+
+
+
+
+
+
\ No newline at end of file
diff --git a/client/graphpaper-inline/src/MetricCard.svelte b/client/graphpaper-inline/src/MetricCard.svelte
new file mode 100644
index 000000000..85c19d358
--- /dev/null
+++ b/client/graphpaper-inline/src/MetricCard.svelte
@@ -0,0 +1,28 @@
+
+
+
+
+
+ {:else}
+ {#if typeof value === "number"}
+
\ No newline at end of file
diff --git a/client/graphpaper-inline/src/Minibar.svelte b/client/graphpaper-inline/src/Minibar.svelte
new file mode 100644
index 000000000..0b54777f2
--- /dev/null
+++ b/client/graphpaper-inline/src/Minibar.svelte
@@ -0,0 +1,40 @@
+
+
+
+
+{metricDef.name}
+ {#if value.constructor === Array}
+ {value.toFixed(metricDef.precision)}{metricDef.units}
+ {:else}
+ {value}{metricDef.units}
+ {/if}
+ {/if}
+
+
+
\ No newline at end of file
diff --git a/client/graphpaper-inline/src/ResizeListener.svelte b/client/graphpaper-inline/src/ResizeListener.svelte
new file mode 100644
index 000000000..aa46655c8
--- /dev/null
+++ b/client/graphpaper-inline/src/ResizeListener.svelte
@@ -0,0 +1,33 @@
+
+
\ No newline at end of file
diff --git a/client/graphpaper-inline/src/Select.svelte b/client/graphpaper-inline/src/Select.svelte
new file mode 100644
index 000000000..8546a1d67
--- /dev/null
+++ b/client/graphpaper-inline/src/Select.svelte
@@ -0,0 +1,46 @@
+
+
+
+
+
+ {#if showList}
+
diff --git a/client/graphpaper-inline/src/StitchHandler.svelte b/client/graphpaper-inline/src/StitchHandler.svelte
new file mode 100644
index 000000000..fdc7058c4
--- /dev/null
+++ b/client/graphpaper-inline/src/StitchHandler.svelte
@@ -0,0 +1,31 @@
+
+
+
+-
+ {#each values as value, i}
+
- selectOption(value)} on:keypress={(_) => {}}>{value} + {/each} +
+
\ No newline at end of file
diff --git a/client/graphpaper-inline/src/TokenGridItem.svelte b/client/graphpaper-inline/src/TokenGridItem.svelte
new file mode 100644
index 000000000..32857ba8d
--- /dev/null
+++ b/client/graphpaper-inline/src/TokenGridItem.svelte
@@ -0,0 +1,50 @@
+
+
+
+{#each token.text as ch, i}
+ {#if ch === ' '}
+
+ {#if i === 0}
+
+ {token.role}
+
+ {/if}
+
+
+ {:else if ch === '\t'}
+
+ {#if i === 0}
+
+ {token.role}
+
+ {/if}
+ \t
+
+ {:else if ch === '\n'}
+
+ {#if i === 0}
+
+ {token.role}
+
+ {/if}
+ \n
+
+
+ {:else}
+
+ {#if i === 0}
+
+ {token.role}
+
+ {/if}
+ {ch}
+
+ {/if}
+{/each}
\ No newline at end of file
diff --git a/client/graphpaper-inline/src/clickoutside.ts b/client/graphpaper-inline/src/clickoutside.ts
new file mode 100644
index 000000000..0c8b38c27
--- /dev/null
+++ b/client/graphpaper-inline/src/clickoutside.ts
@@ -0,0 +1,18 @@
+// Action for clicking outside an element.
+
+export function clickOutside(node: HTMLElement) {
+ const handleClick = (event: MouseEvent) => {
+ let target = event.target as HTMLElement;
+ if (!node.contains(target)) {
+ node.dispatchEvent(new CustomEvent('outclick'));
+ }
+ };
+
+ document.addEventListener('click', handleClick, true);
+
+ return {
+ destroy() {
+ document.removeEventListener('click', handleClick, true);
+ }
+ };
+}
\ No newline at end of file
diff --git a/client/graphpaper-inline/src/interfaces.ts b/client/graphpaper-inline/src/interfaces.ts
new file mode 100644
index 000000000..84d528740
--- /dev/null
+++ b/client/graphpaper-inline/src/interfaces.ts
@@ -0,0 +1,26 @@
+// Interfaces used within the client. This is separate to messaging interfaces.
+
+import type {GenToken} from "./stitch";
+
+export interface MetricDef {
+ name: string,
+ units: string,
+ description: string,
+ isScalar: boolean,
+ precision: number,
+}
+
+export type MetricVal = string | number | Array
+
+ {#if requireFullReplay}
+
+
+ {/if}
+ {#each tokens as token, i}
+ {#if token.special === true && token.role !== ""}
+
+ {#if i === 0}
+
+ {:else}
+ {#each {length: 2} as _}
+
+
+ {/each}
+
+ {/if}
+ {/if}
+
+ {/each}
+
+ {#if isCompleted === false}
+
+
+
+ {/if}
+
+
+{role_name.lower()}
_||>"
-
- # Start of either debug or HTML no disp block
- if indent:
- lm += nodisp_start
- else:
- lm += span_start
-
- # TODO [HN]: Temporary change while I instrument chat_template in transformers only.
- # Eventually have all models use chat_template.
- if hasattr(lm, "get_role_start"):
- lm += lm.get_role_start(role_name, **kwargs)
- elif hasattr(lm, "chat_template"):
- lm += lm.chat_template.get_role_start(role_name)
- else:
- raise Exception(
- f"You need to use a chat model in order the use role blocks like `with {role_name}():`! Perhaps you meant to use the {type(lm).__name__}Chat class?"
- )
-
- # End of either debug or HTML no disp block
- if indent:
- lm += nodisp_end
- else:
- lm += span_end
-
+ lm += lm.role_opener(role_name, **kwargs)
return lm
@guidance
def role_closer(lm, role_name, **kwargs):
- indent = getattr(lm, "indent_roles", True)
- # Start of either debug or HTML no disp block
- if indent:
- lm += nodisp_start
- else:
- lm += span_start
-
- # TODO [HN]: Temporary change while I instrument chat_template in transformers only.
- # Eventually have all models use chat_template.
- if hasattr(lm, "get_role_end"):
- lm += lm.get_role_end(role_name)
- elif hasattr(lm, "chat_template"):
- lm += lm.chat_template.get_role_end(role_name)
-
- # End of either debug or HTML no disp block
- if indent:
- lm += nodisp_end
- else:
- lm += span_end
-
- # End of top container
- if indent:
- lm += "<||_html:
[0]]).decode()
- + ')
',
- display_out,
- )
- display_out = (
- "" - + display_out - + "" + def html(self): + """Displays model as HTML.""" + # NOTE(nopdive): Have this public for now until all widget related issues are sorted out. + clear_output(wait=True) + display(HTML(self._html())) + + def _html(self) -> str: + """Returns HTML string that displays the model object.""" + + return trace_node_to_html( + self.engine.trace_handler.id_node_map[self._id], hasattr(self, "indent_roles") ) - return display_out def _send_to_event_queue(self, value): """For streaming in code. @@ -363,10 +923,13 @@ def copy(self): # then copy a few things we need deeper copies of new_lm._variables = self._variables.copy() new_lm._variables_log_probs = self._variables_log_probs.copy() + new_lm._variables_positions = self._variables_positions.copy() new_lm.opened_blocks = self.opened_blocks.copy() # create a new clean event queue - new_lm._event_queue = None # we start with no event queue because nobody is listening to us yet + new_lm._event_queue = ( + None # we start with no event queue because nobody is listening to us yet + ) if self._event_queue is not None: # if the current lm has an event queue, we make it our parent @@ -374,8 +937,19 @@ def copy(self): elif self._event_parent is not None: # otherwise if the current event que has an event parent then that is also our parent - new_lm._event_parent = self._event_parent - + new_lm._event_parent = self._event_parent + + new_lm._id = self.__class__.gen_id() + new_lm._parent_id = self._id + new_lm._trace_nodes = set() + new_lm._update_trace_node(new_lm._id, new_lm._parent_id, None) + self.engine.model_dict[new_lm._id] = new_lm + new_lm.vis_chunk = None + new_lm._parent = self + new_lm.metrics = self.metrics.model_copy(deep=True) + + weakref.finalize(new_lm, log_cleanup, f"model({id(self)})") + log_copy(f"model({id(self)})") return new_lm def _inplace_append(self, value, force_silent=False): @@ -386,40 +960,18 @@ def _inplace_append(self, value, force_silent=False): Parameters ---------- - value : bytes + value : bytes | str The bytes we should append to our current state. """ # update the byte state - self._state += str(value) # TODO: make _state to be bytes not a string - - # see if we should update the display - if not force_silent: - self._update_display() + v = value + if not isinstance(v, str): + v = str(value) + self._state += v # this is for programmatic streaming among other things - if Model._throttle_refresh > 0: - curr_time = time.time() - if curr_time - self._last_event_stream >= self.max_display_rate: - self._last_event_stream = curr_time - self._send_to_event_queue(self) - else: - self._send_to_event_queue(self) - - def _update_display(self, throttle=True): - if self.echo: - if Model._throttle_refresh > 0: - curr_time = time.time() - if throttle and curr_time - self._last_display < self.max_display_rate: - return # we are throttling the update - else: - self._last_display = curr_time - - if ipython_is_imported: - clear_output(wait=True) - display(HTML(self._html())) - else: - pprint(self._state) + self._send_to_event_queue(self) def reset(self, clear_variables=True): """This resets the state of the model object. @@ -429,13 +981,40 @@ def reset(self, clear_variables=True): clear_variables : bool If we should clear all the model object's variables in addition to reseting the byte state. """ + # TODO(nopdive): This violates the immutability assumption on model class for users. Remove on confirmation. + self._state = self._state[:0] self.opened_blocks = {} if clear_variables: self._variables = {} self._variables_log_probs = {} + self._variables_positions = {} return self + def role_opener(self, role_name, **kwargs): + # TODO [HN]: Temporary change while I instrument chat_template in transformers only. + # Eventually have all models use chat_template. + if hasattr(self, "get_role_start"): + return self.get_role_start(role_name, **kwargs) + elif hasattr(self, "chat_template"): + return self.chat_template.get_role_start(role_name) + else: + raise Exception( + f"You need to use a chat model in order the use role blocks like `with {role_name}():`! Perhaps you meant to use the {type(self).__name__}Chat class?" + ) + + def role_closer(self, role_name, **kwargs): + # TODO [HN]: Temporary change while I instrument chat_template in transformers only. + # Eventually have all models use chat_template. + if hasattr(self, "get_role_end"): + return self.get_role_end(role_name, **kwargs) + elif hasattr(self, "chat_template"): + return self.chat_template.get_role_end(role_name) + else: + raise Exception( + f"You need to use a chat model in order the use role blocks like `with {role_name}():`! Perhaps you meant to use the {type(self).__name__}Chat class?" + ) + def _repr_html_(self): if ipython_is_imported: clear_output(wait=True) @@ -443,14 +1022,30 @@ def _repr_html_(self): def _current_prompt(self): """The current prompt in bytes (which is the state without the context close tags).""" - return format_pattern.sub("", self._state) + return trace_node_to_str(self.engine.trace_handler.id_node_map[self._id]) + + def _update_trace_node( + self, identifier: int, parent_id: Optional[int], node_attr: Optional[NodeAttr] + ): + """Updates trace node that corresponds to this model.""" + + trace_node = self.engine.trace_handler.update_node(identifier, parent_id, node_attr) + self._trace_nodes.add(trace_node) + + if self.renderer is not None: + self.renderer.update( + TraceMessage( + trace_id=identifier, + parent_trace_id=parent_id, + node_attr=node_attr, + ) + ) def __str__(self): """A string representation of the current model object (that includes context closers).""" - out = self._current_prompt() - for context in reversed(self.opened_blocks): - out += format_pattern.sub("", self.opened_blocks[context][1]) - return out + + # TODO(nopdive): Ensure context closers or no? + return trace_node_to_str(self.engine.trace_handler.id_node_map[self._id]) def __add__(self, value): """Adding is the primary mechanism for extending model state. @@ -465,102 +1060,164 @@ def __add__(self, value): # (we need to do this since Model objects are immutable) lm = self.copy() - # inside this context we are free to drop display calls that come too close together - with throttle_refresh(): + # find blocks that are now active, but haven't been opened by lm yet + enter_blocks = [] + for context in Model.global_active_blocks: + if context not in lm.opened_blocks: + enter_blocks.append(context) + lm.opened_blocks[context] = (0, "") + + # find opened blocks by lm, but are no longer active + exit_blocks = [] + for context in list(reversed(lm.opened_blocks.keys())): + if context not in Model.global_active_blocks: + exit_blocks.append(context) + + # finish any exiting blocks + for context in exit_blocks: + pos, close_text = lm.opened_blocks[context] + del lm.opened_blocks[context] + + # handle variables + if context.name is not None: + # TODO(nopdive): Replace with trace traversal. + v = format_pattern.sub("", lm._state[pos:]) + lm._variables[context.name] = v + lm._variables_positions[context.name] = pos + lm._update_trace_node( + lm._id, lm._parent_id, CaptureOutput(name=context.name, value=v) + ) - # find what new blocks need to be applied - new_blocks = [] - for context in Model.open_blocks: - if context not in lm.opened_blocks: - new_blocks.append(context) + # add closer + # TODO(nopdive): Consider removing context closer/opener on confirmation. + closer_text = context.closer if isinstance(context.closer, str) else "" + lm._update_trace_node( + lm._id, lm._parent_id, RoleCloserInput(name=context.name, text=closer_text) + ) + lm += context.closer + lm = lm.copy() + + # start any entering blocks + for context in enter_blocks: + # add opener + opener_text = context.opener if isinstance(context.opener, str) else "" + closer_text = context.closer if isinstance(context.closer, str) else "" + + lm._update_trace_node( + lm._id, + lm._parent_id, + RoleOpenerInput(name=context.name, text=opener_text, closer_text=closer_text), + ) + lm += context.opener + lm = lm.copy() + + # store closer for state extraction later + # TODO(nopdive): Replace with trace traversal. + lm.opened_blocks[context] = (len(lm._state), closer_text) + + # handle variables + # NOTE(nopdive): No stack for variables, this process removes shadowed variables? + if context.name is not None: + if context.name in lm._variables: + del lm._variables[context.name] + if context.name in lm._variables_log_probs: + del lm._variables_log_probs[context.name] + if context.name in lm._variables_positions: + del lm._variables_positions[context.name] + + if isinstance(value, TextOutput): + lm._inplace_append(value.value) + out = lm + out._update_trace_node(out._id, out._parent_id, value) + elif isinstance(value, CaptureOutput): + out = lm + out._update_trace_node(out._id, out._parent_id, value) + elif isinstance(value, str): + # wrap raw string values - # mark this so we don't re-add when computing the opener or closer (even though we don't know the close text yet) - lm.opened_blocks[context] = (0, "") + is_id = False + parts = re.split(_tag_pattern, value) - # find what old blocks need to be removed - old_blocks = [] - for context in list(reversed(lm.opened_blocks)): - if context not in Model.open_blocks and context in lm.opened_blocks: - old_blocks.append((lm.opened_blocks[context], context)) + # we have no embedded objects + if len(parts) == 1: + lm._update_trace_node(lm._id, lm._parent_id, LiteralInput(value=value)) - # delete this so we don't re-close when computing the opener or closer - del lm.opened_blocks[context] + lm._inplace_append(value) + out = lm - # close any newly closed contexts - for (pos, close_text), context in old_blocks: - if context.name is not None: - lm._variables[context.name] = format_pattern.sub( - "", lm._state[pos:] + # generate VisBytesChunk so we know this chunk is input + input_tokens = [] + if self.echo: + _bytes = value.encode("utf-8") + _tokens = out.engine.tokenizer.encode(_bytes) + out.vis_chunk = VisBytesChunk( + bytes=_bytes, + is_input=True, + input_tokens=[ + GenToken( + token_id=_token, + prob=1.0, + text=to_utf8_or_bytes_string(out.engine.tokenizer.decode([_token])), + latency_ms=0, + is_generated=False, + is_force_forwarded=False, + is_input=True, + ) + for _token in _tokens + ], ) - lm += context.closer - - # apply any newly opened contexts (new from this object's perspective) - for context in new_blocks: - lm += context.opener - with grammar_only(): - tmp = lm + context.closer - close_text = tmp._state[len(lm._state):] # get the new state added by calling the closer - lm.opened_blocks[context] = (len(lm._state), close_text) - - # clear out names that we override - if context.name is not None: - if context.name in lm._variables: - del lm._variables[context.name] - if context.name in lm._variables_log_probs: - del lm._variables_log_probs[context.name] + input_tokens = out.vis_chunk.input_tokens - # wrap raw string values - if isinstance(value, str): - is_id = False - parts = re.split(_tag_pattern, value) + out._update_trace_node( + out._id, + out._parent_id, + TextOutput(value=value, is_input=True, tokens=input_tokens), + ) - # we have no embedded objects - if len(parts) == 1: - lm._inplace_append(value) - out = lm + # if we have embedded objects we have to convert the string to a grammar tree + else: + lm._update_trace_node(lm._id, lm._parent_id, EmbeddedInput(value=value)) + + partial_grammar = _null_grammar + lm.suffix = "" + for i, part in enumerate(parts): + if i < len(parts) - 1: + lm.suffix = parts[i + 1] + if is_id: + call = _call_pool[part] + if isinstance(call, GrammarFunction): + partial_grammar += _call_pool[part] + else: + lm += partial_grammar + lm = _call_pool[part](lm) + partial_grammar = _null_grammar + elif part != "": + partial_grammar += string(part) + is_id = not is_id - # if we have embedded objects we have to convert the string to a grammar tree - else: - partial_grammar = _null_grammar - lm.suffix = "" - for i, part in enumerate(parts): - if i < len(parts) - 1: - lm.suffix = parts[i + 1] - if is_id: - call = _call_pool[part] - if isinstance(call, GrammarFunction): - partial_grammar += _call_pool[part] - else: - lm += partial_grammar - lm = _call_pool[part](lm) - partial_grammar = _null_grammar - elif part != "": - partial_grammar += string(part) - is_id = not is_id - out = lm + partial_grammar - - # if we find a null value we do nothing - elif isinstance(value, Null): - out = lm + out = lm + partial_grammar - # run stateless functions (grammar nodes) - elif isinstance(value, GrammarFunction): - out = lm._run_stateless(value) + # if we find a null value we do nothing + elif isinstance(value, Null): + out = lm - # run stateful functions - else: - out = value(lm) - if out is None: - raise Exception( - f"A guidance function returned `None`, not a model object! Did you forget to return the new lm at the end of your function?" - ) - if not isinstance(out, Model): - raise Exception( - f"A guidance function did not return a model object! Did you try to add a function to a model without calling the function? For example `model + guidance_function()` is correct, while `model + guidance_function` will cause this error." - ) + # run stateless functions (grammar nodes) + elif isinstance(value, GrammarFunction): + lm._update_trace_node(lm._id, lm._parent_id, StatelessGuidanceInput(value=value)) + out = lm._run_stateless(value) - # this flushes the display - out._inplace_append("") + # run stateful functions + else: + lm._update_trace_node(lm._id, lm._parent_id, StatefulGuidanceInput(value=value)) + out = value(lm) + if out is None: + raise Exception( + f"A guidance function returned `None`, not a model object! Did you forget to return the new lm at the end of your function?" + ) + if not isinstance(out, Model): + raise Exception( + f"A guidance function did not return a model object! Did you try to add a function to a model without calling the function? For example `model + guidance_function()` is correct, while `model + guidance_function` will cause this error." + ) return out @@ -588,9 +1245,7 @@ def __getitem__(self, key): else: for context in list(reversed(self.opened_blocks)): if context.name == key: - return format_pattern.sub( - "", self._state[self.opened_blocks[context][0] :] - ) + return format_pattern.sub("", self._state[self.opened_blocks[context][0] :]) raise KeyError(f"Model does not contain the variable '{key}'") @@ -739,10 +1394,13 @@ def _run_stateless(self, stateless_function, temperature=0.0, top_p=1.0, n=1): replacements = replace_model_variables(stateless_function, self) # start the generation stream - gen_obj = self.engine(self._current_prompt(), stateless_function) + gen_obj = self.engine(self._current_prompt(), stateless_function, echo=self.echo) # we will return a new extended version of ourselves, which we track as `lm` lm = self + lm.engine.metrics.engine_backtrack_tokens = lm.metrics.engine_backtrack_tokens + lm.engine.metrics.engine_input_tokens = lm.metrics.engine_input_tokens + lm.engine.metrics.engine_output_tokens = lm.metrics.engine_output_tokens # single generation if n == 1: @@ -751,6 +1409,7 @@ def _run_stateless(self, stateless_function, temperature=0.0, top_p=1.0, n=1): delayed_bytes = b"" # last_is_generated = False + for chunk in gen_obj: # we make everything full probability if we are not computing uncertainty @@ -767,16 +1426,43 @@ def _run_stateless(self, stateless_function, temperature=0.0, top_p=1.0, n=1): continue delayed_bytes = b"" + if chunk.backtrack: + lm.engine.metrics.engine_backtrack_tokens += chunk.backtrack + if len(chunk.new_bytes) > 0: generated_value += new_text - if chunk.is_generated: - lm += f"<||_html:_||>" - lm += new_text - if chunk.is_generated: - lm += "<||_html:_||>" - # last_is_generated = chunk.is_generated + # split chunk into generated and force_forwarded parts for better animated visualization + if chunk.generated_bytes: + lm += TextOutput( + value=chunk.generated_bytes.decode("utf8"), + is_generated=True, + token_count=0, + prob=0.0, + tokens=chunk.generated_tokens, + ) + + if chunk.force_forwarded_bytes: + lm += TextOutput( + value=chunk.force_forwarded_bytes.decode("utf8"), + is_force_forwarded=True, + token_count=0, + prob=0.0, + tokens=chunk.force_forwarded_tokens, + ) + + if self.echo: + lm.vis_chunk = VisBytesChunk( + bytes=chunk.new_bytes, + is_input=False, + # generated_bytes=chunk.generated_bytes, + generated_tokens=chunk.generated_tokens, + force_forwarded_tokens=chunk.force_forwarded_tokens, + backtrack=chunk.backtrack, + engine_outputs=chunk.engine_outputs, + ) + # last_is_generated = chunk.is_generated if len(chunk.capture_groups) > 0: for k in chunk.capture_groups: v = chunk.capture_groups[k] @@ -797,13 +1483,24 @@ def _run_stateless(self, stateless_function, temperature=0.0, top_p=1.0, n=1): if k not in lm or not isinstance(lm._variables[k], list): lm._variables[k] = [] - if k not in lm._variables_log_probs or not isinstance(lm._variables_log_probs[k], list): + lm += CaptureOutput(name=k) + if k not in lm._variables_log_probs or not isinstance( + lm._variables_log_probs[k], list + ): lm._variables_log_probs[k] = [] - + if k not in lm._variables_positions: + lm._variables_positions[k] = len(lm._state) + lm._variables[k].append(inner_v) lm._variables_log_probs[k].append( chunk.capture_group_log_probs[k][i] ) + lm += CaptureOutput( + name=k, + value=inner_v, + is_append=True, + log_probs=lm._variables_log_probs[k][i], + ) # ...or standard assignment mode else: @@ -813,8 +1510,15 @@ def _run_stateless(self, stateless_function, temperature=0.0, top_p=1.0, n=1): v = v.decode("utf8") if isinstance(v, bytes) else v except UnicodeDecodeError: pass + lm._variables[k] = v + lm._variables_positions[k] = len(lm._state) - len(v) lm._variables_log_probs[k] = chunk.capture_group_log_probs[k] + lm += CaptureOutput( + name=k, + value=v, + log_probs=chunk.capture_group_log_probs[k], + ) # if len(chunk.capture_groups) > 0: # for k in chunk.capture_groups: @@ -825,8 +1529,346 @@ def _run_stateless(self, stateless_function, temperature=0.0, top_p=1.0, n=1): logger.debug("finish Model._run_stateless") + lm.metrics = lm.engine.metrics.model_copy(deep=True) + return lm + def get_per_token_stats(self) -> list[GenTokenExtra]: + """Get the per token stats (prob, top-k, latency, etc.) for the model. + + Returns + List[GenTokenExtra]: a list of GenToken objects. + """ + + paths = [] + model = self + while model is not None: + paths.append(model) + if model._parent_id is None: + break + model: "Model" = self.engine.model_dict[model._parent_id] + paths.reverse() + + vis_chunks: list[VisBytesChunk] = [ + path.vis_chunk for path in paths if path.vis_chunk is not None + ] + + gen_tokens_infos: list[tuple[int, float, list[GenToken]]] = [] + gen_tokens_indices: list[int] = [] + for vis_chunk in vis_chunks: + for engine_output in vis_chunk.engine_outputs: + gen_tokens_infos.append( + ( + engine_output.issued_token.token_id, + engine_output.issued_token.latency_ms, + engine_output.masked_top_k, + ) + ) + gen_tokens_indices.append(len(gen_tokens_infos) - 1) + + text = self._state + token_ids = self.engine.tokenizer.encode(text.encode("utf-8")) + + # verify if text == encode(decode(text)) + # decoded_text = self.engine.tokenizer.decode(token_ids).decode("utf-8") + decoded_text = "".join(self.engine.tokenizer.decode([_token]).decode("utf-8") for _token in token_ids) + + token_texts: list[str] = [] + if text == decoded_text: + for idx, token_id in enumerate(token_ids): + token_texts.append(self.engine.tokenizer.decode([token_id]).decode("utf-8")) + else: + # Some models like phi-3 removes spaces and line breaks + # Scan through the text and find the missing chunks so we can map the token back into generated chunks by the parser later on + ptr_idx = 0 + for _idx, token_id in enumerate(token_ids): + missing_chunk = "" + decoded = self.engine.tokenizer.decode([token_id]).decode("utf-8") + if not text[ptr_idx:].startswith(decoded): + _next_idx = ptr_idx + found = False + while _next_idx < len(text): + if text[_next_idx:].startswith(decoded): + found = True + break + _next_idx += 1 + + assert found, f"Failed to find the token {decoded} in the text {text}" + missing_chunk = text[ptr_idx:_next_idx] + + token_texts.append(missing_chunk + decoded) + ptr_idx += len(missing_chunk + decoded) + + # verify + _decoded_text = "".join(token_texts) + assert _decoded_text in text, f"Failed to decode the text {text} into {_decoded_text}" + if _decoded_text in text and len(_decoded_text) < len(text): + # there are some missing chunks (probably empty spaces/ end lines removed by the tokenizer) + missing_chunk = text[len(_decoded_text):] + token_texts[-1] += missing_chunk + + # NOTE (loc): Not all engines support the get_logits method + try: + # make sure token_ids start with BOS token if needed + prev_token_ids_len = len(token_ids) + if ( + len(token_ids) > 0 + and self.engine.tokenizer.bos_token_id != None + and token_ids[0] != self.engine.tokenizer.bos_token_id + ): + token_ids = [self.engine.tokenizer.bos_token_id] + token_ids + + tokens_with_topk = self.engine.get_per_token_topk_probs(token_ids, top_k=self.engine._top_k) + + # remove the BOS token + if prev_token_ids_len != len(token_ids): + token_ids = token_ids[1:] + tokens_with_topk = tokens_with_topk[1:] + + except Exception as e: + logger.warning( + f"Failed to get the top k probabilities for the tokens {token_ids}. Error: {e}" + ) + # FIXME (loc): assume prob 1.0 for all tokens + tokens_with_topk = [] + for token_id, token_text in zip(token_ids, token_texts): + tokens_with_topk.append( + GenTokenExtra( + token_id=token_id, + prob=1.0, + text=token_text, + top_k=[ + GenToken(token_id=token_id, prob=1.0, text=token_text), + ], + ) + ) + + start_idx = 0 + end_idx = 1 + start_pos = 0 + remainder = "" + + processed_gen_tokens: list[GenTokenExtra] = [] + # Map token back to generated chunk to extract correct info (is_generated, latency, etc.) + for vis_chunk_idx, vis_chunk in enumerate(vis_chunks): + vis_text = vis_chunk.bytes.decode("utf-8") + + if not vis_text: + continue + + # Find the chunk starting at start_idx that contains the vis_text + end_idx = start_idx + _chunk = "".join(token_texts[start_idx : end_idx + 1]) + while vis_text not in _chunk and end_idx < len(token_texts): + # expand the chunk + end_idx += 1 + _chunk = "".join(token_texts[start_idx : end_idx + 1]) + + if vis_text not in _chunk and end_idx >= len(token_texts): + # failed = True + # break + raise Exception(f"Failed to find the {vis_text} in the tokens chunk {_chunk}") + + if vis_text == _chunk: + # perfect match + pass + else: + start_pos = _chunk.index(vis_text) + remainder = _chunk[start_pos + len(vis_text) :] + + if remainder: + # we have a current chunk that is larger than the vis_text + # probably the last token is a partial token that spans across two chunks due to backtracking + # we should not issue that token for now + end_idx -= 1 + + _chunk_token_ids = token_ids[start_idx : end_idx + 1] + _chunk_tokens_with_topk = tokens_with_topk[start_idx : end_idx + 1] + _chunk_token_texts = token_texts[start_idx : end_idx + 1] + + is_input = len(vis_chunk.input_tokens) > 0 + is_force_forwarded = len(vis_chunk.force_forwarded_tokens) > 0 + + _gen_tokens: list[GenTokenExtra] = [] + for token_id, token_info_with_topk, token_text in zip( + _chunk_token_ids, _chunk_tokens_with_topk, _chunk_token_texts + ): + prob = -1 + for _token in token_info_with_topk.top_k: + if _token.token_id == token_id: + prob = _token.prob + break + + _gen_token = GenTokenExtra( + token_id=token_id, + prob=prob, + text=token_text, + latency_ms=0, + is_input=is_input, + is_generated=False, + is_force_forwarded=False, + ) + _gen_token.top_k = token_info_with_topk.top_k + _gen_tokens.append(_gen_token) + + for i, _gen_token in enumerate(_gen_tokens): + if not is_input: + if i < len(vis_chunk.generated_tokens): + _gen_token.is_generated = True + else: + if is_force_forwarded: + _gen_token.is_force_forwarded = True + + # Start from the end of current chunk + # go backwards to find the match between token and associated text string + found_perfect_match = False + max_idx = gen_tokens_indices[vis_chunk_idx] + for idx in range(max_idx, -1, -1): + if _gen_token.token_id == gen_tokens_infos[idx][0]: + _gen_token.latency_ms = gen_tokens_infos[idx][1] + _masked_top_k = gen_tokens_infos[idx][2] + + # if we find a match in tokens list generated by the engine, then this token should be marked as generated + _gen_token.is_generated = True + _gen_token.is_force_forwarded = False + + if _masked_top_k is None: + # in free accepting state, no masking + for _token in _gen_token.top_k: + _token.is_masked = False + else: + _masked_tokens = [token.token_id for token in _masked_top_k] + for _token in _gen_token.top_k: + if _token.token_id not in _masked_tokens: + _token.is_masked = True + else: + _token.is_masked = False + + found_perfect_match = True + break + + # NOTE (loc): There are cases that the generated token and issued token are not matched + # for example, the engine may issue token "pl" but the parser decides to generate token "plate" due to the constraints + # To mitigate the issue, we narrow down the search space to find the text that may contain the generated token + if not found_perfect_match: + # only search within this chunk + max_idx = gen_tokens_indices[vis_chunk_idx] + prev_max_idx = ( + -1 if vis_chunk_idx == 0 else gen_tokens_indices[vis_chunk_idx - 1] - 1 + ) + for idx in range(max_idx, prev_max_idx, -1): + if ( + self.engine.tokenizer.decode([gen_tokens_infos[idx][0]]).decode( + "utf-8" + ) + in _gen_token.text + ): + _gen_token.latency_ms = gen_tokens_infos[idx][1] + _masked_top_k = gen_tokens_infos[idx][2] + + # if we find a match in tokens list generated by the engine, then this token should be marked as generated + _gen_token.is_generated = True + _gen_token.is_force_forwarded = False + + if _masked_top_k is None: + # in free accepting state, no masking + for _token in _gen_token.top_k: + _token.is_masked = False + else: + _masked_tokens = [token.token_id for token in _masked_top_k] + for _token in _gen_token.top_k: + if ( + _token.token_id not in _masked_tokens + and _token.token_id != _gen_token.token_id + ): + _token.is_masked = True + else: + _token.is_masked = False + + break + else: + # input tokens are not masked + for _token in _gen_token.top_k: + _token.is_masked = False + + for _gen_token in _gen_tokens: + if _gen_token.text != "": + processed_gen_tokens.append(_gen_token) + + start_idx = end_idx + 1 + + start_pos = 0 + remainder = "" + + if not self._variables: + return processed_gen_tokens + + def find_start_and_end_positions(chunk: str, start_idx: int, end_idx: int) -> tuple[int, int]: + s = "".join(gen_token.text for gen_token in processed_gen_tokens[start_idx:end_idx+1]) + if chunk not in s: + # chunk is not in the string + return (-1, -1) + + i = start_idx + (end_idx - start_idx) // 2 + traversed = [] + while True: + if i in traversed: + break + + s = "".join(gen_token.text for gen_token in processed_gen_tokens[i:end_idx+1]) + traversed.append(i) + + if chunk in s: + start_idx = i + # shift the start position to the right + i += (end_idx - i) // 2 + else: + # shift the start position to the left + i -= (i - start_idx) // 2 + + j = end_idx + traversed = [] + while True: + if j in traversed: + break + + s = "".join(gen_token.text for gen_token in processed_gen_tokens[start_idx:j+1]) + traversed.append(j) + + if chunk in s: + end_idx = j + # shift the end position to the left + j -= (j - start_idx) // 2 + else: + # shift the end position to the right + j += (end_idx - j) // 2 + + return (start_idx, end_idx) + + if self.engine.compute_log_probs: + for k in self._variables: + try: + chunk = self._variables[k] + chunk_start_pos = self._variables_positions[k] + # find the start of this capture in case we have multiple similar captures + _, start_idx = find_start_and_end_positions(self._state[:chunk_start_pos+1], 0, len(processed_gen_tokens)-1) + start_idx, end_idx = find_start_and_end_positions(chunk, start_idx, len(processed_gen_tokens)-1) + if start_idx == -1: + continue + + self._variables_log_probs[k] = [ + { + "token": token.text, + "token_id": token.token_id, + "logprob": np.log(token.prob).item() + } + for token in processed_gen_tokens[start_idx:end_idx+1] + ] + except: + pass + + return processed_gen_tokens + class ModelStream: def __init__(self, model, grammar=None, timeout=5): @@ -907,10 +1949,7 @@ def get_role_start(self, role_name, **kwargs): This kwargs are added to the role start as arguments. """ return ( - "<|im_start|>" - + role_name - + "".join([f' {k}="{v}"' for k, v in kwargs.items()]) - + "\n" + "<|im_start|>" + role_name + "".join([f' {k}="{v}"' for k, v in kwargs.items()]) + "\n" ) def get_role_end(self, role_name=None): @@ -951,21 +1990,206 @@ def grammar_only(): return GrammarOnly() -class ThrottleRefresh: - def __enter__(self): - Model._throttle_refresh += 1 - - def __exit__(self, exc_type, exc_value, traceback): - Model._throttle_refresh -= 1 - - -def throttle_refresh(): - """Returns a context manager that allows the print statement to drop display calls above the throttle rate.""" - return ThrottleRefresh() - - class ConstraintException(Exception): def __init__(self, *args, **kwargs): self.prompt = kwargs.pop("prompt", None) self.data = kwargs.pop("data", None) super().__init__(*args, **kwargs) + + +class MonitoringMetric(str, Enum): + CPU_USAGE = "cpu_usage" + MEM_USAGE = "mem_usage" + GPU_USAGE = "gpu_usage" + GPU_USED_MEM = "gpu_used_mem" + GPU_TOTAL_MEM = "gpu_total_mem" + INPUT_TOKENS = "input_tokens" + OUTPUT_TOKENS = "output_tokens" + BACKTRACK_TOKENS = "backtrack_tokens" + TOKEN_COUNT = "token_count" + TOKEN_REDUCTION = "token_reduction" + AVG_LATENCY = "avg_latency" + + +ALL_METRICS = [ + MonitoringMetric.CPU_USAGE, + MonitoringMetric.MEM_USAGE, + MonitoringMetric.GPU_USAGE, + MonitoringMetric.GPU_USED_MEM, + MonitoringMetric.GPU_TOTAL_MEM, + MonitoringMetric.INPUT_TOKENS, + MonitoringMetric.OUTPUT_TOKENS, + MonitoringMetric.BACKTRACK_TOKENS, + MonitoringMetric.TOKEN_COUNT, + MonitoringMetric.TOKEN_REDUCTION, + MonitoringMetric.AVG_LATENCY, +] + + +def _monitor_fn( + stop_flag, + metrics_dict: dict[MonitoringMetric, list], + max_size: int = 100, + interval_ms: float = 1000, +): + # print("Monitoring started") + + to_collect_gpu_stats = False + has_gpustat = False + try: + import gpustat + + has_gpustat = True + except: + logger.warning("gpustat is not installed, run `pip install gpustat` to collect GPU stats.") + + if has_gpustat: + try: + gpu_stats = gpustat.GPUStatCollection.new_query() + if len(gpu_stats) > 0: + # only collect GPU stats if there is at least one GPU + to_collect_gpu_stats = True + except: + logger.warning("Non-Nvidia GPU monitoring is not supported in this version.") + + try: + while not stop_flag.value: + t0 = time.time() + + # cpu_percent = psutil.cpu_percent(interval=1) + cpu_percent = psutil.cpu_percent() + memory_usage = psutil.virtual_memory() + + metrics_dict[MonitoringMetric.CPU_USAGE].append(cpu_percent) + metrics_dict[MonitoringMetric.MEM_USAGE].append(memory_usage.percent) + + if to_collect_gpu_stats: + gpu_stats = gpustat.GPUStatCollection.new_query() + + usage = [gpu.utilization for gpu in gpu_stats.gpus] + mem_usage = [gpu.memory_used for gpu in gpu_stats.gpus] + mem_total = [gpu.memory_total for gpu in gpu_stats.gpus] + + metrics_dict[MonitoringMetric.GPU_USAGE].append(usage) + metrics_dict[MonitoringMetric.GPU_USED_MEM].append(mem_usage) + metrics_dict[MonitoringMetric.GPU_TOTAL_MEM].append(mem_total) + + for metrics in metrics_dict.values(): + if len(metrics) > max_size: + metrics.pop(0) + + lat = time.time() - t0 + + # sleep for the remaining time of the interval + sleep_time = interval_ms / 1000.0 - lat + if sleep_time < 0: + time.sleep(sleep_time) + except Exception as e: + # print(f"Error in monitoring: {e}") + pass + + # print("Monitoring stopped") + + +class Monitor: + """Monitoring service to collect necessary metrics for visualization""" + + def __init__(self, engine_metrics: GuidanceEngineMetrics, **kwargs): + self.engine_metrics = engine_metrics + self.mp_manager = Manager() + + # use list instead of queue for easily accessing each item, e.g., last item + self.max_size = kwargs.get("max_size", 100) + + self.metrics_dict = { + MonitoringMetric.CPU_USAGE: self.mp_manager.list(), + MonitoringMetric.MEM_USAGE: self.mp_manager.list(), + MonitoringMetric.GPU_USAGE: self.mp_manager.list(), + MonitoringMetric.GPU_USED_MEM: self.mp_manager.list(), + MonitoringMetric.GPU_TOTAL_MEM: self.mp_manager.list(), + } + + self.stop_flag = self.mp_manager.Value("b", False) + self.process = None + + self.per_token_metrics = [] # store metrics per token in token list + + def start(self): + self.process = Process( + target=_monitor_fn, args=(self.stop_flag, self.metrics_dict, self.max_size) + ) + self.process.start() + logger.debug("Monitor:start") + + def stop(self): + if self.process: + self.stop_flag.value = True + self.process.terminate() + + for metrics in self.metrics_dict.values(): + metrics[:] = [] # NOTE(nopdive): ListProxy does not have .clear method. + logger.debug("Monitor:stop") + + def reset(self): + self.stop() + + for metrics in self.metrics_dict.values(): + metrics.clear() + + self.start() + logger.debug("Monitor:reset") + + def get_metrics( + self, metrics=None, lm: Union[Model, None] = None + ) -> dict[MonitoringMetric, Any]: + if metrics is None: + metrics = ALL_METRICS + result = {} + + for metric in metrics: + if metric in [ + MonitoringMetric.CPU_USAGE, + MonitoringMetric.MEM_USAGE, + MonitoringMetric.GPU_USAGE, + MonitoringMetric.GPU_USED_MEM, + MonitoringMetric.GPU_TOTAL_MEM, + ]: + result[metric] = ( + self.metrics_dict[metric][-1] if len(self.metrics_dict[metric]) > 0 else None + ) + elif metric == MonitoringMetric.INPUT_TOKENS: + result[metric] = self.engine_metrics.engine_input_tokens + elif metric == MonitoringMetric.OUTPUT_TOKENS: + result[metric] = self.engine_metrics.engine_output_tokens + elif metric == MonitoringMetric.BACKTRACK_TOKENS: + result[metric] = self.engine_metrics.engine_backtrack_tokens + elif metric == MonitoringMetric.TOKEN_COUNT: + result[metric] = lm.token_count if lm is not None else None + elif metric == MonitoringMetric.TOKEN_REDUCTION: + if lm is not None and lm.token_count > 0: + result[metric] = 1 - min(1, (lm.metrics.engine_output_tokens / lm.token_count)) + else: + result[metric] = None + elif metric == MonitoringMetric.AVG_LATENCY: + if lm is None: + result[metric] = None + else: + lats = [] + model = lm + while model._parent is not None: + if model.vis_chunk: + for token in model.vis_chunk.generated_tokens: + lats.append(token.latency_ms) + for token in model.vis_chunk.force_forwarded_tokens: + lats.append(token.latency_ms) + model = model._parent + + if len(lats) == 0: + result[metric] = None + else: + result[metric] = np.mean(lats) + + return result + + def get_metric(self, metric: MonitoringMetric, lm: Union[Model, None] = None) -> Any: + return self.get_metrics([metric], lm)[metric] diff --git a/guidance/models/_remote.py b/guidance/models/_remote.py index 86db8fcc5..973e8e7b6 100644 --- a/guidance/models/_remote.py +++ b/guidance/models/_remote.py @@ -29,7 +29,7 @@ def __init__(self, server_url, api_key, tokenizer=None, verify=None): # build the Engine super().__init__(tokenizer=tokenizer, compute_log_probs=False) - def __call__(self, parser, grammar, ensure_bos_token=True): + def __call__(self, parser, grammar, ensure_bos_token=True, echo: bool = False): # Prepare the request data data = { "parser": parser, diff --git a/guidance/models/llama_cpp/_llama_cpp.py b/guidance/models/llama_cpp/_llama_cpp.py index 3e0b7aa85..d243360f8 100644 --- a/guidance/models/llama_cpp/_llama_cpp.py +++ b/guidance/models/llama_cpp/_llama_cpp.py @@ -11,10 +11,12 @@ import numpy as np +from guidance._schema import GenToken, GenTokenExtra + from .._model import Engine, Model, Chat from .._remote import RemoteEngine from .._tokenizer import Tokenizer -from ..._utils import normalize_notebook_stdout_stderr +from ..._utils import normalize_notebook_stdout_stderr, softmax try: import llama_cpp @@ -100,7 +102,14 @@ def encode(self, byte_string: bytes) -> list[int]: class LlamaCppEngine(Engine): """The core class that runs inference using llama.cpp.""" - def __init__(self, model, compute_log_probs, chat_template=None, enable_backtrack=True, enable_ff_tokens=True, **kwargs): + def __init__(self, + model, + compute_log_probs, + chat_template=None, + enable_backtrack=True, + enable_ff_tokens=True, + enable_monitoring=True, + **kwargs): if not is_llama_cpp: raise Exception( "Please install llama-cpp-python with `pip install llama-cpp-python` in order to use guidance.models.LlamaCpp!" @@ -151,6 +160,8 @@ def __init__(self, model, compute_log_probs, chat_template=None, enable_backtrac compute_log_probs=compute_log_probs, enable_backtrack=enable_backtrack, enable_ff_tokens=enable_ff_tokens, + enable_monitoring=enable_monitoring, + **kwargs, ) self._n_vocab = len(self.tokenizer.tokens) @@ -219,6 +230,112 @@ def get_logits(self, token_ids): return logits + def get_per_token_topk_probs(self, token_ids: list[int], top_k: int = 5) -> list[GenTokenExtra]: + if len(token_ids) == 0: + return [] + + # clear kv-cache + llama_cpp.llama_kv_cache_seq_rm(self.model_obj.ctx, -1, 0, -1) + + # create new context + context = self._context + n_batch = self.model_obj.n_batch + batch = context.batch + + logits_batch = [] + for i in range(0, len(token_ids), n_batch): + n_tokens = min(i + n_batch, len(token_ids)) - i + batch.n_tokens = n_tokens + + for j in range(n_tokens): + batch.token[j] = token_ids[i + j] + batch.pos[j] = i + j + batch.seq_id[j][0] = 0 + batch.n_seq_id[j] = 1 + batch.logits[j] = True + + ret = llama_cpp.llama_decode(self.model_obj.ctx, batch) + if ret != 0: + raise Exception(f"Call to llama_cpp.llama_decode returned {ret}.") + + # get all the logits + if llama_cpp.__version__ < "0.2.58": + logits = llama_cpp.llama_get_logits(self.model_obj.ctx) + logits = logits.reshape((-1, self._n_vocab)) + logits_batch.extend(logits) + else: + for j in range(n_tokens): + logits = llama_cpp.llama_get_logits_ith(self.model_obj.ctx, j) + logits = np.ctypeslib.as_array(logits, shape=(self._n_vocab,)).copy() + logits_batch.append(logits) + + # process each token's logits + text_sequence = [] + + # add 1st token + _bytes = self.tokenizer.decode([token_ids[0]]) + try: + _text = _bytes.decode("utf-8") + except Exception as e: + _text = str(_bytes) + print(f"Failed to decode token: {token_ids[0]}, error: {e}, _bytes: {str(_bytes)}") + text_sequence.append( + GenTokenExtra( + token_id=token_ids[0], + prob=1.0, + text=_text, + top_k=[GenToken(token_id=token_ids[0], prob=1.0, text=_text)], + ) + ) + + for token_id, logits in zip(token_ids[1:], logits_batch[:-1]): + _probs = softmax(logits) + + # get the top k indices + top_k_ids, top_k_probs = self._get_top_k(_probs.copy(), top_k, ascending=False) + if token_id not in top_k_ids: + top_k_ids = np.append(top_k_ids, token_id) + top_k_probs = np.append(top_k_probs, _probs[token_id]) + + top_k_list = [] + for _token_id, _prob in zip(top_k_ids, top_k_probs): + _text = "" + try: + _text = self.tokenizer.decode([_token_id]).decode("utf-8") + except Exception as e: + _bytes = self.tokenizer.decode([_token_id]) + _text = str(_bytes) + print( + f"Failed to decode token: {_token_id}, error: {e}, _bytes: {str(_bytes)}" + ) + top_k_list.append(GenToken(token_id=_token_id, prob=_prob, text=_text)) + + text_sequence.append( + GenTokenExtra( + token_id=token_id, + prob=_probs[token_id], + text=self.tokenizer.decode([token_id]).decode("utf-8"), + top_k=top_k_list, + ) + ) + + return text_sequence + + def _get_top_k(self, probs: np.ndarray, k: int, axis: int = None, ascending: bool = True): + if not ascending: + probs *= -1 + ind = np.argpartition(probs, k, axis=axis) + ind = np.take(ind, np.arange(k), axis=axis) # k non-sorted indices + probs = np.take_along_axis(probs, ind, axis=axis) # k non-sorted values + + # sort within k elements + ind_part = np.argsort(probs, axis=axis) + ind = np.take_along_axis(ind, ind_part, axis=axis) + if not ascending: + probs *= -1 + val = np.take_along_axis(probs, ind_part, axis=axis) + return ind, val + class LlamaCpp(Model): def __init__( @@ -230,6 +347,7 @@ def __init__( chat_template=None, enable_backtrack=True, enable_ff_tokens=True, + enable_monitoring=True, **llama_cpp_kwargs, ): """Build a new LlamaCpp model object that represents a model in a given state.""" @@ -243,6 +361,7 @@ def __init__( chat_template=chat_template, enable_backtrack=enable_backtrack, enable_ff_tokens=enable_ff_tokens, + enable_monitoring=enable_monitoring, **llama_cpp_kwargs, ) diff --git a/guidance/models/transformers/_transformers.py b/guidance/models/transformers/_transformers.py index 3afd0be70..19b73a038 100644 --- a/guidance/models/transformers/_transformers.py +++ b/guidance/models/transformers/_transformers.py @@ -5,6 +5,8 @@ from typing import Sequence, Union +from guidance._schema import GenToken, GenTokenExtra + try: import torch except ModuleNotFoundError: @@ -35,12 +37,15 @@ "trust_remote_code", ] + class ByteDecoderError(Exception): pass + class ByteTokensError(Exception): pass + class TransformersTokenizer(Tokenizer): def __init__( self, @@ -104,10 +109,14 @@ def _tokenizer(self, model: str, **kwargs) -> tuple[ raise except ByteTokensError as e: # Give a specific warning for ByteTokensError and fall back to fast tokenizer - warnings.warn(f"Falling back to fast tokenizer. Could not build byte tokens for model {model!r} due to exception {e.__class__.__name__}: {e}") + warnings.warn( + f"Falling back to fast tokenizer. Could not build byte tokens for model {model!r} due to exception {e.__class__.__name__}: {e}" + ) except Exception as e: # Fall back for other exceptions - warnings.warn(f"Falling back to fast tokenizer. Could not load tokenizer for model {model!r} due to exception {e.__class__.__name__}: {e}") + warnings.warn( + f"Falling back to fast tokenizer. Could not load tokenizer for model {model!r} due to exception {e.__class__.__name__}: {e}" + ) else: return tokenizer, byte_tokens @@ -139,7 +148,9 @@ def _byte_tokens( ) pass else: - return self._byte_tokens_from_byte_decoder(transformers_tokenizer.byte_decoder, transformers_tokenizer) + return self._byte_tokens_from_byte_decoder( + transformers_tokenizer.byte_decoder, transformers_tokenizer + ) if hasattr(transformers_tokenizer, "sp_model"): return self._byte_tokens_from_sp_model(transformers_tokenizer) @@ -154,9 +165,7 @@ def _byte_tokens( fallback_byte_decoder = self._fallback_byte_decoder() try: - self._check_byte_decoder( - fallback_byte_decoder, transformers_tokenizer - ) + self._check_byte_decoder(fallback_byte_decoder, transformers_tokenizer) except ByteDecoderError as e: # Should be the only exception that is raised in _byte_tokens raise ByteTokensError( @@ -230,7 +239,9 @@ def _byte_tokens_by_encoding_token_strings( token_str = transformers_tokenizer.convert_tokens_to_string([token]) encoded_str = transformers_tokenizer.encode(token_str) if len(encoded_str) != 1: - raise ValueError(f"Round-trip encoding of tokens [{token}] failed! Got {encoded_str}") + raise ValueError( + f"Round-trip encoding of tokens [{token}] failed! Got {encoded_str}" + ) roundtrip_id = encoded_str[0] if roundtrip_id == i: byte_coded = token_str.encode() @@ -246,7 +257,7 @@ def _byte_tokens_by_encoding_token_strings( def _fallback_byte_decoder(self) -> dict[str, int]: byte_decoder = transformers_package.AutoTokenizer.from_pretrained( "gpt2", use_fast=False - ).byte_decoder # fall back to gpt2 mapping + ).byte_decoder # fall back to gpt2 mapping # some special tokens may not have their whitespace encoded... byte_decoder[" "] = 32 @@ -293,8 +304,10 @@ def check_byte_decoder_complex_round_trip() -> None: # if it's at the start of the reconstructed bytes # Some tokenizers add this automatically as part of the call function, so # we need to remove it to compare - if hasattr(transformers_tokenizer, "bos_token") and transformers_tokenizer.bos_token and reconstructed.startswith( - transformers_tokenizer.bos_token.encode() + if ( + hasattr(transformers_tokenizer, "bos_token") + and transformers_tokenizer.bos_token + and reconstructed.startswith(transformers_tokenizer.bos_token.encode()) ): reconstructed = reconstructed[len(transformers_tokenizer.bos_token) :] # TODO: can we narrow this exception? @@ -371,7 +384,15 @@ def recode(self, tokens: Sequence[int]) -> list[int]: class TransformersEngine(Engine): - def __init__(self, model, tokenizer, compute_log_probs: bool, chat_template=None, enable_backtrack=True, enable_ff_tokens=True, **kwargs): + def __init__(self, + model, + tokenizer, + compute_log_probs: bool, + chat_template=None, + enable_backtrack=True, + enable_ff_tokens=True, + enable_monitoring=True, + **kwargs): # fill in default model value if model is None: model = os.environ.get("TRANSFORMERS_MODEL", None) @@ -414,7 +435,9 @@ def __init__(self, model, tokenizer, compute_log_probs: bool, chat_template=None my_tokenizer, compute_log_probs=compute_log_probs, enable_backtrack=enable_backtrack, - enable_ff_tokens=enable_ff_tokens + enable_ff_tokens=enable_ff_tokens, + enable_monitoring=enable_monitoring, + **kwargs, ) def _model(self, model, **kwargs): @@ -517,6 +540,62 @@ def get_logits(self, token_ids): return self._cached_logits + def get_per_token_topk_probs(self, token_ids: list[int], top_k: int = 5) -> list[GenTokenExtra]: + tokenizer = self.tokenizer._orig_tokenizer + + # NOTE (loc) - assume batch size of 1 + input_ids = torch.tensor(token_ids).unsqueeze(0).long().to(self.device) + + outputs = self.model_obj(input_ids) + probs = torch.softmax(outputs.logits, dim=-1).detach() + + # append "1" to probs to account for the 1st token in the input_ids + probs = torch.cat([torch.ones_like(probs[:, :1, :]), probs], dim=1) + + # collect the probability of the generated token + probs = probs[:, :-1, :] + + batch = [] + for input_sentence, input_probs in zip(input_ids, probs): + text_sequence = [] + + for _token_id, _probs in zip(input_sentence, input_probs): + _token = tokenizer.decode(_token_id) + + if len(text_sequence) == 0: + token = GenTokenExtra( + token_id=_token_id.item(), + prob=1.0, + text=tokenizer.decode([_token_id]), + top_k=[ + GenToken(token_id=_token_id.item(), prob=1.0, text=_token), + ], + ) + text_sequence.append(token) + continue + + # get the top k indices + top_k_indices = torch.topk(_probs, top_k).indices.tolist() + if _token_id not in top_k_indices: + top_k_indices.append(_token_id.item()) + + top_k_probs = [_probs[i].item() for i in top_k_indices] + top_k_list = [] + for t, p in zip(top_k_indices, top_k_probs): + top_k_list.append(GenToken(token_id=t, prob=p, text=tokenizer.decode([t]))) + + token = GenTokenExtra( + token_id=_token_id.item(), + prob=_probs[_token_id].item(), + text=_token, + top_k=top_k_list, + ) + text_sequence.append(token) + + batch.append(text_sequence) + + return batch[0] + class Transformers(Model): def __init__( @@ -528,6 +607,7 @@ def __init__( chat_template=None, enable_backtrack=True, enable_ff_tokens=True, + enable_monitoring=True, **kwargs, ): """Build a new Transformers model object that represents a model in a given state.""" @@ -539,6 +619,7 @@ def __init__( chat_template=chat_template, enable_backtrack=enable_backtrack, enable_ff_tokens=enable_ff_tokens, + enable_monitoring=enable_monitoring, **kwargs, ), echo=echo, diff --git a/guidance/resources/graphpaper-inline.html b/guidance/resources/graphpaper-inline.html new file mode 100644 index 000000000..4d108f32c --- /dev/null +++ b/guidance/resources/graphpaper-inline.html @@ -0,0 +1,16 @@ + + + + + + + + + + + + + diff --git a/guidance/trace/__init__.py b/guidance/trace/__init__.py new file mode 100644 index 000000000..f70829da7 --- /dev/null +++ b/guidance/trace/__init__.py @@ -0,0 +1,11 @@ +"""Trace tree of inputs & outputs generated from a guidance program. + +The first implementation aims for simplicity. +Once benchmark figures are out, we'll figure out what to optimize. + +The most critical class is the trace handler. See its documentation for trace design & motivations. +""" + +from ._trace import NodeAttr, InputAttr, OutputAttr, StatefulGuidanceInput, StatelessGuidanceInput +from ._trace import LiteralInput, EmbeddedInput, ImageInput, RoleCloserInput, RoleOpenerInput +from ._trace import TextOutput, ImageOutput, CaptureOutput, TraceNode, TraceHandler diff --git a/guidance/trace/_trace.py b/guidance/trace/_trace.py new file mode 100644 index 000000000..3bfea4bd0 --- /dev/null +++ b/guidance/trace/_trace.py @@ -0,0 +1,339 @@ +# TODO(nopdive): Consider integrating token operations into trace nodes (handles token healing cleaner). +# TODO(nopdive): Benchmark (expected heap fragmentation issue). Likely need memory pooling (via rust/ctypes/Cython). +# TODO(nopdive): Integrate images when PR for multimodal is in. +import weakref +from itertools import count +from typing import Any, Optional, Generator, Dict +import logging +from pydantic import BaseModel, Field +from guidance._schema import GenToken +from .._utils import pydantic_no_default_repr, pydantic_no_default_str, log_cleanup + +logger = logging.getLogger(__name__) + + +class NodeAttr(BaseModel): + """Attributes of a trace node.""" + + class_name: str = "" + + def __init__(self, **kwargs): + kwargs["class_name"] = self.__class__.__name__ + super().__init__(**kwargs) + + def __repr__(self): + return pydantic_no_default_repr(self) + + def __str__(self): + return pydantic_no_default_str(self) + + +class InputAttr(NodeAttr): + """Input for a guidance program (i.e. literal or guidance grammar).""" + + pass + + +class OutputAttr(NodeAttr): + """Output for a guidance program (i.e. text output).""" + + pass + + +class StatelessGuidanceInput(InputAttr): + """Stateless guidance input (light wrapper).""" + + # NOTE(nopdive): Open to debate what we should serialize here, excluding for now. + value: Any = Field(exclude=True) + + def __repr__(self): + return f"{self.__class__.__name__}({self.value})" + + +class StatefulGuidanceInput(InputAttr): + """Stateful guidance input (light wrapper).""" + + # NOTE(nopdive): Open to debate what we should serialize here, excluding for now. + value: Any = Field(exclude=True) + + def __repr__(self): + return f"{self.__class__.__name__}({self.value})" + + +class LiteralInput(InputAttr): + """Text string as a literal.""" + + value: str + + +# NOTE(nopdive): Placeholder, needs to be filled once multimodal PR is in. +class ImageInput(InputAttr): + """Image input.""" + + value: bytes + + +class EmbeddedInput(InputAttr): + """Text string with embedded guidance input.""" + + value: str + + +class RoleOpenerInput(InputAttr): + """Appears when a role is opened (i.e. user / system). + + This usually occurs as a role context and __enter__ is called. + """ + + name: Optional[str] = None + text: Optional[str] = None + closer_text: Optional[str] = None + + +class RoleCloserInput(InputAttr): + """Appears when a role is closed (i.e. user / system). + + This usually occurs as a role context and __exit__ is called. + """ + + name: Optional[str] = None + text: Optional[str] = None + + +class TextOutput(OutputAttr): + """Text string.""" + + value: str + is_input: bool = False + is_generated: bool = False + is_force_forwarded: bool = False + token_count: int = 0 + prob: float = 0.0 + tokens: list[GenToken] = Field( + exclude=True, default_factory=list + ) # use to store tokens associated with output + + def __str__(self): + return self.value + + +# NOTE(nopdive): Placeholder, needs to be filled once multimodal PR is in. +class ImageOutput(OutputAttr): + """Image as bytes.""" + + value: bytes + + +class CaptureOutput(OutputAttr): + """Capture variable output as a string. + + If `value` is set to None, this means it's a reset (needed for append capture group outputs). + """ + + name: str + value: Optional[str] = None + is_append: bool = False + log_probs: float = 0.0 + + def __str__(self): + return f"{self.name}{'+=' if self.is_append else '='}{self.value.__str__()}" + + +class WeakRefList(list): + """Weak reference list implementation that uses weakref ref objects. + + This does not override all methods for list. + """ + + def append(self, item): + super().append(weakref.ref(item)) + + def __getitem__(self, index): + ref = super().__getitem__(index) + obj = ref() + if obj is None: + raise ReferenceError("The referenced object has been garbage collected") + return obj + + def __iter__(self): + return (ref() for ref in super().__iter__() if ref() is not None) + + def remove(self, item): + ref_to_remove = None + for ref in super().__iter__(): + obj = ref() + if obj is item: + ref_to_remove = ref + break + if ref_to_remove: + super().remove(ref_to_remove) + + +def _cleanup(log_msg: str): + log_cleanup(log_msg) + + +class TraceNode(BaseModel): + """Trace node which associates inputs and outputs of a guidance program.""" + + identifier: int = Field(default_factory=count().__next__) + parent: Optional["TraceNode"] = None + children: list["TraceNode"] = Field(default_factory=WeakRefList) + input: Optional[InputAttr] = None + output: Optional[OutputAttr] = None + + def __init__(self, **kwargs): + super().__init__(**kwargs) + weakref.finalize(self, _cleanup, f"trace({id(self)}):{self.identifier}") + + def add_child(self, child: "TraceNode") -> None: + """Add a child node to the trace node. + + Args: + child: The child node to add. + """ + child.parent = self + self.children.append(child) + + def remove_child(self, child: "TraceNode") -> None: + """Remove a child node from the trace node. + + Args: + child: The child node to remove. + """ + child.parent = None + self.children.remove(child) + + def ancestors(self) -> Generator["TraceNode", None, None]: + """Yields ancestor trace nodes ending with root. + + Yields: + Ancestor trace nodes. + """ + node = self + while node.parent is not None: + yield node.parent + node = node.parent + + def path(self) -> Generator["TraceNode", None, None]: + """Yields path of from root to self inclusively. + + Yields: + Trace nodes from root to self. + """ + yield from reversed(list(self.ancestors())) + yield self + + def root(self) -> "TraceNode": + """Returns root by traversing parents of self. + + Returns: + Root of tree self is part of. + """ + if self.parent is None: + root = self + else: + *_, root = self.ancestors() + return root + + def traverse(self, bfs: bool = True): + """Traverse the trace nodes starting with self. + + Args: + bfs: Use breadth-first-search, otherwise depth-first-search. + + Yields: + Trace nodes in traversal order. + """ + queue = [self] + while queue: + node = queue.pop(0) + yield node + + if bfs: + queue.extend(node.children) + else: + # NOTE(nopdive): Analogous to extend but at front of list. + queue[0:0] = node.children + + def __repr__(self): + return f"{self.identifier}:{self.input!r}:{self.output!r}" + + def __hash__(self): + return hash(self.identifier) + + +class TraceHandler(BaseModel): + """Trace handler that will own a tree of trace nodes. + + This will primarily be owned by a model's engine. + Each guidance model corresponds to one trace node. + All guidance models emitted from an engine is to be included as their own paths within the tree. + + The requirement for holding all live traces ensures downstream consumers such as UI providers + can do near-real-time partial updates. + """ + + # NOTE(nopdive): Type trickery for pydantic. + id_node_map: Dict[int, TraceNode] = weakref.WeakValueDictionary() # type: ignore + node_id_map: Dict[TraceNode, int] = weakref.WeakKeyDictionary() # type: ignore + + def __init__(self, **kwargs): + super().__init__(**kwargs) + weakref.finalize(self, _cleanup, f"tracehandler({id(self)})") + + def __getitem__(self, item): + return self.id_node_map[item] + + def update_node( + self, identifier: int, parent_id: Optional[int], node_attr: Optional[NodeAttr] = None + ) -> TraceNode: + """Update the trace node with the given identifier. + + If the trace node does not exist, it will be created. + Both parent id and node attributes can be updated only once until further notice. + + Args: + identifier: User-defined identifier for the trace node. + parent_id: User-defined parent identifier for the trace node. + node_attr: Input or output node attribute to be updated on the trace node. + """ + node = self.id_node_map.get(identifier, None) + if node is None: + node = TraceNode() + + self.id_node_map[identifier] = node + self.node_id_map[node] = identifier + + if parent_id is not None: + parent_node = self.id_node_map.get(parent_id, None) + if parent_node is not None: + parent_node.add_child(node) + + if node_attr is not None: + if isinstance(node_attr, InputAttr): + assert node.input is None + node.input = node_attr + elif isinstance(node_attr, OutputAttr): + assert node.output is None + node.output = node_attr + else: + raise ValueError(f"Unexpected node attr: {node_attr}") + return node + + def root(self) -> TraceNode: + """Returns root node of trace handler. + + Raises: + Exception: If root cannot be found. + + Returns: Root trace node. + """ + root = None + for _, node in self.id_node_map.items(): + if node.parent is None: + root = node + break + if root is None: + raise Exception("No root in trace handler.") + return root diff --git a/guidance/visual/__init__.py b/guidance/visual/__init__.py index a286a2509..1cbcbefae 100644 --- a/guidance/visual/__init__.py +++ b/guidance/visual/__init__.py @@ -3,4 +3,9 @@ Users should have few reasons to be accessing this module. """ -from ._environment import Environment \ No newline at end of file +from ._message import GuidanceMessage, TraceMessage, ResetDisplayMessage, ClientReadyMessage, ClientReadyAckMessage +from ._message import ExecutionCompletedMessage, TokensMessage, MetricMessage, OutputRequestMessage +from ._message import ExecutionStartedMessage +from ._renderer import AutoRenderer, LegacyHtmlRenderer, JupyterWidgetRenderer, Renderer, legacy_mode +from ._message import serialize_message, deserialize_message +from ._trace import trace_node_to_str, display_trace_tree, trace_node_to_html \ No newline at end of file diff --git a/guidance/visual/_async.py b/guidance/visual/_async.py new file mode 100644 index 000000000..2ddc69d7a --- /dev/null +++ b/guidance/visual/_async.py @@ -0,0 +1,84 @@ +""" Asynchronous handling for visual components. + +This includes a separate thread dedicated for visualization and messaging. +""" +# NOTE(nopdive): This is run on a single global thread. +# Consider per engine threads later after memory review on engine. + +import asyncio +import threading +from asyncio import AbstractEventLoop, Future, Task +from typing import Tuple, Coroutine + + +def _start_asyncio_loop(loop: AbstractEventLoop): + asyncio.set_event_loop(loop) + loop.run_forever() + + +def _asyncio_background_thread() -> Tuple[threading.Thread, AbstractEventLoop]: + loop = asyncio.new_event_loop() + thread = threading.Thread(target=_start_asyncio_loop, args=(loop,)) + thread.daemon = True + return thread, loop + +def _run_thread_if_needed(): + global _loop + global _thread + + if _loop is None: + _thread, _loop = _asyncio_background_thread() + _thread.start() + return _thread, _loop + + +async def async_task(coroutine: Coroutine) -> Task: + """ Creates an asyncio task from coroutine. + + Args: + coroutine: Coroutine within task. + + Returns: + Asyncio task. + """ + task = asyncio.create_task(coroutine) + return task + + +async def print_all_tasks(): # pragma: no cover + """Prints all tasks running in visual thread loop.""" + for task in asyncio.all_tasks(): + print(task) + + +def async_loop() -> AbstractEventLoop: + """ Returns async loop in visual thread. + + Returns: + Async loop of visual thread. + """ + _, loop = _run_thread_if_needed() + return loop + + +# TODO: Test. +def call_soon_threadsafe(cb, *args, context = None): + loop = async_loop() + return loop.call_soon_threadsafe(cb, *args, context=context) + + +def run_async_coroutine(coroutine: Coroutine) -> Future: + """ Runs an asynchronous coroutine in the visual thread. + + Args: + coroutine: Coroutine to be run on visual thread. + + Returns: + Future of coroutine. + """ + _, loop = _run_thread_if_needed() + future = asyncio.run_coroutine_threadsafe(coroutine, loop) + return future + +_loop = None +_thread = None \ No newline at end of file diff --git a/guidance/visual/_jupyter.py b/guidance/visual/_jupyter.py new file mode 100644 index 000000000..01dc70230 --- /dev/null +++ b/guidance/visual/_jupyter.py @@ -0,0 +1,30 @@ +""" Jupyter specific utilities.""" + + +import weakref +from typing import Callable, Any, Tuple +import logging +try: + from IPython import get_ipython +except ImportError: + pass + +logger = logging.getLogger(__name__) + +IPythonCallback = Callable[[Any], None] + +# TODO(nopdive): Connect cell handling again to renderer. + + +def ipy_handle_event_once(cb: IPythonCallback, event_name: str) -> IPythonCallback: + ipy = get_ipython() + + if ipy is None: + return None + + def cb_closure(msg): + cb(info=msg) + ipy.events.unregister(event_name, cb_closure) + ipy.events.register(event_name, cb_closure) + + return cb_closure \ No newline at end of file diff --git a/guidance/visual/_message.py b/guidance/visual/_message.py new file mode 100644 index 000000000..8c0298239 --- /dev/null +++ b/guidance/visual/_message.py @@ -0,0 +1,129 @@ +"""Messages that used between server (usually Jupyter Python kernel) and client. + +Messages are required to be added to the model registry for serialization. +""" +from typing import Optional, Dict, Union + +from pydantic import BaseModel, Field + +from guidance._schema import GenTokenExtra +from ..trace import NodeAttr +import json + + +_msg_counter: int = -1 +class GuidanceMessage(BaseModel): + """Message sent within Guidance layer.""" + + message_id: int = Field(default=None) + class_name: str = "" + + def __init__(self, **kwargs): + global _msg_counter + + kwargs["class_name"] = self.__class__.__name__ + if kwargs.get("message_id") is None: + _msg_counter += 1 + kwargs["message_id"] = _msg_counter + super().__init__(**kwargs) + + +class TraceMessage(GuidanceMessage): + """Update on a trace node.""" + trace_id: int + parent_trace_id: Optional[int] = None + node_attr: Optional[NodeAttr] = None + + +class MetricMessage(GuidanceMessage): + """Metric that has been emitted.""" + name: str + value: Union[float, str, list[float], list[str]] = Field(union_mode='left_to_right') + scalar: bool = True + + +class ExecutionStartedMessage(GuidanceMessage): + """Fired when renderer has started trace messages.""" + + +class ExecutionCompletedMessage(GuidanceMessage): + """Fired when renderer has completed trace messages. + + This functions as the last message sent to client. + """ + last_trace_id: Optional[int] = None + is_err: bool = False + + +class TokensMessage(GuidanceMessage): + """Fired when trace messages are completed, with tokens for client.""" + trace_id: int + text: str + tokens: list[GenTokenExtra] + + def __str__(self): + return f"message_id={self.message_id} class_name={self.class_name} trace_id={self.trace_id}" + + +class ResetDisplayMessage(GuidanceMessage): + """Instructs client to reset the display, removing all output.""" + pass + + +class ClientReadyMessage(GuidanceMessage): + """Fired when client is ready to receive messages.""" + pass + + +class ClientReadyAckMessage(GuidanceMessage): + """Fired when server acknowledges client readiness.""" + pass + + +class OutputRequestMessage(GuidanceMessage): + """Fired when client requests tokens from server.""" + pass + + +model_registry: Dict[str, type(GuidanceMessage)] = { + 'TraceMessage': TraceMessage, + 'ExecutionStartedMessage': ExecutionStartedMessage, + 'ExecutionCompletedMessage': ExecutionCompletedMessage, + 'ExecutionCompletedOutputMessage': TokensMessage, + 'ResetDisplayMessage': ResetDisplayMessage, + 'ClientReadyMessage': ClientReadyMessage, + 'ClientReadyAckMessage': ClientReadyAckMessage, + 'OutputRequestMessage': OutputRequestMessage, + 'MetricMessage': MetricMessage, + 'TokensMessage': TokensMessage, +} + + +def serialize_message(message: GuidanceMessage) -> str: + """ Serializes guidance message. + + Args: + message: Message to be serialized. + + Returns: + Serialized message in JSON format. + """ + message_json = message.model_dump_json(indent=2, serialize_as_any=True) + return message_json + + +def deserialize_message(data: str) -> GuidanceMessage: + """ Deserializes string into a guidance message. + + Args: + data: JSON string to be deserialized. + + Returns: + Guidance message. + """ + data_json = json.loads(data) + class_name = data_json.get("class_name") + model_class = model_registry.get(class_name) + if not model_class: + raise ValueError(f"Unknown class_name: {class_name}") + return model_class.model_validate_json(data) \ No newline at end of file diff --git a/guidance/visual/_renderer.py b/guidance/visual/_renderer.py new file mode 100644 index 000000000..3dee7ec1a --- /dev/null +++ b/guidance/visual/_renderer.py @@ -0,0 +1,497 @@ +""" Renderer is responsible for displaying output. + +Our main focus is on jupyter notebooks and later terminal. +""" +# TODO(nopdive): Implementation for terminals & append-only text displays. +# NOTE(nopdive): Testing this notebook related components is tricky. Should figure this out at some point. + +import asyncio +import logging +import weakref +from typing import Optional, Callable, Tuple, Any +from asyncio import Queue +from functools import partial + +from . import MetricMessage, TokensMessage +from ._environment import Environment +from ._jupyter import ipy_handle_event_once +from ._message import ExecutionCompletedMessage, \ + deserialize_message, serialize_message, ClientReadyAckMessage, ExecutionStartedMessage +from .._utils import log_cleanup +from ..trace import TraceHandler +from ..visual import GuidanceMessage, TraceMessage, ResetDisplayMessage, ClientReadyMessage +from ._trace import trace_node_to_html +from ._async import run_async_coroutine, async_task, call_soon_threadsafe +from warnings import warn + +try: + from IPython.display import clear_output, display, HTML + from IPython import get_ipython + + ipython_imported = True +except ImportError: + ipython_imported = False + + +try: + import stitch + + stitch_installed = True +except ImportError: + stitch_installed = False + +logger = logging.getLogger(__name__) + + +# NOTE(nopdive): Temporary solution until we iron out all issues with widgets. +_legacy_mode = False # pragma: no cover +def legacy_mode(flag: Optional[bool] = None) -> bool: # pragma: no cover + """ Sets visualizations to legacy mode (how Guidance used to be). + + This method call will likely deprecate when a fresher API for + renderers is defined. + + Args: + flag: True if legacy is wanted. + + Returns: + Whether legacy mode is enabled. + """ + global _legacy_mode + if flag is not None: + _legacy_mode = flag + return _legacy_mode + + +class Renderer: + """Renders guidance model to a visual medium.""" + + def __init__(self): + """Initializes. """ + self._observers = [] + + def notify(self, message: GuidanceMessage): + """ Notifies all observers of the renderer of an incoming message. + + Args: + message: Incoming message. + """ + for observer in self._observers: + observer(message) + + def subscribe(self, callback: Callable[[GuidanceMessage], None]) -> None: + """ Subscribes to incoming messages. + + Args: + callback: Callback to handle incoming messages. + """ + self._observers.append(callback) + + def unsubscribe(self, callback: Callable[[GuidanceMessage], None]) -> None: + """ Unsubscribes from incoming messages. + + Args: + callback: Callback to remove. + """ + self._observers.remove(callback) + + def update(self, message: GuidanceMessage) -> None: + """ Updates renderer with incoming message. + + Args: + message: Incoming message. + """ + raise NotImplementedError("Update not implemented.") + + +def _create_stitch_widget(): + from stitch import StitchWidget + import pkg_resources + + if _create_stitch_widget.src_doc_template is None: + with open( + pkg_resources.resource_filename("guidance", "resources/graphpaper-inline.html"), "r" + ) as f: + _create_stitch_widget.src_doc_template = f.read() + w = StitchWidget() + w.initial_width = "100%" + w.initial_height = "auto" + w.srcdoc = _create_stitch_widget.src_doc_template + weakref.finalize(w, log_cleanup, f"stitch({id(w)})") + + return w + + +_create_stitch_widget.src_doc_template = None + + +def _cleanup(recv_queue: Optional[Queue], send_queue: Optional[Queue], log_msg: str) -> None: + log_cleanup(log_msg) + if recv_queue is not None: + call_soon_threadsafe(send_queue.put_nowait, None) + if send_queue is not None: + call_soon_threadsafe(recv_queue.put_nowait, None) + + +async def _create_queue() -> Queue: + # This will run in the visual thread. + return Queue() + + +def _on_stitch_clientmsg(recv_queue_weakref: weakref.ReferenceType["Queue"], change: dict) -> None: + # NOTE(nopdive): Widget callbacks do not print to stdout/stderr nor module log. + recv_queue = recv_queue_weakref() + if recv_queue is not None: + call_soon_threadsafe(recv_queue.put_nowait, change['new']) + + +def _on_cell_completion(renderer_weakref: weakref.ReferenceType["Renderer"], info) -> None: + logger.debug(f"CELL:executed") + try: + renderer = renderer_weakref() + if renderer is None: + return + + message = ExecutionCompletedMessage( + last_trace_id=renderer._last_trace_id, + is_err=info.error_in_exec is not None, + ) + renderer.update(message) + except Exception as e: + logger.error(repr(e)) + + +async def _handle_recv_messages(renderer_weakref: weakref.ReferenceType["Renderer"], queue_weakref: weakref.ReferenceType["Queue"]) -> None: + logger.debug("RECV:init") + + while True: + try: + queue = queue_weakref() + if queue is None: + break + value = await queue.get() + + # logger.debug(f"RECV:raw:{value}") + + if value is None: + logger.debug("RECV:closing") + break + + message = deserialize_message(value) + # logger.debug(f"RECV:msg:{message}") + + renderer = renderer_weakref() + if renderer is None: + logger.debug("RECV:renderer early clean") + break + + if isinstance(message, ClientReadyMessage): + logger.debug("RECV:clientready") + call_soon_threadsafe(renderer._send_queue.put_nowait, ClientReadyAckMessage()) + + renderer.notify(message) + renderer._recv_queue.task_done() + except Exception as e: + logger.error(f"RECV:err:{repr(e)}") + + +async def _handle_send_messages(renderer_weakref: weakref.ReferenceType["Renderer"], queue_weakref: weakref.ReferenceType["Queue"]) -> None: + logger.debug("SEND:init") + # NOTE(nopdive): Waiting on client cb does not work, client messages received on cell completion. + # Currently, we do a replay of messages on completion for client if client + # first receives non-zero message identifier. + + # What if we only used 1% of our brain? + await asyncio.sleep(200 / 1000.) + logger.debug("SEND:ready") + + while True: + try: + queue = queue_weakref() + if queue is None: + break + message = await queue.get() + # logger.debug(f"SEND:msg:{message}") + + if message is None: + logger.debug("SEND:closing") + break + + message_json = serialize_message(message) + # logger.debug(f"SEND:json:{message_json}") + + renderer = renderer_weakref() + if renderer is None: + break + if renderer._stitch_widget is not None: + renderer._stitch_widget.kernelmsg = message_json + else: + logger.debug(f"SEND:jupyter:send but no widget") + renderer._send_queue.task_done() + except Exception as e: + logger.error(f"SEND:err:{repr(e)}") + + +class JupyterWidgetRenderer(Renderer): + """Jupyter widget renderer that is implemented via stitch package.""" + + def __init__(self, trace_handler: TraceHandler) -> None: + """ Initializes. + + Args: + trace_handler: Trace handler of an engine. + """ + super().__init__() + + self._trace_handler = trace_handler + self._messages: list[GuidanceMessage] = [] + self._completed = False + self._running = False + self._need_reset = False + self._stitch_widget = None + self._stitch_on_clientmsg = None + self._cell_event_handler = None + self._last_trace_id = None + + # Create queue and wait for instantiation + self._send_queue: Queue = run_async_coroutine(_create_queue()).result() + self._recv_queue: Queue = run_async_coroutine(_create_queue()).result() + + # Start send/recv message loops + recv_coroutine = _handle_recv_messages(weakref.ref(self), weakref.ref(self._recv_queue)) + send_coroutine = _handle_send_messages(weakref.ref(self), weakref.ref(self._send_queue)) + self._recv_task = run_async_coroutine(async_task(recv_coroutine)).result() + self._send_task = run_async_coroutine(async_task(send_coroutine)).result() + + weakref.finalize(self, _cleanup, self._recv_queue, self._send_queue, f"renderer({id(self)})") + + + def has_divergence(self, message: GuidanceMessage) -> Tuple[bool, int]: + """Checks if message has divergence with current path. + + Args: + message: Incoming message. + + Returns: + Tuple of (has diverged, shared ancestor index). Index will be -1 if no divergence. + + Raises: + Exception if there is no shared ancestor (including root). This should not happen. + """ + if not isinstance(message, TraceMessage): + return False, -1 + + # If we diverge from the model path, truncate and reset + message_trace_node = self._trace_handler[message.trace_id] + + prev_trace_messages = [x for x in self._messages if isinstance(x, TraceMessage)] + trace_messages_len = len(prev_trace_messages) + if trace_messages_len == 0: + return False, -1 + elif trace_messages_len == 1: + if isinstance(self._messages[0], TraceMessage): + last_trace_node = self._trace_handler[self._messages[0].trace_id] + if message_trace_node.parent == last_trace_node: + return False, -1 + else: + return True, 0 + else: + return False, -1 + else: + last_trace_message = prev_trace_messages[-1] + last_trace_node = self._trace_handler[last_trace_message.trace_id] + + if last_trace_node not in message_trace_node.path(): + logger.debug(f"DIVERGENCE:curr:{message_trace_node}") + logger.debug(f"DIVERGENCE:prev:{last_trace_node}") + + # Truncate path that is no longer used by current trace node + ancestor_idx = -1 + ancestors = set(message_trace_node.ancestors()) + for idx, prev_message in enumerate(self._messages): + if isinstance(prev_message, TraceMessage): + prev_trace_node = self._trace_handler[prev_message.trace_id] + if prev_trace_node in ancestors: + ancestor_idx = idx + + if ancestor_idx == -1: + if message_trace_node.parent == last_trace_node.root(): # pragma: no cover + ancestor_idx = 0 + else: + raise Exception(f"Parent missing for trace node: {message_trace_node}") + + return True, ancestor_idx + else: + return False, -1 + + + def update(self, message: GuidanceMessage) -> None: + out_messages = [] + + if isinstance(message, ExecutionCompletedMessage): + logger.debug("RENDERER:execution end") + self._completed = True + self._running = False + self.notify(message) + + if message.is_err: + out_messages.append(MetricMessage(name="status", value="⚠")) + else: + out_messages.append(MetricMessage(name="status", value="✓")) + + if not self._running and isinstance(message, TraceMessage): + logger.debug("RENDERER:execution start") + started_msg = ExecutionStartedMessage() + out_messages.append(started_msg) + out_messages.append(MetricMessage(name="status", value='⟳')) + + ipy_handle_event_once( + partial(_on_cell_completion, weakref.ref(self)), + 'post_run_cell' + ) + self._need_reset = True + self._running = True + self._completed = False + + # TODO(nopdive): Fire off execution immediately to renderer subscribers. Review later. + call_soon_threadsafe(self._recv_queue.put_nowait, serialize_message(started_msg)) + + # Check if message has diverged from prev messages + diverged, shared_ancestor_idx = self.has_divergence(message) + if diverged: + out_messages.append(ResetDisplayMessage()) + out_messages[len(out_messages):] = self._messages[:shared_ancestor_idx] + self._messages.clear() + + # Reset if needed + if self._need_reset: + logger.debug("RENDERER:reset") + + # Clear messages + self._messages = [] + + if self._stitch_widget is not None: + self._stitch_widget.unobserve(self._stitch_on_clientmsg, names='clientmsg') + + self._stitch_widget = _create_stitch_widget() + self._stitch_on_clientmsg = partial(_on_stitch_clientmsg, weakref.ref(self._recv_queue)) + self._stitch_widget.observe(self._stitch_on_clientmsg, names='clientmsg') + + # Redraw + # clear_output(wait=True) + display(self._stitch_widget) + + self._need_reset = False + + # Append current message to outgoing + out_messages.append(message) + + # Send outgoing messages to client + for out_message in out_messages: + # logger.debug(f"RENDERER:out:{out_message}") + if isinstance(out_message, TraceMessage): + self._last_trace_id = out_message.trace_id + + self._messages.append(out_message) + call_soon_threadsafe(self._send_queue.put_nowait, out_message) + +class DoNothingRenderer(Renderer): + """ It does nothing. Placeholder for future renderers.""" + + def __init__(self, trace_handler: TraceHandler) -> None: + """ Initializes. + + Args: + trace_handler: Trace handler of an engine. + """ + self._trace_handler = trace_handler + super().__init__() + + def update(self, message: GuidanceMessage) -> None: + pass + + +class LegacyHtmlRenderer(JupyterWidgetRenderer): + """Original HTML renderer for guidance.""" + + def __init__(self, trace_handler: TraceHandler) -> None: + """ Initializes. + + Args: + trace_handler: Trace handler of an engine. + """ + + self._trace_handler = trace_handler + super().__init__(trace_handler) + + def update(self, message: GuidanceMessage) -> None: + pass + # # Handle Jupyter cell completion + # self._handle_jupyter_cell_completion() + # + # if isinstance(message, TraceMessage) or isinstance(message, ExecutionCompletedOutputMessage): + # complete_msg = None + # if isinstance(message, ExecutionCompletedOutputMessage): + # complete_msg = message + # + # trace_node = self._trace_handler[message.trace_id] + # self._last_trace_id = message.trace_id + # if trace_node is not None: + # clear_output(wait=True) + # display(HTML(trace_node_to_html(trace_node, prettify_roles=False, complete_msg=complete_msg))) + # elif isinstance(message, ExecutionCompletedMessage): + # logger.debug("RENDERER:cell executed") + # else: + # pass + + +class AutoRenderer(Renderer): + """ Automatically detects which renderer to use based on environment.""" + + def __init__(self, trace_handler: TraceHandler): + """ Initializes. + + Args: + trace_handler: Trace handler of an engine. + """ + self._env = Environment() + + if legacy_mode(): + self._renderer = LegacyHtmlRenderer(trace_handler=trace_handler) + else: + if self._env.is_notebook(): + if stitch_installed: + self._renderer = JupyterWidgetRenderer(trace_handler=trace_handler) + else: + self._renderer = LegacyHtmlRenderer(trace_handler=trace_handler) + elif self._env.is_terminal(): + # TODO(nopdive): When IPython events are figured out (cell completion) + # hook up terminal interface separate to non-interactive + # shell. + self._renderer = DoNothingRenderer(trace_handler=trace_handler) + else: # pragma: no cover + logger.error("Env detection has failed. This is a bug.") + warn("Env detection has failed. No renderer will be provided.") + self._renderer = DoNothingRenderer(trace_handler=trace_handler) + + super().__init__() + + def notify(self, message: GuidanceMessage): + self._renderer.notify(message) + + def subscribe(self, callback: Callable[[GuidanceMessage], None]) -> None: + self._renderer.subscribe(callback) + + def unsubscribe(self, callback: Callable[[GuidanceMessage], None]) -> None: + self._renderer.unsubscribe(callback) + + def update(self, message: GuidanceMessage) -> None: + self._renderer.update(message) + + def renderer_type(self) -> type: + """Type of renderer that has been selected based on environment. + + Returns: + Type of selected renderer. + """ + return type(self._renderer) \ No newline at end of file diff --git a/guidance/visual/_trace.py b/guidance/visual/_trace.py new file mode 100644 index 000000000..24f6f2c62 --- /dev/null +++ b/guidance/visual/_trace.py @@ -0,0 +1,208 @@ +""" Visualization related to trace. """ + +import base64 +import json +from typing import Optional, Dict +from .._schema import GenToken + +from ..visual._message import TokensMessage +from ..trace import ( + TextOutput, + TraceNode, + TraceHandler, + RoleOpenerInput, + RoleCloserInput, + ImageOutput, +) +import html + + +def trace_node_to_html( + node: TraceNode, prettify_roles=False, complete_msg: TokensMessage = None +) -> str: + """Represents trace path as html string. + + Args: + node: Trace node that designates the end of a trace path for HTML output. + prettify_roles: Enables prettier formatting for roles. + complete_msg: Output message received on completion of engine. + + Returns: + HTML string of trace path as html. + """ + buffer = [] + node_path = list(node.path()) + active_role: Optional[TraceNode] = None + + prob_idx = 0 + # remaining_text = "" + full_text = "" + if complete_msg: + for token in complete_msg.tokens: + full_text += token.text + + for i, node in enumerate(node_path): + if isinstance(node.input, RoleOpenerInput): + active_role = node + elif isinstance(node.input, RoleCloserInput): + active_role = node + + if isinstance(node.output, TextOutput): + if active_role is not None: + if isinstance(active_role.input, RoleOpenerInput) and prettify_roles: + role_name = active_role.input.name + fmt = f"
{role_name.lower()}
"
+ buffer.append(fmt)
+ if not prettify_roles:
+ buffer.append("")
+
+ if not (active_role and prettify_roles):
+ attr = node.output
+
+ fmt = ""
+ if not complete_msg:
+ if attr.is_generated:
+ # fmt = f"{html.escape(attr.value)}"
+ fmt = f"{html.escape(attr.value)}"
+ elif attr.is_force_forwarded:
+ fmt = f"{html.escape(attr.value)}"
+ else:
+ # fmt = f"{html.escape(attr.value)}"
+ fmt += f"{html.escape(attr.value)}"
+ else:
+ # switch to token-based
+ # cell_tokens = attr.tokens
+ # for token in cell_tokens:
+ # # assert token.token == complete_msg.tokens[prob_idx].token, f"Token mismatch {token.token} != {complete_msg.tokens[prob_idx].token}"
+ # if token.token_id != complete_msg.tokens[prob_idx].token_id:
+ # if remaining_text + token.text != complete_msg.tokens[prob_idx].text:
+ # remaining_text += token.text
+ # continue
+ # else:
+ # remaining_text = ""
+
+ # token_str = complete_msg.tokens[prob_idx].text
+ # prob = complete_msg.tokens[prob_idx].prob
+ # top_k = {}
+ # # find the correct token
+ # for _item in complete_msg.tokens[prob_idx].top_k:
+ # top_k[f"{_item.text}"] = f"{_item.prob} - Masked: {_item.is_masked}"
+ # top_k = json.dumps(top_k, indent=2)
+
+ # latency = f"{complete_msg.tokens[prob_idx].latency_ms:.2f}"
+
+ # if complete_msg.tokens[prob_idx].is_generated:
+ # fmt += f"{html.escape(token_str)}"
+ # elif complete_msg.tokens[prob_idx].is_force_forwarded:
+ # fmt += f"{html.escape(token_str)}"
+ # else:
+ # fmt += f"{html.escape(token_str)}"
+
+ # full_text = full_text[len(token_str) :]
+ # prob_idx += 1
+
+ chunk_text = attr.value
+ # find tokens in complete message that cover this chunk
+ tokens: list[GenToken] = []
+ _idx = prob_idx
+ tokens_text = ""
+ while _idx < len(complete_msg.tokens):
+ _token = complete_msg.tokens[_idx]
+ tokens_text += _token.text
+ tokens.append(_token)
+ if chunk_text in tokens_text:
+ break
+ _idx += 1
+
+ assert (
+ chunk_text in tokens_text
+ ), f"Token mismatch {tokens_text} != {chunk_text}"
+
+ start_idx = tokens_text.index(chunk_text)
+ remaining_text = tokens_text[start_idx + len(chunk_text) :]
+
+ if remaining_text:
+ # remove the last tokens
+ tokens.pop(-1)
+
+ # update prob_idx
+ prob_idx += len(tokens)
+
+ for token in tokens:
+ token_str = token.text
+ prob = token.prob
+ top_k = {}
+ # find the correct token
+ for _item in token.top_k:
+ top_k[f"{_item.text}"] = f"{_item.prob} - Masked: {_item.is_masked}"
+ top_k = json.dumps(top_k, indent=2)
+
+ latency = f"{token.latency_ms:.2f}"
+
+ if token.is_generated:
+ fmt += f"{html.escape(token_str)}"
+ elif token.is_force_forwarded:
+ fmt += f"{html.escape(token_str)}"
+ else:
+ fmt += f"{html.escape(token_str)}"
+
+ buffer.append(fmt)
+
+ if active_role is not None:
+ if not prettify_roles:
+ buffer.append("")
+ if isinstance(active_role.input, RoleCloserInput) and prettify_roles:
+ buffer.append(f"
'
+ )
+
+ buffer.insert(
+ 0,
+ "",
+ )
+ buffer.append("")
+ return "".join(buffer)
+
+
+def trace_node_to_str(node: TraceNode) -> str:
+ """Visualize output attributes of a trace node up to the root.
+
+ Users should not be accessing this function. For debugging purposes.
+
+ Args:
+ node: The trace node to visualize.
+ Returns:
+ Output as string.
+ """
+ buffer = []
+ for node in node.path():
+ if isinstance(node.output, TextOutput):
+ buffer.append(str(node.output))
+ return "".join(buffer)
+
+
+def display_trace_tree(trace_handler: TraceHandler) -> None:
+ """Visualize tree of a trace node going down to all its leaves.
+
+ Users should not normally be accessing this function. For debugging purposes.
+
+ Args:
+ trace_handler: Trace handler needed to pull user-defined identifiers of trace nodes.
+ """
+ from anytree import Node, RenderTree
+
+ root = trace_handler.root()
+ trace_viz_map: Dict[TraceNode, Node] = {}
+ for node in root.traverse(bfs=False):
+ viz_parent = trace_viz_map.get(node.parent, None)
+ viz_node = Node(f"{trace_handler.node_id_map[node]}:{node!r}", parent=viz_parent)
+ trace_viz_map[node] = viz_node
+ viz_root = trace_viz_map[root]
+
+ for pre, fill, node in RenderTree(viz_root):
+ tree_str = "%s%s" % (pre, node.name)
+ print(tree_str)
diff --git a/notebooks/unstable/.gitignore b/notebooks/unstable/.gitignore
new file mode 100644
index 000000000..c96a04f00
--- /dev/null
+++ b/notebooks/unstable/.gitignore
@@ -0,0 +1,2 @@
+*
+!.gitignore
\ No newline at end of file
diff --git a/notebooks/unstable/State Debugging.ipynb b/notebooks/unstable/State Debugging.ipynb
new file mode 100644
index 000000000..7d7df97e7
--- /dev/null
+++ b/notebooks/unstable/State Debugging.ipynb
@@ -0,0 +1,196 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "d7d1e6967616e177",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-10-11T22:50:52.915277Z",
+ "start_time": "2024-10-11T22:50:52.730830Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import stitch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "1ec02432-a44d-4fdb-b44c-b153529fc03d",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-10-20T19:58:31.247827Z",
+ "start_time": "2024-10-20T19:58:24.650343Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/samueljenkins/dev/guidance-dev/venv/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "from guidance.models import Transformers\n",
+ "from guidance import gen, user, system\n",
+ "\n",
+ "import logging\n",
+ "logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s',\n",
+ " datefmt='%Y-%m-%d %H:%M:%S')\n",
+ "logging.getLogger().setLevel(logging.DEBUG)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "18b70f42-4b6d-41a4-94be-a41360571901",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ed0ad5ac277c4702b21ff8fdc9494dcf",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "StitchWidget(initial_height='auto', initial_width='100%', srcdoc='\\n\\n\\n …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-10-21 00:33:38 - DEBUG - start gen(name=\"suffix\")\n",
+ "2024-10-21 00:33:38 - DEBUG - finish gen\n",
+ "2024-10-21 00:33:38 - DEBUG - start Model._run_stateless\n",
+ "2024-10-21 00:33:38 - DEBUG - SEND:ready\n",
+ "2024-10-21 00:33:38 - DEBUG - finish Model._run_stateless\n",
+ "2024-10-21 00:33:38 - DEBUG - CELL:executed:message_id=201 class_name='ExecutionCompletedMessage' last_trace_id=69\n",
+ "2024-10-21 00:33:38 - DEBUG - ENGINE:message_id=201 class_name='ExecutionCompletedMessage' last_trace_id=69\n",
+ "2024-10-21 00:33:38 - DEBUG - JUPYTER:changed:8eea1d49-65c5-4f25-a863-5d7000948161|7\n",
+ "2024-10-21 00:33:38 - DEBUG - ENGINE:final_text:<|im_start|>system\n",
+ "You are responsible for writing an epic poem.<|im_end|>\n",
+ "<|im_start|>user\n",
+ "Roses are red and white and blue and red and\n",
+ "2024-10-21 00:33:38 - DEBUG - RENDER:execution completed out\n",
+ "2024-10-21 00:33:38 - DEBUG - RENDER:execution completed\n",
+ "2024-10-21 00:33:38 - DEBUG - RECV:clientready\n",
+ "2024-10-21 00:33:38 - DEBUG - ENGINE:message_id=208 class_name='ClientReadyMessage'\n",
+ "2024-10-21 00:33:38 - DEBUG - JUPYTER:changed:b5537f3f-cafc-4815-9da1-413e814172bd|7\n"
+ ]
+ }
+ ],
+ "source": [
+ "m0 = Transformers(\"gpt2\")\n",
+ "# m0 = Transformers(\"microsoft/Phi-3-mini-4k-instruct\")\n",
+ "# m1 = m0 + \"Alms for the \" + gen(name=\"suffix\", max_tokens=5, stop='\\n')\n",
+ "\n",
+ "with system():\n",
+ " # m1 = m0 + \"You are responsible for autocompleting a sentence.\"\n",
+ " m1 = m0 + \"You are responsible for writing an epic poem.\"\n",
+ "with user():\n",
+ " m2 = m1 + \"Roses are red and \" + gen(name=\"suffix\", regex=r'[\\w\\s]{20,30}', max_tokens=30)\n",
+ " # m2 = m1 + \"Roses are red and \" + gen(name=\"suffix\", regex=r'[\\w\\s]+{100}', max_tokens=500)\n",
+ " # m2 = m1 + \"Once upon a time\" + gen(name=\"end\", max_tokens=500)\n",
+ "# m3 = m2 + \">:(\"\n",
+ "\n",
+ "# with user():\n",
+ "# m3 = m2 + \"\\nWhat can I do?\"\n",
+ "\n",
+ "# m1 = m0 + f\"Hi there {gen(name='name', max_tokens=10, regex='[A-Za-z]{3,5}')}\"\n",
+ "# m2 = m0 + f\"You can't stop \" + gen(name='name', max_tokens=10, regex='[A-Za-z]{3,5}')\n",
+ "\n",
+ "# with system():\n",
+ "# m3 = m0 + \"You are responsible for autocompleting a sentence.\"\n",
+ "\n",
+ "# with user():\n",
+ "# m4 = m3 + \"The sun is rising \" + gen(name='sentence_suffix', max_tokens=3)\n",
+ "\n",
+ "# m2.html()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "5a5d668a11bda28f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7bc266ab105a4e1ebac84a5ccf374e93",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "StitchWidget(initial_height='auto', initial_width='100%', srcdoc='\\n\\n\\n …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-10-21 00:23:55 - DEBUG - CELL:executed:message_id=70 class_name='ExecutionCompletedMessage' last_trace_id=25\n",
+ "2024-10-21 00:23:55 - DEBUG - ENGINE:message_id=70 class_name='ExecutionCompletedMessage' last_trace_id=25\n",
+ "2024-10-21 00:23:55 - DEBUG - JUPYTER:changed:4a597956-2fce-418b-9cd4-7442bfc9dea2|5\n",
+ "2024-10-21 00:23:55 - DEBUG - SEND:ready\n",
+ "2024-10-21 00:23:55 - DEBUG - ENGINE:final_text:What is this?\n",
+ "2024-10-21 00:23:55 - DEBUG - RENDER:execution completed out\n",
+ "2024-10-21 00:23:55 - DEBUG - RENDER:execution completed\n",
+ "2024-10-21 00:23:55 - DEBUG - RECV:clientready\n",
+ "2024-10-21 00:23:55 - DEBUG - ENGINE:message_id=77 class_name='ClientReadyMessage'\n",
+ "2024-10-21 00:23:55 - DEBUG - JUPYTER:changed:196b33d5-8401-4388-ba99-d49248536afb|5\n"
+ ]
+ }
+ ],
+ "source": [
+ "from guidance import legacy_mode\n",
+ "legacy_mode(False)\n",
+ "\n",
+ "m5 = Transformers(\"gpt2\")\n",
+ "m6 = m5 + \"What is this?\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "235109a1-e1de-4303-bb0b-d7f9931a421c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/packages/python/stitch/.coveragerc b/packages/python/stitch/.coveragerc
new file mode 100644
index 000000000..dc0dc815f
--- /dev/null
+++ b/packages/python/stitch/.coveragerc
@@ -0,0 +1,2 @@
+[run]
+omit = stitch/tests/*
diff --git a/packages/python/stitch/.eslintignore b/packages/python/stitch/.eslintignore
new file mode 100644
index 000000000..e8a22210a
--- /dev/null
+++ b/packages/python/stitch/.eslintignore
@@ -0,0 +1,5 @@
+node_modules
+dist
+coverage
+**/*.d.ts
+tests
\ No newline at end of file
diff --git a/packages/python/stitch/.eslintrc.js b/packages/python/stitch/.eslintrc.js
new file mode 100644
index 000000000..9fb27ea5b
--- /dev/null
+++ b/packages/python/stitch/.eslintrc.js
@@ -0,0 +1,28 @@
+module.exports = {
+ extends: [
+ 'eslint:recommended',
+ 'plugin:@typescript-eslint/eslint-recommended',
+ 'plugin:@typescript-eslint/recommended',
+ 'plugin:prettier/recommended'
+ ],
+ parser: '@typescript-eslint/parser',
+ parserOptions: {
+ project: 'tsconfig.eslint.json',
+ sourceType: 'module'
+ },
+ plugins: ['@typescript-eslint'],
+ rules: {
+ '@typescript-eslint/no-unused-vars': ['warn', { args: 'none' }],
+ '@typescript-eslint/no-explicit-any': 'off',
+ '@typescript-eslint/no-namespace': 'off',
+ '@typescript-eslint/no-use-before-define': 'off',
+ '@typescript-eslint/quotes': [
+ 'error',
+ 'single',
+ { avoidEscape: true, allowTemplateLiterals: false }
+ ],
+ curly: ['error', 'all'],
+ eqeqeq: 'error',
+ 'prefer-arrow-callback': 'error'
+ }
+};
\ No newline at end of file
diff --git a/packages/python/stitch/.github/workflows/build.yml b/packages/python/stitch/.github/workflows/build.yml
new file mode 100644
index 000000000..dfab4e374
--- /dev/null
+++ b/packages/python/stitch/.github/workflows/build.yml
@@ -0,0 +1,44 @@
+name: Build
+
+on:
+ push:
+ branches: main
+ pull_request:
+ branches: "*"
+
+jobs:
+ build:
+ runs-on: ${{ matrix.os }}
+ strategy:
+ fail-fast: false
+ matrix:
+ os: [ubuntu-latest, windows-latest, macos-latest]
+ python-version: ["3.7", "3.10"]
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v2
+
+ - uses: jupyterlab/maintainer-tools/.github/actions/base-setup@v1
+
+ - name: Install dependencies
+ run: |
+ python -m pip install -U codecov
+ npm install -g codecov
+ - name: Test the extension
+ run: |
+ python -m pip install --upgrade -v -e ".[test, examples, docs]"
+ python -m pytest
+ yarn run test
+
+ - name: Linting
+ if: ${{ matrix.os == 'ubuntu-latest' }}
+ run: |
+ yarn run lint:check
+
+ - name: Check docs can be build + links
+ if: ${{ matrix.os == 'ubuntu-latest' }}
+ working-directory: docs
+ run: |
+ sudo apt install -y pandoc
+ make html
+ python -m pytest --check-links
diff --git a/packages/python/stitch/.gitignore b/packages/python/stitch/.gitignore
new file mode 100644
index 000000000..61493d6f8
--- /dev/null
+++ b/packages/python/stitch/.gitignore
@@ -0,0 +1,156 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask instance folder
+instance/
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+docs/source/_static/embed-bundle.js
+docs/source/_static/embed-bundle.js.map
+
+# PyBuilder
+target/
+
+# IPython Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# dotenv
+.env
+
+# virtualenv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+
+# Rope project settings
+.ropeproject
+
+# =========================
+# Operating System Files
+# =========================
+
+# OSX
+# =========================
+
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+# Windows
+# =========================
+
+# Windows image file caches
+Thumbs.db
+ehthumbs.db
+
+# Folder config file
+Desktop.ini
+
+# Recycle Bin used on file shares
+$RECYCLE.BIN/
+
+# Windows Installer files
+*.cab
+*.msi
+*.msm
+*.msp
+
+# Windows shortcuts
+*.lnk
+
+
+# NPM
+# ----
+
+**/node_modules/
+stitch/nbextension/index.*
+.yarn/
+
+# Coverage data
+# -------------
+**/coverage/
+
+# Packed lab extensions
+stitch/labextension
diff --git a/packages/python/stitch/.npmignore b/packages/python/stitch/.npmignore
new file mode 100644
index 000000000..f8ec1d196
--- /dev/null
+++ b/packages/python/stitch/.npmignore
@@ -0,0 +1,7 @@
+.DS_Store
+node_modules/
+tests/
+.jshintrc
+# Ignore any build output from python:
+dist/*.tar.gz
+dist/*.wheel
diff --git a/packages/python/stitch/.prettierignore b/packages/python/stitch/.prettierignore
new file mode 100644
index 000000000..b56605bbd
--- /dev/null
+++ b/packages/python/stitch/.prettierignore
@@ -0,0 +1,4 @@
+node_modules
+**/node_modules
+**/lib
+**/package.json
\ No newline at end of file
diff --git a/packages/python/stitch/.prettierrc b/packages/python/stitch/.prettierrc
new file mode 100644
index 000000000..dc2fb828f
--- /dev/null
+++ b/packages/python/stitch/.prettierrc
@@ -0,0 +1,3 @@
+{
+ "singleQuote": true
+}
\ No newline at end of file
diff --git a/packages/python/stitch/.yarnrc.yml b/packages/python/stitch/.yarnrc.yml
new file mode 100644
index 000000000..3186f3f07
--- /dev/null
+++ b/packages/python/stitch/.yarnrc.yml
@@ -0,0 +1 @@
+nodeLinker: node-modules
diff --git a/packages/python/stitch/LICENSE.txt b/packages/python/stitch/LICENSE.txt
new file mode 100644
index 000000000..be5b7c6c6
--- /dev/null
+++ b/packages/python/stitch/LICENSE.txt
@@ -0,0 +1,27 @@
+Copyright (c) 2024 Guidance Contributors
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+ list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+ this list of conditions and the following disclaimer in the documentation
+ and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+ contributors may be used to endorse or promote products derived from
+ this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/packages/python/stitch/MANIFEST.in b/packages/python/stitch/MANIFEST.in
new file mode 100644
index 000000000..ef35b9f2d
--- /dev/null
+++ b/packages/python/stitch/MANIFEST.in
@@ -0,0 +1,41 @@
+include LICENSE.txt
+include README.md
+
+include setup.py
+include pyproject.toml
+include pytest.ini
+include .coverage.rc
+
+include tsconfig.json
+include package.json
+include webpack.config.js
+include stitch/labextension/*.tgz
+
+# Documentation
+graft docs
+exclude docs/\#*
+prune docs/build
+prune docs/gh-pages
+prune docs/dist
+
+# Examples
+graft examples
+
+# Tests
+graft tests
+prune tests/build
+
+# Javascript files
+graft stitch/nbextension
+graft src
+graft css
+prune **/node_modules
+prune coverage
+prune lib
+
+# Patterns to exclude from any directory
+global-exclude *~
+global-exclude *.pyc
+global-exclude *.pyo
+global-exclude .git
+global-exclude .ipynb_checkpoints
diff --git a/packages/python/stitch/README.md b/packages/python/stitch/README.md
new file mode 100644
index 000000000..3267f1986
--- /dev/null
+++ b/packages/python/stitch/README.md
@@ -0,0 +1,83 @@
+
+# stitch
+
+[](https://travis-ci.org/guidance-ai/stitch)
+[](https://codecov.io/gh/guidance-ai/stitch)
+
+
+Bidirectional comms for Jupyter and JavaScript.
+
+## Installation
+
+You can install using `pip`:
+
+```bash
+pip install stitch
+```
+
+If you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
+the nbextension:
+```bash
+jupyter nbextension enable --py [--sys-prefix|--user|--system] stitch
+```
+
+## Development Installation
+
+Create a dev environment:
+```bash
+conda create -n stitch-dev -c conda-forge nodejs python jupyterlab=4.0.11
+conda activate stitch-dev
+```
+
+Install the python. This will also build the TS package.
