chore: add mypy inference fp8_impls

Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>

llama_stack/distribution/inspect.py

@@ -54,6 +54,7 @@ class DistributionInspectImpl(Inspect):
                             provider_types=[],  # These APIs don't have "real" providers - they're internal to the stack
                         )
                         for e in endpoints
+                        if e.methods
                     ]
                 )
             else:
@@ -63,10 +64,11 @@ class DistributionInspectImpl(Inspect):
                         [
                             RouteInfo(
                                 route=e.path,
-                                method=next(iter([m for m in e.methods if m != "HEAD"])),
+                                method=next(iter([m for m in e.methods if m != "HEAD"])) if e.methods else "POST",
                                 provider_types=[p.provider_type for p in providers],
                             )
                             for e in endpoints
+                            if e.methods
                         ]
                     )
 

llama_stack/distribution/server/routes.py

@@ -64,9 +64,7 @@ def get_all_api_routes() -> dict[Api, list[Route]]:
                 http_method = hdrs.METH_DELETE
             else:
                 http_method = hdrs.METH_POST
-            routes.append(
+            routes.append(Route(path=path, methods=[http_method], name=name, endpoint=None))
-                Route(path=path, methods=[http_method], name=name, endpoint=None)
-            )  # setting endpoint to None since don't use a Router object
 
         apis[api] = routes
 
@@ -95,6 +93,8 @@ def initialize_route_impls(impls: dict[Api, Any]) -> RouteImpls:
             impl = impls[api]
             func = getattr(impl, route.name)
             # Get the first (and typically only) method from the set, filtering out HEAD
+            if route.methods is None:
+                continue  # Skip if no methods are available
             available_methods = [m for m in route.methods if m != "HEAD"]
             if not available_methods:
                 continue  # Skip if only HEAD method is available

llama_stack/models/llama/checkpoint.py

@@ -42,14 +42,14 @@ def maybe_reshard_state_dict(
     else:
         torch.set_default_tensor_type(torch.cuda.BFloat16Tensor)
 
-    ckpt_paths = np.array(sorted(ckpt_paths))
+    ckpt_paths_array = np.array(sorted(ckpt_paths))
 
     new_mp_size, new_mp_rank = get_model_parallel_world_size(), get_model_parallel_rank()
-    old_mp_size = len(ckpt_paths)
+    old_mp_size = len(ckpt_paths_array)
     old_mp_ranks = map_mp_rank(old_mp_size, new_mp_size, new_mp_rank)
 
-    print(f"Loading checkpoint shards:\n{str(ckpt_paths[old_mp_ranks])}")  # type: ignore
+    print(f"Loading checkpoint shards:\n{str(ckpt_paths_array[old_mp_ranks])}")
-    paths = ckpt_paths[old_mp_ranks]  # type: ignore
+    paths = ckpt_paths_array[old_mp_ranks]
     state_dicts = [torch.load(str(p), map_location=map_location, mmap=mmap) for p in paths]
 
     if new_mp_size == old_mp_size:

llama_stack/providers/inline/telemetry/meta_reference/console_span_processor.py

@@ -34,6 +34,9 @@ class ConsoleSpanProcessor(SpanProcessor):
         if span.attributes and span.attributes.get("__autotraced__"):
             return
 
+        if span.start_time is None:
+            return
+
         timestamp = datetime.fromtimestamp(span.start_time / 1e9, tz=UTC).strftime("%H:%M:%S.%f")[:-3]
 
         print(
@@ -46,6 +49,9 @@ class ConsoleSpanProcessor(SpanProcessor):
         if span.attributes and span.attributes.get("__autotraced__"):
             return
 
+        if span.end_time is None:
+            return
+
         timestamp = datetime.fromtimestamp(span.end_time / 1e9, tz=UTC).strftime("%H:%M:%S.%f")[:-3]
 
         span_context = (
@@ -59,6 +65,7 @@ class ConsoleSpanProcessor(SpanProcessor):
         elif span.status.status_code != StatusCode.UNSET:
             span_context += f"{COLORS['reset']} [{span.status.status_code}]"
 
+        if span.start_time is not None and span.end_time is not None:
             duration_ms = (span.end_time - span.start_time) / 1e6
             span_context += f"{COLORS['reset']} ({duration_ms:.2f}ms)"
 
