7 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
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},
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},
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{
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LLM_ARCH_ADEPT,
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LLM_ARCH_PERSIMMON,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd"},
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm"},
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@ -2323,7 +2323,7 @@ static void llm_load_tensors(
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}
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}
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} break;
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case LLM_ARCH_ADEPT:
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case LLM_ARCH_PERSIMMON:
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{
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model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
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model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, GGML_BACKEND_CPU);
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@ -3739,7 +3739,7 @@ static void log_tensor(
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LLAMA_LOG_INFO("\n");
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}
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static struct ggml_cgraph * llm_build_adept(
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static struct ggml_cgraph * llm_build_persimmon(
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llama_context & lctx,
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const llama_token * tokens,
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const float * embd,
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@ -3756,6 +3756,7 @@ static struct ggml_cgraph * llm_build_adept(
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const int64_t n_embd = hparams.n_embd;
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const int64_t n_layer = hparams.n_layer;
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//const int64_t n_layer = 1;
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const int64_t n_ctx = hparams.n_ctx;
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const int64_t n_head_kv = hparams.n_head_kv;
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const int64_t n_head = hparams.n_head;
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@ -3811,105 +3812,74 @@ static struct ggml_cgraph * llm_build_adept(
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// Input is (d_model, L)
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// Attention
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struct ggml_tensor * residual = ggml_dup(ctx0, inpL);
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ggml_set_name(residual, format((char*)"layer_inputs_%d", il).c_str());
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//ggml_format_name(inpL, "printme_layer_inputs_%d", il);
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{
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// input norming
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cur = ggml_norm(ctx0, inpL, hparams.f_norm_eps);
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cur = ggml_add(ctx0, ggml_mul(
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ctx0, cur, model.layers[il].attn_norm),
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model.layers[il].attn_norm_b);
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cur = ggml_mul(
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ctx0, cur, model.layers[il].attn_norm);
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//ggml_format_name(cur, "printme_normed_%d", il);
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cur = ggml_add(ctx0, cur, model.layers[il].attn_norm_b);
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}
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ggml_set_name(cur, "cur");
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{
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// QKV
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//log_tensor(cur);
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cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
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// 3 * d_model, L
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// or 2 * n_head_kv + n_embd_head, L
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// + bias
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ggml_format_name(cur, "qkv_preadd_%d", il);
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cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
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// Apply Q, K layernorm
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// Where is the Q/K/V? it's in order. Hopefully...
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// So q has offset 0.
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// And split into heads
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// -> (d_h, n_head, L)
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const size_t wsize = ggml_type_size(cur->type);
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// split qkv
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GGML_ASSERT(n_head_kv == n_head);
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//LLAMA_LOG_INFO("N: %d\n", N);
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ggml_set_name(cur, format("qkv_%d", il).c_str());
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//log_tensor(cur);
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// cur is (3 * d_head * n_head, N)
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struct ggml_tensor * tmpqkv = ggml_view_4d(
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ctx0, cur, n_embd_head, 3, n_head, N,
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/* nb1 = */ wsize * n_embd_head,
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/* nb2 = */ wsize * n_embd_head * 3,
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/* nb3 = */ wsize * n_embd_head * 3 * n_head,
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/* offset = */ 0
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);
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struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, N);
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// get it to (d_h, n_head, L, 3)
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struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
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ggml_format_name(tmpqkv_perm, "tmpqkv_perm_%d", il);
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//log_tensor(tmpqkv_perm);
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struct ggml_tensor * tmpq = ggml_cont(
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ctx0,
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ggml_view_3d(
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struct ggml_tensor * tmpq = ggml_view_3d(
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ctx0, tmpqkv_perm, n_embd_head, n_head, N,
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/* nb1 = */ sizeof(float) * n_embd_head,
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/* nb2 = */ sizeof(float) * n_embd_head * n_head,
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/* nb1 = */ ggml_element_size(tmpqkv_perm) * n_embd_head,
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/* nb2 = */ ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
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/* offset = */ 0
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)
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);
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struct ggml_tensor * tmpk = ggml_cont(
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ctx0,
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ggml_view_3d(
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);
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struct ggml_tensor * tmpk = ggml_view_3d(
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ctx0, tmpqkv_perm, n_embd_head, n_head, N,
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/* nb1 = */ sizeof(float) * n_embd_head,
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/* nb2 = */ sizeof(float) * n_embd_head * n_head,
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/* offset = */ sizeof(float) * n_embd_head * n_head * N
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)
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);
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struct ggml_tensor * tmpv = ggml_cont(
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ctx0,
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ggml_view_3d(
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/* nb1 = */ ggml_element_size(tmpqkv_perm) * n_embd_head,
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/* nb2 = */ ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
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/* offset = */ ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * N
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);
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struct ggml_tensor * tmpv = ggml_view_3d(
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ctx0, tmpqkv_perm, n_embd_head, n_head, N,
|
||||
/* nb1 = */ sizeof(float) * n_embd_head,
|
||||
/* nb2 = */ sizeof(float) * n_embd_head * n_head,
|
||||
/* offset = */ sizeof(float) * n_embd_head * n_head * N * 2
|
||||
)
|
||||
);
|
||||
// Q / K layernorm
|
||||
ggml_set_name(tmpq, format("tmpq_%d", il).c_str());
|
||||
/* nb1 = */ ggml_element_size(tmpqkv_perm) * n_embd_head,
|
||||
/* nb2 = */ ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
|
||||
/* offset = */ ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * N * 2
|
||||
);
|
||||
//ggml_format_name(tmpq, "printme_tmpq_%d", il);
|
||||
tmpq = ggml_norm(ctx0, tmpq, hparams.f_norm_eps);
|
||||
tmpq = ggml_mul(ctx0, tmpq, model.layers[il].attn_q_norm);
|
||||
tmpq = ggml_add(ctx0, tmpq, model.layers[il].attn_q_norm_b);
|
||||
ggml_set_name(tmpq, format("tmpq_%d", il).c_str());
|
||||
//log_tensor(tmpq);
|
||||
|
||||
//ggml_format_name(tmpq, "printme_tmpk_%d", il);
|
||||
tmpk = ggml_norm(ctx0, tmpk, hparams.f_norm_eps);
|
||||
tmpk = ggml_mul(ctx0, tmpk, model.layers[il].attn_k_norm);
|
||||
ggml_set_name(tmpk, format("preadd_%d", il).c_str());
|
||||
tmpk = ggml_add(ctx0, tmpk, model.layers[il].attn_k_norm_b);
|
||||
ggml_set_name(tmpk, format("tmpk_%d", il).c_str());
|
||||
//log_tensor(tmpk);
|
||||
|
||||
|
||||
const size_t n_rot = n_embd_head / 2;
|
||||
|
||||
struct ggml_tensor * qrot = ggml_cont(ctx0, ggml_view_3d(
|
||||
ctx0, tmpq, n_rot, n_head, N,
|
||||
/* nb1 = */ wsize * n_embd_head,
|
||||
/* nb2 = */ wsize * n_embd_head * n_head,
|
||||
/* nb1 = */ ggml_element_size(tmpq) * n_embd_head,
|
||||
/* nb2 = */ ggml_element_size(tmpq) * n_embd_head * n_head,
|
||||
/* offset = */ 0
|
||||
));
|
||||
// get the second half of tmpq, e.g tmpq[n_rot:, :, :]
|
||||
struct ggml_tensor * qpass = ggml_cont(ctx0, ggml_view_3d(
|
||||
ctx0, tmpq, n_rot, n_head, N,
|
||||
/* nb1 = */ wsize * n_embd_head,
|
||||
/* nb2 = */ wsize * n_embd_head * n_head,
|
||||
/* offset = */ wsize * n_rot
|
||||
/* nb1 = */ ggml_element_size(tmpq) * n_embd_head,
|
||||
/* nb2 = */ ggml_element_size(tmpq) * n_embd_head * n_head,
|
||||
/* offset = */ ggml_element_size(tmpq) * n_rot
|
||||
));
|
||||
ggml_set_name(qrot, format("qrot_%d", il).c_str());
|
||||
ggml_set_name(qpass, format("qpass_%d", il).c_str());
|
||||
|
|
@ -3918,20 +3888,18 @@ static struct ggml_cgraph * llm_build_adept(
|
|||
|
||||
struct ggml_tensor * krot = ggml_cont(ctx0, ggml_view_3d(
|
||||
ctx0, tmpk, n_rot, n_head, N,
|
||||
/* nb1 = */ wsize * n_embd_head,
|
||||
/* nb2 = */ wsize * n_embd_head * n_head,
|
||||
/* nb1 = */ ggml_element_size(tmpk) * n_embd_head,
|
||||
/* nb2 = */ ggml_element_size(tmpk) * n_embd_head * n_head,
|
||||
/* offset = */ 0
|
||||
));
|
||||
struct ggml_tensor * kpass = ggml_cont(ctx0, ggml_view_3d(
|
||||
ctx0, tmpk, n_rot, n_head, N,
|
||||
/* nb1 = */ wsize * n_embd_head,
|
||||
/* nb2 = */ wsize * n_embd_head * n_head,
|
||||
/* offset = */ wsize * n_rot
|
||||
/* nb1 = */ ggml_element_size(tmpk) * n_embd_head,
|
||||
/* nb2 = */ ggml_element_size(tmpk) * n_embd_head * n_head,
|
||||
/* offset = */ ggml_element_size(tmpk) * n_rot
|
||||
));
|
||||
ggml_set_name(krot, format("krot_%d", il).c_str());
|
||||
ggml_set_name(kpass, format("kpass_%d", il).c_str());
|
||||
//log_tensor(krot);
|
||||
//log_tensor(kpass);
|
||||
|
||||
struct ggml_tensor * qrotated = ggml_cont(ctx0, ggml_permute(ctx0,
|
||||
ggml_rope_custom_inplace(
|
||||
|
|
@ -3939,17 +3907,15 @@ static struct ggml_cgraph * llm_build_adept(
|
|||
),
|
||||
2, 1, 0, 3
|
||||
));
|
||||
ggml_set_name(qrotated, format("qrotated_%d", il).c_str());
|
||||
//log_tensor(qrotated);
|
||||
qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
|
||||
|
||||
//ggml_format_name(krot, "printme_krot_%d", il);
|
||||
struct ggml_tensor * krotated = ggml_cont(ctx0, ggml_permute(ctx0,
|
||||
ggml_rope_custom_inplace(
|
||||
ctx0, krot, n_past, n_rot, 2, 0, freq_base, freq_scale
|
||||
),
|
||||
2, 1, 0, 3
|
||||
));
|
||||
ggml_set_name(krotated, format("krotated_%d", il).c_str());
|
||||
//log_tensor(krotated);
|
||||
kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
|
||||
|
||||
struct ggml_tensor * Qcur = ggml_cont(ctx0,
|
||||
|
|
@ -3962,16 +3928,12 @@ static struct ggml_cgraph * llm_build_adept(
|
|||
);
|
||||
ggml_set_name(Qcur, format("Qcur_%d", il).c_str());
|
||||
ggml_set_name(Kcur, format("Kcur_%d", il).c_str());
|
||||
//log_tensor(Qcur);
|
||||
//////log_tensor(Kcur);
|
||||
//log_tensor(kv_self.k);
|
||||
{
|
||||
// View v as (N, n_embd)
|
||||
struct ggml_tensor * Vcur = ggml_transpose(
|
||||
ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd, N)
|
||||
);
|
||||
ggml_set_name(Vcur, "Vcur");
|
||||
|
||||
// Select k from kv cache as 1d view (N * n_embd)
|
||||
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd,
|
||||
(ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)
|
||||
|
|
@ -3997,7 +3959,6 @@ static struct ggml_cgraph * llm_build_adept(
|
|||
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il));
|
||||
|
||||
ggml_set_name(K, "K");
|
||||
//log_tensor(K);
|
||||
|
||||
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
||||
ggml_set_name(KQ, "KQ");
|
||||
|
|
@ -4009,7 +3970,7 @@ static struct ggml_cgraph * llm_build_adept(
|
|||
ggml_set_name(KQ_masked, "KQ_mask");
|
||||
|
||||
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
|
||||
ggml_set_name(KQ_soft_max, format("KQ_soft_max_%d", il).c_str());
|
||||
//ggml_set_name(KQ_soft_max, format("printme_KQ_soft_max_%d", il).c_str());
|
||||
|
||||
struct ggml_tensor * V =
|
||||
ggml_view_3d(ctx0, kv_self.v,
|
||||
|
|
@ -4031,7 +3992,6 @@ static struct ggml_cgraph * llm_build_adept(
|
|||
cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
|
||||
cur = ggml_add(ctx0, cur, model.layers[il].bo);
|
||||
ggml_set_name(cur, "result_wo");
|
||||
//log_tensor(cur);
|
||||
}
|
||||
cur = ggml_add(ctx0, residual, cur);
|
||||
struct ggml_tensor * residual2 = ggml_dup(ctx0, cur);
|
||||
|
|
@ -4044,17 +4004,12 @@ static struct ggml_cgraph * llm_build_adept(
|
|||
model.layers[il].ffn_norm_b
|
||||
);
|
||||
}
|
||||
// FFN
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
|
||||
ggml_format_name(cur, "pre_act_%d", il);
|
||||
cur = ggml_add(ctx0, cur, model.layers[il].b3);
|
||||
// //log_tensor(cur);
|
||||
// Correct through here.
|
||||
// Squared ReLU
|
||||
cur = ggml_relu(ctx0, cur);
|
||||
cur = ggml_sqr(ctx0, cur);
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
|
||||
ggml_format_name(cur, "post_ffn_down_%d", il);
|
||||
//ggml_format_name(cur, "printme_ffn_down_%d", il);
|
||||
struct ggml_tensor * ffn_out = ggml_add(ctx0,
|
||||
cur,
|
||||
model.layers[il].b2);
|
||||
|
|
@ -4105,9 +4060,9 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm_build_starcoder(lctx, tokens, embd, n_tokens, n_past);
|
||||
} break;
|
||||
case LLM_ARCH_ADEPT:
|
||||
case LLM_ARCH_PERSIMMON:
|
||||
{
|
||||
result = llm_build_adept(lctx, tokens, embd, n_tokens, n_past);
|
||||
result = llm_build_persimmon(lctx, tokens, embd, n_tokens, n_past);
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue