Merge branch 'master' into concedo_experimental
# Conflicts: # .github/workflows/build.yml # ggml-opencl.cpp
This commit is contained in:
Concedo
commit
b5cd935cdb
5 changed files with 138 additions and 93 deletions
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@ -44,9 +44,12 @@ let package = Package(
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cSettings: [
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.unsafeFlags(["-Wno-shorten-64-to-32"]),
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.define("GGML_USE_K_QUANTS"),
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.define("GGML_USE_ACCELERATE"),
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.define("ACCELERATE_NEW_LAPACK"),
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.define("ACCELERATE_LAPACK_ILP64")
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.define("GGML_USE_ACCELERATE")
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// NOTE: NEW_LAPACK will required iOS version 16.4+
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// We should consider add this in the future when we drop support for iOS 14
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// (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
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// .define("ACCELERATE_NEW_LAPACK"),
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// .define("ACCELERATE_LAPACK_ILP64")
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] + additionalSettings,
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linkerSettings: [
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.linkedFramework("Accelerate")
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@ -361,7 +361,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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invalid_param = true;
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break;
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}
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params.lora_adapter.push_back({argv[i], 1.0f});
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params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f));
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params.use_mmap = false;
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} else if (arg == "--lora-scaled") {
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if (++i >= argc) {
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@ -373,7 +373,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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invalid_param = true;
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break;
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}
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params.lora_adapter.push_back({lora_adapter, std::stof(argv[i])});
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params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i])));
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params.use_mmap = false;
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} else if (arg == "--lora-base") {
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if (++i >= argc) {
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@ -616,6 +616,9 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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process_escapes(params.prompt);
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process_escapes(params.input_prefix);
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process_escapes(params.input_suffix);
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for (auto & antiprompt : params.antiprompt) {
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process_escapes(antiprompt);
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}
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}
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return true;
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@ -4,6 +4,7 @@
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from __future__ import annotations
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import argparse
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import contextlib
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import json
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import os
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import struct
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@ -20,10 +21,10 @@ if 'NO_LOCAL_GGUF' not in os.environ:
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import gguf
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def count_model_parts(dir_model: Path) -> int:
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def count_model_parts(dir_model: Path, prefix: str) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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if filename.startswith("pytorch_model-"):
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if filename.startswith(prefix):
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num_parts += 1
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if num_parts > 0:
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@ -77,20 +78,26 @@ print("gguf: loading model "+dir_model.name)
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with open(dir_model / "config.json", "r", encoding="utf-8") as f:
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hparams = json.load(f)
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if hparams["architectures"][0] != "RWForCausalLM":
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if hparams["architectures"][0] != "FalconForCausalLM":
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print("Model architecture not supported: " + hparams["architectures"][0])
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sys.exit(1)
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# get number of model parts
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num_parts = count_model_parts(dir_model)
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num_parts = count_model_parts(dir_model, "model-00")
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if num_parts:
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is_safetensors = True
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from safetensors import safe_open
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else:
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is_safetensors = False
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num_parts = count_model_parts(dir_model, "pytorch_model-")
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ARCH=gguf.MODEL_ARCH.FALCON
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gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
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print("gguf: get model metadata")
