Merge branch 'main' into c0sogi/main

README.md

@@ -169,7 +169,7 @@ docker run --rm -it -p 8000:8000 -v /path/to/models:/models -e MODEL=/models/ggm
 ## Low-level API
 
 The low-level API is a direct [`ctypes`](https://docs.python.org/3/library/ctypes.html) binding to the C API provided by `llama.cpp`.
-The entire lowe-level API can be found in [llama_cpp/llama_cpp.py](https://github.com/abetlen/llama-cpp-python/blob/master/llama_cpp/llama_cpp.py) and directly mirrors the C API in [llama.h](https://github.com/ggerganov/llama.cpp/blob/master/llama.h).
+The entire low-level API can be found in [llama_cpp/llama_cpp.py](https://github.com/abetlen/llama-cpp-python/blob/master/llama_cpp/llama_cpp.py) and directly mirrors the C API in [llama.h](https://github.com/ggerganov/llama.cpp/blob/master/llama.h).
 
 Below is a short example demonstrating how to use the low-level API to tokenize a prompt:
 

llama_cpp/llama.py

@@ -29,6 +29,8 @@ from .llama_grammar import LlamaGrammar
 import numpy as np
 import numpy.typing as npt
 
+from .utils import suppress_stdout_stderr
+
 class BaseLlamaCache(ABC):
     """Base cache class for a llama.cpp model."""
 
@@ -228,6 +230,7 @@ class Llama:
         grammar: Optional[Union[str, Path]] = None,
         n_gqa: Optional[int] = None,  # (TEMPORARY) must be 8 for llama2 70b
         rms_norm_eps: Optional[float] = None,  # (TEMPORARY)
+        mul_mat_q: Optional(bool) = None,  # (TEMPORARY)
         verbose: bool = True,
     ):
         """Load a llama.cpp model from `model_path`.
@@ -281,7 +284,9 @@ class Llama:
 
         if self.tensor_split is not None:
             FloatArray = (ctypes.c_float * len(self.tensor_split))(*self.tensor_split)
-            self._p_tensor_split = ctypes.POINTER(ctypes.c_float)(FloatArray) # keep a reference to the array so it is not gc'd
+            self._p_tensor_split = ctypes.POINTER(ctypes.c_float)(
+                FloatArray
+            )  # keep a reference to the array so it is not gc'd
             self.params.tensor_split = self._p_tensor_split
 
         self.params.rope_freq_base = rope_freq_base
@@ -293,6 +298,9 @@ class Llama:
         if rms_norm_eps is not None:
             self.params.rms_norm_eps = rms_norm_eps
 
+        if mul_mat_q is not None:
+            self.params.mul_mat_q = mul_mat_q
+
         self.last_n_tokens_size = last_n_tokens_size
         self.n_batch = min(n_ctx, n_batch)
 
@@ -310,12 +318,25 @@ class Llama:
         if not os.path.exists(model_path):
             raise ValueError(f"Model path does not exist: {model_path}")
 
+        if verbose:
+            self.model = llama_cpp.llama_load_model_from_file(
+                self.model_path.encode("utf-8"), self.params
+            )
+        else:
+            with suppress_stdout_stderr():
                 self.model = llama_cpp.llama_load_model_from_file(
                     self.model_path.encode("utf-8"), self.params
                 )
         assert self.model is not None
 
+        if verbose:
             self.ctx = llama_cpp.llama_new_context_with_model(self.model, self.params)
+        else:
+            with suppress_stdout_stderr():
+                print("here")
+                self.ctx = llama_cpp.llama_new_context_with_model(
+                    self.model, self.params
+                )
 
         assert self.ctx is not None
 
@@ -986,9 +1007,7 @@ class Llama:
                 for token in remaining_tokens:
                     token_end_position += len(self.detokenize([token]))
                     # Check if stop sequence is in the token
-                    if token_end_position >= (
-                        remaining_length - first_stop_position
-                    ):
+                    if token_end_position >= (remaining_length - first_stop_position):
                         break
                     logprobs_or_none: Optional[CompletionLogprobs] = None
                     if logprobs is not None:
@@ -1530,10 +1549,10 @@ class Llama:
             return self._convert_text_completion_to_chat(completion)
 
     def __del__(self):
-        if self.model is not None:
+        if hasattr(self, "model") and self.model is not None:
             llama_cpp.llama_free_model(self.model)
             self.model = None
-        if self.ctx is not None:
+        if hasattr(self, "ctx") and self.ctx is not None:
             llama_cpp.llama_free(self.ctx)
             self.ctx = None
 

llama_cpp/llama_cpp.py

@@ -181,6 +181,7 @@ llama_progress_callback = ctypes.CFUNCTYPE(None, c_float, c_void_p)
 