@@ -76,6 +83,9 @@ class ConsoleSpanProcessor(SpanProcessor):
         for event in span.events:
             event_time = datetime.fromtimestamp(event.timestamp / 1e9, tz=UTC).strftime("%H:%M:%S.%f")[:-3]
 
+            severity = "info"
+            message = event.name
+            if event.attributes:
                 severity = event.attributes.get("severity", "info")
                 message = event.attributes.get("message", event.name)
                 if isinstance(message, dict | list):
@@ -87,9 +97,10 @@ class ConsoleSpanProcessor(SpanProcessor):
                 "info": COLORS["white"],
                 "debug": COLORS["dim"],
             }
-            msg_color = severity_colors.get(severity, COLORS["white"])
+            msg_color = severity_colors.get(str(severity), COLORS["white"])
 
-            print(f" {event_time} {msg_color}[{severity.upper()}] {message}{COLORS['reset']}")
+            severity_str = str(severity).upper() if severity else "INFO"
+            print(f" {event_time} {msg_color}[{severity_str}] {message}{COLORS['reset']}")
 
             if event.attributes:
                 for key, value in event.attributes.items():

llama_stack/providers/inline/telemetry/meta_reference/sqlite_span_processor.py

@@ -10,8 +10,7 @@ import sqlite3
 import threading
 from datetime import UTC, datetime
 
-from opentelemetry.sdk.trace import SpanProcessor
+from opentelemetry.sdk.trace import ReadableSpan, SpanProcessor
-from opentelemetry.trace import Span
 from opentelemetry.trace.span import format_span_id, format_trace_id
 
 from llama_stack.providers.utils.telemetry.tracing import LOCAL_ROOT_SPAN_MARKER
@@ -93,11 +92,11 @@ class SQLiteSpanProcessor(SpanProcessor):
         conn.commit()
         cursor.close()
 
-    def on_start(self, span: Span, parent_context=None):
+    def on_start(self, span: ReadableSpan, parent_context=None):
         """Called when a span starts."""
         pass
 
-    def on_end(self, span: Span):
+    def on_end(self, span: ReadableSpan):
         """Called when a span ends. Export the span data to SQLite."""
         try:
             conn = self._get_connection()

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -168,7 +168,7 @@ def _process_vllm_chat_completion_end_of_stream(
     last_chunk_content: str | None,
     current_event_type: ChatCompletionResponseEventType,
     tool_call_bufs: dict[str, UnparseableToolCall] | None = None,
-) -> list[OpenAIChatCompletionChunk]:
+) -> list[ChatCompletionResponseStreamChunk]:
     chunks = []
 
     if finish_reason is not None:
@@ -247,9 +247,10 @@ async def _process_vllm_chat_completion_stream_response(
         if choice.delta.tool_calls:
             for delta_tool_call in choice.delta.tool_calls:
                 tool_call = convert_tool_call(delta_tool_call)
-                if delta_tool_call.index not in tool_call_bufs:
+                index_str = str(delta_tool_call.index)
-                    tool_call_bufs[delta_tool_call.index] = UnparseableToolCall()
+                if index_str not in tool_call_bufs:
-                tool_call_buf = tool_call_bufs[delta_tool_call.index]
+                    tool_call_bufs[index_str] = UnparseableToolCall()
+                tool_call_buf = tool_call_bufs[index_str]
                 tool_call_buf.tool_name += str(tool_call.tool_name)
                 tool_call_buf.call_id += tool_call.call_id
                 tool_call_buf.arguments += (

pyproject.toml

@@ -251,7 +251,7 @@ exclude = [
     "^llama_stack/models/llama/llama3/multimodal/model\\.py$",
     "^llama_stack/models/llama/llama4/",
     "^llama_stack/providers/inline/inference/meta_reference/parallel_utils\\.py$",
-    "^llama_stack/providers/inline/inference/meta_reference/quantization/fp8_impls\\.py$",
+    "^llama_stack/providers/inline/inference/meta_reference/quantization/loader\\.py$",
     "^llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers\\.py$",
     "^llama_stack/providers/inline/inference/vllm/",
     "^llama_stack/providers/inline/post_training/common/validator\\.py$",