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block_count = hparams["n_layer"]
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block_count = hparams["num_hidden_layers"]
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gguf_writer.add_name("Falcon")
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gguf_writer.add_context_length(2048) # not in config.json
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@ -98,9 +105,9 @@ gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform
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gguf_writer.add_embedding_length(hparams["hidden_size"])
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gguf_writer.add_feed_forward_length(4 * hparams["hidden_size"])
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gguf_writer.add_block_count(block_count)
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gguf_writer.add_head_count(hparams["n_head"])
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if "n_head_kv" in hparams:
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gguf_writer.add_head_count_kv(hparams["n_head_kv"])
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gguf_writer.add_head_count(hparams["num_attention_heads"])
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if "num_kv_heads" in hparams:
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gguf_writer.add_head_count_kv(hparams["num_kv_heads"])
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else:
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gguf_writer.add_head_count_kv(1)
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gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
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@ -146,8 +153,8 @@ special_vocab.add_to_gguf(gguf_writer)
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tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
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# params for qkv transform
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n_head = hparams["n_head"]
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n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1
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n_head = hparams["num_attention_heads"]
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n_head_kv = hparams["num_kv_heads"] if "num_kv_heads" in hparams else 1
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head_dim = hparams["hidden_size"] // n_head
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@ -156,6 +163,10 @@ print("gguf: get tensor metadata")
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if num_parts == 0:
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part_names = iter(("pytorch_model.bin",))
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elif is_safetensors:
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part_names = (
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f"model-{n:05}-of-{num_parts:05}.safetensors" for n in range(1, num_parts + 1)
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)
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else:
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part_names = (
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f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
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@ -165,60 +176,64 @@ for part_name in part_names:
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if args.vocab_only:
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break
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print("gguf: loading model part '" + part_name + "'")
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model_part = torch.load(dir_model / part_name, map_location="cpu")
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if is_safetensors:
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ctx = safe_open(dir_model / part_name, framework="pt", device="cpu")
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else:
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ctx = contextlib.nullcontext(torch.load(dir_model / part_name, map_location="cpu"))
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for name in model_part.keys():
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data = model_part[name]
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with ctx as model_part:
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for name in model_part.keys():
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data = model_part.get_tensor(name) if is_safetensors else model_part[name]
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old_dtype = data.dtype
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old_dtype = data.dtype
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# convert any unsupported data types to float32
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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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# convert any unsupported data types to float32
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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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# QKV tensor transform
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# The original query_key_value tensor contains n_head_kv "kv groups",
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# each consisting of n_head/n_head_kv query weights followed by one key
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# and one value weight (shared by all query heads in the kv group).
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# This layout makes it a big pain to work with in GGML.
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# So we rearrange them here,, so that we have n_head query weights
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# followed by n_head_kv key weights followed by n_head_kv value weights,
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# in contiguous fashion.
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# ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
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# QKV tensor transform
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# The original query_key_value tensor contains n_head_kv "kv groups",
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# each consisting of n_head/n_head_kv query weights followed by one key
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# and one value weight (shared by all query heads in the kv group).
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# This layout makes it a big pain to work with in GGML.
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# So we rearrange them here,, so that we have n_head query weights
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# followed by n_head_kv key weights followed by n_head_kv value weights,