+```bash
+pip install -e ".[test, examples]"
+```
+
+When developing your extensions, you need to manually enable your extensions with the
+notebook / lab frontend. For lab, this is done by the command:
+
+```
+jupyter labextension develop --overwrite .
+jlpm run build
+```
+
+For classic notebook, you need to run:
+
+```
+jupyter nbextension install --sys-prefix --symlink --overwrite --py stitch
+jupyter nbextension enable --sys-prefix --py stitch
+```
+
+Note that the `--symlink` flag doesn't work on Windows, so you will here have to run
+the `install` command every time that you rebuild your extension. For certain installations
+you might also need another flag instead of `--sys-prefix`, but we won't cover the meaning
+of those flags here.
+
+### How to see your changes
+#### Typescript:
+If you use JupyterLab to develop then you can watch the source directory and run JupyterLab at the same time in different
+terminals to watch for changes in the extension's source and automatically rebuild the widget.
+
+```bash
+# Watch the source directory in one terminal, automatically rebuilding when needed
+jlpm run watch
+# Run JupyterLab in another terminal
+jupyter lab
+```
+
+After a change wait for the build to finish and then refresh your browser and the changes should take effect.
+
+#### Python:
+If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.
+
+## Updating the version
+
+To update the version, install tbump and use it to bump the version.
+By default it will also create a tag.
+
+```bash
+pip install tbump
+tbump \n",
+ "MESSAGE: \n",
+ "
\n",
+ "\n",
+ "\n",
+ "\"\"\"\n",
+ "w.initial_width = '100%'\n",
+ "w.initial_height = 'auto'\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "w.kernelmsg = \"\"\"\n",
+ "A language model is a probabilistic model of a natural language.[1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.[2]\n",
+ "\n",
+ "Language models are useful for a variety of tasks, including speech recognition[3] (helping prevent predictions of low-probability (e.g. nonsense) sequences), machine translation,[4] natural language generation (generating more human-like text), optical character recognition, handwriting recognition,[5] grammar induction,[6] and information retrieval.[7][8]\n",
+ "\n",
+ "Large language models, currently their most advanced form, are a combination of larger datasets (frequently using words scraped from the public internet), feedforward neural networks, and transformers. They have superseded recurrent neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model. \n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "w.observe(lambda x: print(x['new']), 'kernelmsg')\n",
+ "w.kernelmsg = \"Wow, a change!\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/packages/python/stitch/install.json b/packages/python/stitch/install.json
new file mode 100644
index 000000000..fd70e6be8
--- /dev/null
+++ b/packages/python/stitch/install.json
@@ -0,0 +1,5 @@
+{
+ "packageManager": "python",
+ "packageName": "stitch",
+ "uninstallInstructions": "Use your Python package manager (pip, conda, etc.) to uninstall the package stitch"
+}
diff --git a/packages/python/stitch/jest.config.js b/packages/python/stitch/jest.config.js
new file mode 100644
index 000000000..79453a3ae
--- /dev/null
+++ b/packages/python/stitch/jest.config.js
@@ -0,0 +1,16 @@
+module.exports = {
+ automock: false,
+ moduleNameMapper: {
+ '\\.(css|less|sass|scss)$': 'identity-obj-proxy',
+ },
+ preset: 'ts-jest/presets/js-with-babel',
+ moduleFileExtensions: ['ts', 'tsx', 'js', 'jsx', 'json', 'node'],
+ testPathIgnorePatterns: ['/lib/', '/node_modules/'],
+ testRegex: '/__tests__/.*.spec.ts[x]?$',
+ transformIgnorePatterns: ['/node_modules/(?!(@jupyter(lab|-widgets)/.*)/)'],
+ globals: {
+ 'ts-jest': {
+ tsconfig: 'srcdoc should be defined by the user
', + ); + }); + + it('should be createable with a value', () => { + const state = { srcdoc: 'it is alright' }; + const model = createTestModel(StitchModel, state); + expect(model).toBeInstanceOf(StitchModel); + expect(model.get('srcdoc')).toEqual('it is alright'); + }); + }); +}); diff --git a/packages/python/stitch/src/__tests__/utils.ts b/packages/python/stitch/src/__tests__/utils.ts new file mode 100644 index 000000000..0a61d63e9 --- /dev/null +++ b/packages/python/stitch/src/__tests__/utils.ts @@ -0,0 +1,119 @@ +// Copyright (c) Jupyter Development Team. +// Distributed under the terms of the Modified BSD License. + +import * as widgets from '@jupyter-widgets/base'; +import * as baseManager from '@jupyter-widgets/base-manager'; +import * as services from '@jupyterlab/services'; + +let numComms = 0; + +export class MockComm implements widgets.IClassicComm { + constructor() { + this.comm_id = `mock-comm-id-${numComms}`; + numComms += 1; + } + on_close(fn: ((x?: any) => void) | null): void { + this._on_close = fn; + } + on_msg(fn: (x?: any) => void): void { + this._on_msg = fn; + } + _process_msg(msg: services.KernelMessage.ICommMsgMsg): void | Promisesrcdoc should be defined by the user
', + initial_height: '1px', + initial_width: '1px', + initial_border: '0', + }; + } + + static serializers: ISerializers = { + ...DOMWidgetModel.serializers, + // Add any extra serializers here + }; + + static model_name = 'StitchModel'; + static model_module = MODULE_NAME; + static model_module_version = MODULE_VERSION; + static view_name = 'StitchView'; // Set to null if no view + static view_module = MODULE_NAME; // Set to null if no view + static view_module_version = MODULE_VERSION; +} + +export class StitchView extends DOMWidgetView { + private _iframe: HTMLIFrameElement; + + render() { + // Create sandboxed frame + const iframe = document.createElement('iframe'); + iframe.sandbox.add('allow-scripts'); + iframe.srcdoc = this.model.get('srcdoc'); + iframe.style.height = this.model.get('initial_height'); + iframe.style.width = this.model.get('initial_width'); + iframe.style.border = this.model.get('initial_border'); + iframe.style.display = 'block'; + this._iframe = iframe; + + // Send first kernelmsg on load. + const refreshTimeMs = 100; + const sendKernelMsgOnReady = () => { + if (this.model.isNew()) { + window.setTimeout(sendKernelMsgOnReady, refreshTimeMs); + } else { + this.kernelmsg_changed(); + } + }; + window.setTimeout(sendKernelMsgOnReady, refreshTimeMs); + + // Add callback for forwarding messages from client to kernel + const model = this.model; + const recvFromClient = (event: any) => { + const win = iframe.contentWindow; + if (win === event.source && event.data.type === 'clientmsg') { + model.set('clientmsg', event.data.content); + model.save_changes(); + } else if (win === event.source && event.data.type === 'resize') { + iframe.style.height = event.data.content.height; + iframe.style.width = event.data.content.width; + } + }; + window.addEventListener('message', recvFromClient); + + // Add iframe to root element of widget + this.el.appendChild(this._iframe); + + // Connect callbacks for forwarding from kernel to client + this.model.on('change:kernelmsg', this.kernelmsg_changed, this); + + // Connect callback for when kernel changes the srcdoc + this.model.on('change:srcdoc', this.srcdoc_changed, this); + } + + kernelmsg_changed() { + // Forward message from kernel to client + const kernelmsg = this.model.get('kernelmsg'); + const winmsg = { + type: 'kernelmsg', + content: kernelmsg, + }; + this._iframe.contentWindow?.postMessage(winmsg, '*'); + } + + srcdoc_changed() { + // Apply srcdoc provided by client + const srcdoc = this.model.get('srcdoc'); + this._iframe.srcdoc = srcdoc; + + // Push last kernelmsg immediately to iframe + this.kernelmsg_changed(); + } +} diff --git a/packages/python/stitch/stitch.json b/packages/python/stitch/stitch.json new file mode 100644 index 000000000..d18d678e2 --- /dev/null +++ b/packages/python/stitch/stitch.json @@ -0,0 +1,5 @@ +{ + "load_extensions": { + "stitch/extension": true + } +} diff --git a/packages/python/stitch/stitch/__init__.py b/packages/python/stitch/stitch/__init__.py new file mode 100644 index 000000000..e984ee173 --- /dev/null +++ b/packages/python/stitch/stitch/__init__.py @@ -0,0 +1,49 @@ +#!/usr/bin/env python +# coding: utf-8 + +# Copyright (c) Guidance Contributors. +# Distributed under the terms of the Modified BSD License. + +from .stitch import StitchWidget +from ._version import __version__, version_info + +def _jupyter_labextension_paths(): + """Called by Jupyter Lab Server to detect if it is a valid labextension and + to install the widget + Returns + ======= + src: Source directory name to copy files from. Webpack outputs generated files + into this directory and Jupyter Lab copies from this directory during + widget installation + dest: Destination directory name to install widget files to. Jupyter Lab copies + from `src` directory intosrcdoc should be defined by the user
").tag(sync=True) + initial_height = Unicode("1px").tag(sync=True) + initial_width = Unicode("1px").tag(sync=True) + initial_border = Unicode("0").tag(sync=True) diff --git a/packages/python/stitch/stitch/tests/__init__.py b/packages/python/stitch/stitch/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/python/stitch/stitch/tests/conftest.py b/packages/python/stitch/stitch/tests/conftest.py new file mode 100644 index 000000000..ad3bd1758 --- /dev/null +++ b/packages/python/stitch/stitch/tests/conftest.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python +# coding: utf-8 + +# Copyright (c) Guidance Contributors. +# Distributed under the terms of the Modified BSD License. + +import pytest + +from ipykernel.comm import Comm +from ipywidgets import Widget + +class MockComm(Comm): + """A mock Comm object. + + Can be used to inspect calls to Comm's open/send/close methods. + """ + comm_id = 'a-b-c-d' + kernel = 'Truthy' + + def __init__(self, *args, **kwargs): + self.log_open = [] + self.log_send = [] + self.log_close = [] + super(MockComm, self).__init__(*args, **kwargs) + + def open(self, *args, **kwargs): + self.log_open.append((args, kwargs)) + + def send(self, *args, **kwargs): + self.log_send.append((args, kwargs)) + + def close(self, *args, **kwargs): + self.log_close.append((args, kwargs)) + +_widget_attrs = {} +undefined = object() + + +@pytest.fixture +def mock_comm(): + _widget_attrs['_comm_default'] = getattr(Widget, '_comm_default', undefined) + Widget._comm_default = lambda self: MockComm() + _widget_attrs['_ipython_display_'] = Widget._ipython_display_ + def raise_not_implemented(*args, **kwargs): + raise NotImplementedError() + Widget._ipython_display_ = raise_not_implemented + + yield MockComm() + + for attr, value in _widget_attrs.items(): + if value is undefined: + delattr(Widget, attr) + else: + setattr(Widget, attr, value) diff --git a/packages/python/stitch/stitch/tests/test_example.py b/packages/python/stitch/stitch/tests/test_example.py new file mode 100644 index 000000000..0d216157a --- /dev/null +++ b/packages/python/stitch/stitch/tests/test_example.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python +# coding: utf-8 + +# Copyright (c) Guidance Contributors. +# Distributed under the terms of the Modified BSD License. + +import pytest + +from ..stitch import StitchWidget + + +def test_example_creation_blank(): + w = StitchWidget() + assert w.kernelmsg == "" + assert w.clientmsg == "" + assert w.srcdoc == "srcdoc should be defined by the user
" diff --git a/packages/python/stitch/stitch/tests/test_nbextension_path.py b/packages/python/stitch/stitch/tests/test_nbextension_path.py new file mode 100644 index 000000000..c134480ab --- /dev/null +++ b/packages/python/stitch/stitch/tests/test_nbextension_path.py @@ -0,0 +1,15 @@ +#!/usr/bin/env python +# coding: utf-8 + +# Copyright (c) Guidance Contributors. +# Distributed under the terms of the Modified BSD License. + + +def test_nbextension_path(): + # Check that magic function can be imported from package root: + from stitch import _jupyter_nbextension_paths + # Ensure that it can be called without incident: + path = _jupyter_nbextension_paths() + # Some sanity checks: + assert len(path) == 1 + assert isinstance(path[0], dict) diff --git a/packages/python/stitch/tsconfig.eslint.json b/packages/python/stitch/tsconfig.eslint.json new file mode 100644 index 000000000..737e3e680 --- /dev/null +++ b/packages/python/stitch/tsconfig.eslint.json @@ -0,0 +1,5 @@ +{ + "extends": "./tsconfig.json", + "include": ["src/**/*.ts", "src/**/*.tsx"], + "exclude": [] +} \ No newline at end of file diff --git a/packages/python/stitch/tsconfig.json b/packages/python/stitch/tsconfig.json new file mode 100644 index 000000000..f143a554e --- /dev/null +++ b/packages/python/stitch/tsconfig.json @@ -0,0 +1,25 @@ +{ + "compilerOptions": { + "declaration": true, + "esModuleInterop":true, + "lib": ["es2015", "dom"], + "module": "commonjs", + "moduleResolution": "node", + "noEmitOnError": true, + "noUnusedLocals": true, + "outDir": "lib", + "resolveJsonModule": true, + "rootDir": "src", + "skipLibCheck": true, + "sourceMap": true, + "strict": true, + "strictPropertyInitialization": false, + "target": "es2015", + "types": ["jest"] + }, + "include": [ + "src/**/*.ts", + "src/**/*.tsx", + ], + "exclude": ["src/**/__tests__"] +} diff --git a/packages/python/stitch/webpack.config.js b/packages/python/stitch/webpack.config.js new file mode 100644 index 000000000..8faaf089b --- /dev/null +++ b/packages/python/stitch/webpack.config.js @@ -0,0 +1,91 @@ +const path = require('path'); +const version = require('./package.json').version; + +// Custom webpack rules +const rules = [ + { test: /\.ts$/, loader: 'ts-loader' }, + { test: /\.js$/, loader: 'source-map-loader' }, + { test: /\.css$/, use: ['style-loader', 'css-loader']} +]; + +// Packages that shouldn't be bundled but loaded at runtime +const externals = ['@jupyter-widgets/base']; + +const resolve = { + // Add '.ts' and '.tsx' as resolvable extensions. + extensions: [".webpack.js", ".web.js", ".ts", ".js"] +}; + +module.exports = [ + /** + * Notebook extension + * + * This bundle only contains the part of the JavaScript that is run on load of + * the notebook. + */ + { + entry: './src/extension.ts', + output: { + filename: 'index.js', + path: path.resolve(__dirname, 'stitch', 'nbextension'), + libraryTarget: 'amd', + publicPath: '', + }, + module: { + rules: rules + }, + devtool: 'source-map', + externals, + resolve, + }, + + /** + * Embeddable stitch bundle + * + * This bundle is almost identical to the notebook extension bundle. The only + * difference is in the configuration of the webpack public path for the + * static assets. + * + * The target bundle is always `dist/index.js`, which is the path required by + * the custom widget embedder. + */ + { + entry: './src/index.ts', + output: { + filename: 'index.js', + path: path.resolve(__dirname, 'dist'), + libraryTarget: 'amd', + library: "stitch", + publicPath: 'https://unpkg.com/stitch@' + version + '/dist/' + }, + devtool: 'source-map', + module: { + rules: rules + }, + externals, + resolve, + }, + + + /** + * Documentation widget bundle + * + * This bundle is used to embed widgets in the package documentation. + */ + { + entry: './src/index.ts', + output: { + filename: 'embed-bundle.js', + path: path.resolve(__dirname, 'docs', 'source', '_static'), + library: "stitch", + libraryTarget: 'amd' + }, + module: { + rules: rules + }, + devtool: 'source-map', + externals, + resolve, + } + +]; diff --git a/packages/python/stitch/yarn.lock 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