 #     // Keep the booleans together to avoid misalignment during copy-by-value.
 #     bool low_vram;   // if true, reduce VRAM usage at the cost of performance
+#     bool mul_mat_q;  // if true, use experimental mul_mat_q kernels
 #     bool f16_kv;     // use fp16 for KV cache
 #     bool logits_all; // the llama_eval() call computes all logits, not just the last one
 #     bool vocab_only; // only load the vocabulary, no weights
@@ -203,6 +204,7 @@ class llama_context_params(Structure):
         ("progress_callback", llama_progress_callback),
         ("progress_callback_user_data", c_void_p),
         ("low_vram", c_bool),
+        ("mul_mat_q", c_bool),
         ("f16_kv", c_bool),
         ("logits_all", c_bool),
         ("vocab_only", c_bool),

llama_cpp/server/app.py

@@ -103,6 +103,10 @@ class Settings(BaseSettings):
         default=None,
         description="TEMPORARY",
     )
+    mul_mat_q: Optional[bool] = Field(
+        default=None,
+        description="TEMPORARY",
+    )
 
 
 class ErrorResponse(TypedDict):

llama_cpp/utils.py

@@ -0,0 +1,38 @@
+import os
+import sys
+
+
+class suppress_stdout_stderr(object):
+    # Oddly enough this works better than the contextlib version
+    def __enter__(self):
+        self.outnull_file = open(os.devnull, "w")
+        self.errnull_file = open(os.devnull, "w")
+
+        self.old_stdout_fileno_undup = sys.stdout.fileno()
+        self.old_stderr_fileno_undup = sys.stderr.fileno()
+
+        self.old_stdout_fileno = os.dup(sys.stdout.fileno())
+        self.old_stderr_fileno = os.dup(sys.stderr.fileno())
+
+        self.old_stdout = sys.stdout
+        self.old_stderr = sys.stderr
+
+        os.dup2(self.outnull_file.fileno(), self.old_stdout_fileno_undup)
+        os.dup2(self.errnull_file.fileno(), self.old_stderr_fileno_undup)
+
+        sys.stdout = self.outnull_file
+        sys.stderr = self.errnull_file
+        return self
+
+    def __exit__(self, *_):
+        sys.stdout = self.old_stdout
+        sys.stderr = self.old_stderr
+
+        os.dup2(self.old_stdout_fileno, self.old_stdout_fileno_undup)
+        os.dup2(self.old_stderr_fileno, self.old_stderr_fileno_undup)
+
+        os.close(self.old_stdout_fileno)
+        os.close(self.old_stderr_fileno)
+
+        self.outnull_file.close()
+        self.errnull_file.close()

poetry.lock

@@ -384,17 +384,17 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "fastapi"
-version = "0.100.1"
+version = "0.101.0"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "fastapi-0.100.1-py3-none-any.whl", hash = "sha256:ec6dd52bfc4eff3063cfcd0713b43c87640fefb2687bbbe3d8a08d94049cdf32"},
-    {file = "fastapi-0.100.1.tar.gz", hash = "sha256:522700d7a469e4a973d92321ab93312448fbe20fca9c8da97effc7e7bc56df23"},
+    {file = "fastapi-0.101.0-py3-none-any.whl", hash = "sha256:494eb3494d89e8079c20859d7ca695f66eaccc40f46fe8c75ab6186d15f05ffd"},
+    {file = "fastapi-0.101.0.tar.gz", hash = "sha256:ca2ae65fe42f6a34b5cf6c994337149154b1b400c39809d7b2dccdceb5ae77af"},
 ]
 
 [package.dependencies]
-pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<3.0.0"
+pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
 starlette = ">=0.27.0,<0.28.0"
 typing-extensions = ">=4.5.0"
 
@@ -744,13 +744,13 @@ files = [
 
 [[package]]
 name = "mkdocs"
-version = "1.5.1"
+version = "1.5.2"
 description = "Project documentation with Markdown."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "mkdocs-1.5.1-py3-none-any.whl", hash = "sha256:67e889f8d8ba1fe5decdfc59f5f8f21d6a8925a129339e93dede303bdea03a98"},
-    {file = "mkdocs-1.5.1.tar.gz", hash = "sha256:f2f323c62fffdf1b71b84849e39aef56d6852b3f0a5571552bca32cefc650209"},
+    {file = "mkdocs-1.5.2-py3-none-any.whl", hash = "sha256:60a62538519c2e96fe8426654a67ee177350451616118a41596ae7c876bb7eac"},
+    {file = "mkdocs-1.5.2.tar.gz", hash = "sha256:70d0da09c26cff288852471be03c23f0f521fc15cf16ac89c7a3bfb9ae8d24f9"},
 ]
 
 [package.dependencies]
@@ -1757,4 +1757,4 @@ server = ["fastapi", "pydantic-settings", "sse-starlette", "uvicorn"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8.1"
-content-hash = "6718d680fa89f9518a232c1110ba43958d3e21c54c4dbd9129effa4f40a02b81"
+content-hash = "4bfb67dfb72b02c845376211f7f958b2ece8c985944fbd03d246c858e846ddf6"

pyproject.toml

@@ -25,7 +25,7 @@ pydantic-settings = { version = ">=2.0.1", optional = true }
 [tool.poetry.group.dev.dependencies]
 black = "^23.7.0"
 twine = "^4.0.2"
-mkdocs = "^1.4.3"
+mkdocs = "^1.5.2"
 mkdocstrings = {extras = ["python"], version = "^0.22.0"}
 mkdocs-material = "^9.1.21"
 pytest = "^7.4.0"

vendor/llama.cpp

@@ -1 +1 @@
-Subproject commit 41c674161fb2459bdf7806d1eebead15bc5d046e
+Subproject commit f5bfea0580e417f99850d5456ca541d871a3e48c