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# in contiguous fashion.
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# ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
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if "query_key_value" in name:
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qkv = data.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
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q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head)
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k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
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v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
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data = torch.cat((q,k,v)).reshape_as(data)
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if "query_key_value" in name:
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qkv = data.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
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q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head)
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k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
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v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
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data = torch.cat((q,k,v)).reshape_as(data)
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data = data.squeeze().numpy()
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data = data.squeeze().numpy()
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# map tensor names
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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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sys.exit()
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# map tensor names
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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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sys.exit()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if ftype == 0 and data_dtype == np.float16:
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data = data.astype(np.float32)
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# if f32 desired, convert any float16 to float32
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if ftype == 0 and data_dtype == np.float16:
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data = data.astype(np.float32)
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# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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data = data.astype(np.float32)
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# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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data = data.astype(np.float32)
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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gguf_writer.add_tensor(new_name, data)
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gguf_writer.add_tensor(new_name, data)
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print("gguf: write header")
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@ -504,9 +504,11 @@ struct llama_server_context
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});
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}
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bool tg = true;
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while (n_past < embd.size())
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{
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int n_eval = (int)embd.size() - n_past;
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tg = n_eval == 1;
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if (n_eval > params.n_batch)
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{
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n_eval = params.n_batch;
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@ -633,7 +635,9 @@ struct llama_server_context
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last_n_tokens.erase(last_n_tokens.begin());
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last_n_tokens.push_back(result.tok);
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num_tokens_predicted++;
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if (tg) {
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num_tokens_predicted++;
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}
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}
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// add it to the context
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@ -1011,7 +1015,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
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invalid_param = true;
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break;
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}
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params.lora_adapter.push_back({argv[i], 1.0f});
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params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f));
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params.use_mmap = false;
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}
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else if (arg == "--lora-scaled")
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@ -1027,7 +1031,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
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invalid_param = true;
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break;
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}
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params.lora_adapter.push_back({lora_adapter, std::stof(argv[i])});
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params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i])));
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params.use_mmap = false;
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}
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else if (arg == "--lora-base")
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@ -1124,8 +1128,6 @@ static json format_timings(llama_server_context &llama)
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{
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const auto timings = llama_get_timings(llama.ctx);
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assert(timings.n_eval == ptrdiff_t(llama.num_tokens_predicted));
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return json{
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{"prompt_n", timings.n_p_eval},
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{"prompt_ms", timings.t_p_eval_ms},
