convert : update phi-2 to latest HF repo (#4903)
* convert : update phi-2 to latest HF repo ggml-ci * py : try to fix flake stuff
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@ -23,6 +23,15 @@ if 'NO_LOCAL_GGUF' not in os.environ:
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import gguf
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# check for any of the given keys in the dictionary and return the value of the first key found
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def get_key_opts(d, keys):
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for k in keys:
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if k in d:
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return d[k]
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print(f"Could not find any of {keys}")
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sys.exit()
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###### MODEL DEFINITIONS ######
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class SentencePieceTokenTypes(IntEnum):
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@ -257,10 +266,11 @@ class Model:
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toktypes.append(gguf.TokenType.USER_DEFINED)
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elif reverse_vocab[i] in added_vocab:
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tokens.append(reverse_vocab[i])
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if tokenizer.added_tokens_decoder[i].special:
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toktypes.append(gguf.TokenType.CONTROL)
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else:
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toktypes.append(gguf.TokenType.USER_DEFINED)
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if hasattr(tokenizer, "added_tokens_decoder"):
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if tokenizer.added_tokens_decoder[i].special:
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toktypes.append(gguf.TokenType.CONTROL)
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else:
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toktypes.append(gguf.TokenType.USER_DEFINED)
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else:
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tokens.append(reverse_vocab[i])
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toktypes.append(gguf.TokenType.NORMAL)
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@ -1068,17 +1078,22 @@ class GPT2Model(Model):
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class Phi2Model(Model):
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def set_gguf_parameters(self):
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block_count = self.hparams["n_layer"]
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block_count = get_key_opts(self.hparams, ["num_hidden_layers", "n_layer"])
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rot_pct = get_key_opts(self.hparams, ["partial_rotary_factor"])
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n_embd = get_key_opts(self.hparams, ["hidden_size", "n_embd"])
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n_head = get_key_opts(self.hparams, ["num_attention_heads", "n_head"])
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self.gguf_writer.add_name("Phi2")
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self.gguf_writer.add_context_length(self.hparams["n_positions"])
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self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
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self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
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self.gguf_writer.add_context_length(get_key_opts(self.hparams, ["n_positions", "max_position_embeddings"]))
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self.gguf_writer.add_embedding_length(n_embd)
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self.gguf_writer.add_feed_forward_length(4 * n_embd)
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self.gguf_writer.add_block_count(block_count)
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self.gguf_writer.add_head_count(self.hparams["n_head"])
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self.gguf_writer.add_head_count_kv(self.hparams["n_head"])
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self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
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self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"])
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self.gguf_writer.add_head_count(n_head)
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self.gguf_writer.add_head_count_kv(n_head)
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self.gguf_writer.add_layer_norm_eps(get_key_opts(self.hparams, ["layer_norm_epsilon", "layer_norm_eps"]))
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self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
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self.gguf_writer.add_file_type(self.ftype)
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self.gguf_writer.add_add_bos_token(False)
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@ -389,6 +389,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_QKV,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_DOWN,
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@ -191,6 +191,7 @@ class TensorNameMap:
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"transformer.h.{bid}.mlp.w1", # qwen
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"h.{bid}.mlp.c_fc", # gpt2
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"transformer.h.{bid}.mlp.fc1", # phi2
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"model.layers.{bid}.mlp.fc1", # phi2
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"model.layers.layers.{bid}.mlp.up_proj", # plamo
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),
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@ -232,6 +233,7 @@ class TensorNameMap:
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"model.layers.{bid}.mlp.dense_4h_to_h", # persimmon
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"h.{bid}.mlp.c_proj", # gpt2
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"transformer.h.{bid}.mlp.fc2", # phi2
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"model.layers.{bid}.mlp.fc2", # phi2
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"model.layers.layers.{bid}.mlp.down_proj", # plamo
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),
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42
llama.cpp
42
llama.cpp
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@ -574,6 +574,9 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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@ -3676,8 +3679,19 @@ static bool llm_load_tensors(
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layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
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layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
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layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
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layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
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layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, false);
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layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false);
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if (layer.wqkv == nullptr) {
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
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layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
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layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
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layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
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layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
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layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
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}
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
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layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
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@ -5637,15 +5651,25 @@ struct llm_build_context {
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// self-attention
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{
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cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
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cb(cur, "wqkv", il);
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struct ggml_tensor * Qcur = nullptr;
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struct ggml_tensor * Kcur = nullptr;
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struct ggml_tensor * Vcur = nullptr;
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cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
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cb(cur, "bqkv", il);
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if (model.layers[il].wqkv) {
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cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
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cb(cur, "wqkv", il);
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struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
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struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
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struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
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cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
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cb(cur, "bqkv", il);
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Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
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Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
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Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
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} else {
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Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
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Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
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Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
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}
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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