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|
|
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@ -203,14 +203,14 @@ inline void get_scale_min_k4(int j, const __global uint8_t *q, uint8_t *d, uint8
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__kernel void dequantize_block_q2_K(__global const struct block_q2_K *x, __global float *yy)
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{
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const int i = get_group_id(0);
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const int i = get_group_id(0) + get_global_offset(0);
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const int tid = get_local_id(0);
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const int n = tid / 32;
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const int l = tid - 32 * n;
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const int is = 8 * n + l / 16;
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const uint8_t q = x[i].qs[32 * n + l];
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__global float *y = yy + i * QK_K + 128 * n;
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__global float *y = yy + get_group_id(0) * QK_K + 128 * n;
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const float dall = vload_half(0, &x[i].d);
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const float dmin = vload_half(0, &x[i].dmin);
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@ -224,7 +224,7 @@ __kernel void dequantize_block_q2_K(__global const struct block_q2_K *x, __globa
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__kernel void dequantize_block_q3_K(__global const struct block_q3_K *x, __global float *yy)
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{
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int r = get_local_id(0) / 4;
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int i = get_group_id(0);
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int i = get_group_id(0) + get_global_offset(0);
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int tid = r / 2;
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int is0 = r % 2;
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int l0 = 16 * is0 + 4 * (get_local_id(0) % 4);
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@ -242,7 +242,7 @@ __kernel void dequantize_block_q3_K(__global const struct block_q3_K *x, __globa
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float d_all = vload_half(0, &x[i].d);
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float dl = d_all * (us - 32);
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__global float *y = yy + i * QK_K + 128 * n + 32 * j;
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__global float *y = yy + get_group_id(0) * QK_K + 128 * n + 32 * j;
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const __global uint8_t *q = x[i].qs + 32 * n;
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const __global uint8_t *hm = x[i].hmask;
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@ -252,14 +252,14 @@ __kernel void dequantize_block_q3_K(__global const struct block_q3_K *x, __globa
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__kernel void dequantize_block_q4_K(__global const struct block_q4_K *x, __global float *yy)
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{
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||||
const int i = get_group_id(0);
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const int i = get_group_id(0) + get_global_offset(0);
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const int tid = get_local_id(0);
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const int il = tid / 8;
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const int ir = tid % 8;
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const int is = 2 * il;
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const int n = 4;
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__global float *y = yy + i * QK_K + 64 * il + n * ir;
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__global float *y = yy + get_group_id(0) * QK_K + 64 * il + n * ir;
|
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const float dall = vload_half(0, &x[i].d);
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const float dmin = vload_half(0, &x[i].dmin);
|
||||
|
|
@ -282,13 +282,13 @@ __kernel void dequantize_block_q4_K(__global const struct block_q4_K *x, __globa
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__kernel void dequantize_block_q5_K(__global const struct block_q5_K *x, __global float *yy)
|
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{
|
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const int i = get_group_id(0);
|
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const int i = get_group_id(0) + get_global_offset(0);
|
||||
const int tid = get_local_id(0);
|
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const int il = tid / 16;
|
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const int ir = tid % 16;
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const int is = 2 * il;
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|
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__global float *y = yy + i * QK_K + 64 * il + 2 * ir;
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__global float *y = yy + get_group_id(0) * QK_K + 64 * il + 2 * ir;
|
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|
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const float dall = vload_half(0, &x[i].d);
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const float dmin = vload_half(0, &x[i].dmin);
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|
|
@ -314,13 +314,13 @@ __kernel void dequantize_block_q5_K(__global const struct block_q5_K *x, __globa
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|||
|
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__kernel void dequantize_block_q6_K(__global const struct block_q6_K *x, __global float *yy)
|
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{
|
||||
const int i = get_group_id(0);
|
||||
const int i = get_group_id(0) + get_global_offset(0);
|
||||
const int tid = get_local_id(0);
|
||||
const int ip = tid / 32;
|
||||
const int il = tid - 32 * ip;
|
||||
const int is = 8 * ip + il / 16;
|
||||
|
||||
__global float *y = yy + i * QK_K + 128 * ip + il;
|
||||
__global float *y = yy + get_group_id(0) * QK_K + 128 * ip + il;
|
||||
|
||||
const float d = vload_half(0, &x[i].d);
|
||||
|
||||
|
|
@ -731,7 +731,7 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __global float* y) {
|
|||
const uint qk = QUANT_K;
|
||||
const uint qr = QUANT_R;
|
||||
|
||||
const int ib = i/qk; // block index
|
||||
const int ib = i/qk + get_global_offset(0); // block index
|
||||
const int iqs = (i%qk)/qr; // quant index
|
||||
const int iybs = i - i%qk; // y block start index
|
||||
const int y_offset = qr == 1 ? 1 : qk/2;
|
||||
|
|
@ -1357,30 +1357,42 @@ static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t o
|
|||
const enum ggml_type type = src->type;
|
||||
const size_t ts = ggml_type_size(type);
|
||||
const size_t bs = ggml_blck_size(type);
|
||||
const uint64_t row_size = ts*ne0/bs;
|
||||
|
||||
const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3);
|
||||
if (nb0 == ts && nb1 == ts*ne0/bs) {
|
||||
err = clEnqueueWriteBuffer(queue, dst, CL_FALSE, offset, ne1*nb1, x, 0, NULL, ev);
|
||||
return err;
|
||||
const char * x = (const char *) src->data + i2*nb2 + i3*nb3;
|
||||
if (nb0 == ts && nb1 == row_size) {
|
||||
return clEnqueueWriteBuffer(queue, dst, CL_FALSE, offset, ne1*row_size, x, 0, NULL, ev);
|
||||
}
|
||||
if (nb0 == ts) {
|
||||
const size_t buffer_origin[3] = { offset, 0, 0 };
|
||||
const size_t host_origin[3] = { 0, 0, 0 };
|
||||
const size_t region[3] = { ts*ne0/bs, ne1, 1 };
|
||||
err = clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, ts*ne0/bs, 0, nb1, 0, x, 0, NULL, ev);
|
||||
return err;
|
||||
const size_t region[3] = { row_size, ne1, 1 };
|
||||
return clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, row_size, 0, nb1, 0, x, 0, NULL, ev);
|
||||
}
|
||||
std::vector<cl_event> events;
|
||||
if (ev && ne1>1) events.reserve(ne1-1);
|
||||
for (uint64_t i1 = 0; i1 < ne1; i1++) {
|
||||
// pretend the row is a matrix with cols=1
|
||||
const size_t buffer_origin[3] = { offset, i1, 0 };
|
||||
const size_t buffer_origin[3] = { offset + i1*row_size, 0, 0 };
|
||||
const size_t host_origin[3] = { 0, 0, 0 };
|
||||
const size_t region[3] = { ts/bs, ne0, 1 };
|
||||
err = clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, 0, 0, nb0, 0, ((const char *)x) + i1*nb0, 0, NULL, ev);
|
||||
const size_t region[3] = { ts, ne0/bs, 1 };
|
||||
// if an event is requested, make the last write wait for all previous writes to complete
|
||||
if (ev && i1) {
|
||||
events.push_back(*ev);
|
||||
}
|
||||
cl_uint nevents = i1 == ne1-1 ? events.size() : 0U;
|
||||
err = clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, ts, 0, nb0, 0, x + i1*nb1, nevents, nevents ? events.data() : nullptr, ev);
|
||||
if (err != CL_SUCCESS) {
|
||||
break;
|
||||
for (auto event : events) {
|
||||
clReleaseEvent(event);
|
||||
}
|
||||
return err;
|
||||
}
|
||||
}
|
||||
return err;
|
||||
for (auto event : events) {
|
||||
CL_CHECK(clReleaseEvent(event));
|
||||
}
|
||||
return CL_SUCCESS;
|
||||
}
|
||||
|
||||
static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
||||
|
|
@ -1511,6 +1523,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
|
||||
|
||||
size_t x_offset = 0;
|
||||
int64_t pi02 = -1;
|
||||
int64_t pi03 = -1;
|
||||
|
||||
|
|
@ -1521,7 +1534,9 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
int64_t i02 = i12 / r2;
|
||||
|
||||
// copy data to device
|
||||
if (src0->backend != GGML_BACKEND_GPU && (i02 != pi02 || i03 != pi03)) {
|
||||
if (src0->backend == GGML_BACKEND_GPU) {
|
||||
x_offset = (i03 * ne02 + i02) * x_ne;
|
||||
} else if (i02 != pi02 || i03 != pi03) {
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
|
||||
pi02 = i02;
|
||||
pi03 = i03;
|
||||
|
|
@ -1536,7 +1551,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
(CLBlastTranspose)clblast::Transpose::kYes, (CLBlastTranspose)clblast::Transpose::kNo,
|
||||
ne01, ne11, ne10,
|
||||
alpha,
|
||||
d_X, 0, ne00,
|
||||
d_X, x_offset, ne00,
|
||||
d_Y, 0, ne10,
|
||||
beta,
|
||||
d_D, 0, ne01,
|
||||
|
|
@ -1605,6 +1620,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
bool src1_cont_rows = nb10 == sizeof(float);
|
||||
bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
|
||||
|
||||
size_t x_offset = 0;
|
||||
int64_t pi02 = -1;
|
||||
int64_t pi03 = -1;
|
||||
|
||||
|
|
@ -1615,7 +1631,9 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
int64_t i02 = i12 / r2;
|
||||
|
||||
// copy src0 to device
|
||||
if (src0->backend != GGML_BACKEND_GPU && (i02 != pi02 || i03 != pi03)) {
|
||||
if (src0->backend == GGML_BACKEND_GPU) {
|
||||
x_offset = (i03 * ne02 + i02) * x_ne;
|
||||
} else if (i02 != pi02 || i03 != pi03) {
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
|
||||
pi02 = i02;
|
||||
pi03 = i03;
|
||||
|
|
@ -1655,7 +1673,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
(CLBlastTranspose)clblast::Transpose::kYes, (CLBlastTranspose)clblast::Transpose::kNo,
|
||||
ne01, ne11, ne10,
|
||||
alpha,
|
||||
d_X, 0, ne00,
|
||||
d_X, x_offset, ne00,
|
||||
d_Y, 0, ne10,
|
||||
beta,
|
||||
d_D, 0, ne01,
|
||||
|
|
@ -1706,7 +1724,8 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
|
|||
const int x_ne = ne01 * ne00;
|
||||
const int y_ne = ne11 * ne10;
|
||||
const int d_ne = ne11 * ne01;
|
||||
const size_t q_sz = ggml_type_size(type) * x_ne / ggml_blck_size(type);
|
||||
const int x_bps = x_ne / ggml_blck_size(type); // blocks per 2D slice
|
||||
const size_t q_sz = ggml_type_size(type) * x_bps;
|
||||
|
||||
size_t x_size;
|
||||
size_t y_size;
|
||||
|
|
@ -1774,9 +1793,10 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
|
|||
} else { // general dequantization kernel + CLBlast matrix matrix multiplication
|
||||
// convert src0 to fp32 on device
|
||||
const size_t global = x_ne / global_denom;
|
||||
const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
|
||||
CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
|
||||
CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
|
||||
CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, NULL, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
|
||||
CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, offset > 0 ? &offset : NULL, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
|
||||
|
||||
// copy src1 to device
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
|
||||
|
|
@ -1908,17 +1928,19 @@ void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) {
|
|||
const int64_t ne3 = tensor->ne[3];
|
||||
|
||||
const ggml_type type = tensor->type;
|
||||
const size_t q_sz = ggml_type_size(type) * ne0 * ne1 * ne2 * ne3 / ggml_blck_size(type);
|
||||
const size_t s_sz = ggml_type_size(type) * (size_t) (ne0 * ne1 / ggml_blck_size(type));
|
||||
const size_t q_sz = s_sz * (size_t) (ne2 * ne3);
|
||||
|
||||
size_t q_size;
|
||||
cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size);
|
||||
|
||||
tensor->data = data;
|
||||
// copy tensor to device
|
||||
size_t offset = 0;
|
||||
for (int64_t i3 = 0; i3 < ne3; i3++) {
|
||||
for (int64_t i2 = 0; i2 < ne2; i2++) {
|
||||
int i = i3*ne2 + i2;
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, dst, i*ne0*ne1, tensor, i3, i2, NULL));
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, dst, offset, tensor, i3, i2, NULL));
|
||||
offset += s_sz;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue