Bump gradio from 3.15.0 to 3.34.0 (#1518)

Bumps [gradio](https://github.com/gradio-app/gradio) from 3.15.0 to 3.34.0.
- [Release notes](https://github.com/gradio-app/gradio/releases)
- [Changelog](https://github.com/gradio-app/gradio/blob/main/CHANGELOG.md)
- [Commits](https://github.com/gradio-app/gradio/compare/v3.15.0...v3.34.0)

---
updated-dependencies:
- dependency-name: gradio
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

.github/workflows/nightly.yml

@@ -50,27 +50,13 @@ jobs:
       shell: powershell
       run: |
         ./setup_venv.ps1
+        $env:SHARK_PACKAGE_VERSION=${{ env.package_version }}
+        pip wheel -v -w dist . --pre -f https://download.pytorch.org/whl/nightly/cpu -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
         python process_skipfiles.py
         pyinstaller .\apps\stable_diffusion\shark_sd.spec
-        mv ./dist/shark_sd.exe ./dist/shark_sd_${{ env.package_version_ }}.exe
-        signtool sign /f c:\g\shark_02152023.cer /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/shark_sd_${{ env.package_version_ }}.exe
-        pyinstaller .\apps\stable_diffusion\shark_sd_cli.spec
-        python process_skipfiles.py
-        mv ./dist/shark_sd_cli.exe ./dist/shark_sd_cli_${{ env.package_version_ }}.exe
-        signtool sign /f c:\g\shark_02152023.cer /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/shark_sd_cli_${{ env.package_version_ }}.exe
-
-        
-    # GHA windows VM OOMs so disable for now
-    #- name: Build and validate the SHARK Runtime package
-    #  shell: powershell
-    #  run: |
-    #    $env:SHARK_PACKAGE_VERSION=${{ env.package_version }}
-    #    pip wheel -v -w dist . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
-
-    #- uses: actions/upload-artifact@v2
-    #  with:
-    #    path: dist/*
-    
+        mv ./dist/shark_sd.exe ./dist/nodai_shark_sd_${{ env.package_version_ }}.exe
+        signtool sign /f c:\g\shark_02152023.cer /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/nodai_shark_sd_${{ env.package_version_ }}.exe
+  
     - name: Upload Release Assets
       id: upload-release-assets
       uses: dwenegar/upload-release-assets@v1
@@ -78,7 +64,7 @@ jobs:
         GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
       with:
         release_id: ${{ steps.create_release.outputs.id }}
-        assets_path: ./dist/*
+        assets_path: ./dist/nodai*
         #asset_content_type: application/vnd.microsoft.portable-executable 
 
     - name: Publish Release

apps/language_models/scripts/sharded_vicuna_fp32.py

@@ -1,656 +0,0 @@
-import sys
-import warnings
-
-warnings.filterwarnings("ignore")
-sys.path.insert(0, "D:\S\SB\I\python_packages\iree_compiler")
-sys.path.insert(0, "D:\S\SB\I\python_packages\iree_runtime")
-import torch
-import torch_mlir
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from torch.fx.experimental.proxy_tensor import make_fx
-from torch._decomp import get_decompositions
-from typing import List
-from io import BytesIO
-from pathlib import Path
-from shark.shark_downloader import download_public_file
-from shark.shark_importer import transform_fx as transform_fx_
-import re
-from shark.shark_inference import SharkInference
-from tqdm import tqdm
-from torch_mlir import TensorPlaceholder
-
-
-class FirstVicunaLayer(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-
-    def forward(self, hidden_states, attention_mask, position_ids):
-        outputs = self.model(
-            hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            use_cache=True,
-        )
-        next_hidden_states = outputs[0]
-        past_key_value_out0, past_key_value_out1 = (
-            outputs[-1][0],
-            outputs[-1][1],
-        )
-
-        return (
-            next_hidden_states,
-            past_key_value_out0,
-            past_key_value_out1,
-        )
-
-
-class SecondVicunaLayer(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        position_ids,
-        past_key_value0,
-        past_key_value1,
-    ):
-        outputs = self.model(
-            hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=(
-                past_key_value0,
-                past_key_value1,
-            ),
-            use_cache=True,
-        )
-        next_hidden_states = outputs[0]
-        past_key_value_out0, past_key_value_out1 = (
-            outputs[-1][0],
-            outputs[-1][1],
-        )
-
-        return (
-            next_hidden_states,
-            past_key_value_out0,
-            past_key_value_out1,
-        )
-
-
-class CompiledFirstVicunaLayer(torch.nn.Module):
-    def __init__(self, shark_module):
-        super().__init__()
-        self.model = shark_module
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        position_ids,
-        past_key_value=None,
-        output_attentions=False,
-        use_cache=True,
-    ):
-        hidden_states = hidden_states.detach()
-        attention_mask = attention_mask.detach()
-        position_ids = position_ids.detach()
-        output = self.model(
-            "forward",
-            (
-                hidden_states,
-                attention_mask,
-                position_ids,
-            ),
-        )
-
-        output0 = torch.tensor(output[0])
-        output1 = torch.tensor(output[1])
-        output2 = torch.tensor(output[2])
-
-        return (
-            output0,
-            (
-                output1,
-                output2,
-            ),
-        )
-
-
-class CompiledSecondVicunaLayer(torch.nn.Module):
-    def __init__(self, shark_module):
-        super().__init__()
-        self.model = shark_module
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        position_ids,
-        past_key_value,
-        output_attentions=False,
-        use_cache=True,
-    ):
-        hidden_states = hidden_states.detach()
-        attention_mask = attention_mask.detach()
-        position_ids = position_ids.detach()
-        pkv0 = past_key_value[0].detach()
-        pkv1 = past_key_value[1].detach()
-        output = self.model(
-            "forward",
-            (
-                hidden_states,
-                attention_mask,
-                position_ids,
-                pkv0,
-                pkv1,
-            ),
-        )
-
-        output0 = torch.tensor(output[0])
-        output1 = torch.tensor(output[1])
-        output2 = torch.tensor(output[2])
-
-        return (
-            output0,
-            (
-                output1,
-                output2,
-            ),
-        )
-
-
-class ShardedVicunaModel(torch.nn.Module):
-    def __init__(self, model, layers0, layers1):
-        super().__init__()
-        self.model = model
-        assert len(layers0) == len(model.model.layers)
-        # self.model.model.layers = torch.nn.modules.container.ModuleList(layers0)
-        self.model.model.config.use_cache = True
-        self.model.model.config.output_attentions = False
-        self.layers0 = layers0
-        self.layers1 = layers1
-
-    def forward(
-        self,
-        input_ids,
-        is_first=True,
-        past_key_values=None,
-        attention_mask=None,
-    ):
-        if is_first:
-            self.model.model.layers = torch.nn.modules.container.ModuleList(
-                self.layers0
-            )
-            return self.model.forward(input_ids, attention_mask=attention_mask)
-        else:
-            self.model.model.layers = torch.nn.modules.container.ModuleList(
-                self.layers1
-            )
-            return self.model.forward(
-                input_ids,
-                attention_mask=attention_mask,
-                past_key_values=past_key_values,
-            )
-
-
-def write_in_dynamic_inputs0(module, dynamic_input_size):
-    new_lines = []
-    for line in module.splitlines():
-        line = re.sub(f"{dynamic_input_size}x", "?x", line)
-        if "?x" in line:
-            line = re.sub("tensor.empty\(\)", "tensor.empty(%dim)", line)
-        line = re.sub(f" {dynamic_input_size},", " %dim,", line)
-        if "tensor.empty" in line and "?x?" in line:
-            line = re.sub(
-                "tensor.empty\(%dim\)", "tensor.empty(%dim, %dim)", line
-            )
-        if "arith.cmpi" in line:
-            line = re.sub(f"c{dynamic_input_size}", "dim", line)
-        new_lines.append(line)
-    new_module = "\n".join(new_lines)
-    return new_module
-
-
-def write_in_dynamic_inputs1(module, dynamic_input_size):
-    new_lines = []
-    for line in module.splitlines():
-        if "dim_42 =" in line:
-            continue
-        if f"%c{dynamic_input_size}_i64 =" in line:
-            new_lines.append(
-                "%dim_42 = tensor.dim %arg1, %c3 : tensor<1x1x1x?xf32>"
-            )
-            new_lines.append(
-                f"%dim_42_i64 = arith.index_cast %dim_42 : index to i64"
-            )
-            continue
-        line = re.sub(f"{dynamic_input_size}x", "?x", line)
-        if "?x" in line:
-            line = re.sub("tensor.empty\(\)", "tensor.empty(%dim_42)", line)
-        line = re.sub(f" {dynamic_input_size},", " %dim_42,", line)
-        if "tensor.empty" in line and "?x?" in line:
-            line = re.sub(
-                "tensor.empty\(%dim_42\)",
-                "tensor.empty(%dim_42, %dim_42)",
-                line,
-            )
-        if "arith.cmpi" in line:
-            line = re.sub(f"c{dynamic_input_size}", "dim_42", line)
-        new_lines.append(line)
-    new_module = "\n".join(new_lines)
-    return new_module
-
-
-def compile_vicuna_layer(
-    vicuna_layer,
-    hidden_states,
-    attention_mask,
-    position_ids,
-    past_key_value0=None,
-    past_key_value1=None,
-):
-    hidden_states_placeholder = TensorPlaceholder.like(
-        hidden_states, dynamic_axes=[1]
-    )
-    attention_mask_placeholder = TensorPlaceholder.like(
-        attention_mask, dynamic_axes=[2, 3]
-    )
-    position_ids_placeholder = TensorPlaceholder.like(
-        position_ids, dynamic_axes=[1]
-    )
-
-    if past_key_value0 is None and past_key_value1 is None:
-        fx_g = make_fx(
-            vicuna_layer,
-            decomposition_table=get_decompositions(
-                [
-                    torch.ops.aten.embedding_dense_backward,
-                    torch.ops.aten.native_layer_norm_backward,
-                    torch.ops.aten.slice_backward,
-                    torch.ops.aten.select_backward,
-                    torch.ops.aten.norm.ScalarOpt_dim,
-                    torch.ops.aten.native_group_norm,
-                    torch.ops.aten.upsample_bilinear2d.vec,
-                    torch.ops.aten.split.Tensor,
-                    torch.ops.aten.split_with_sizes,
-                ]
-            ),
-        )(hidden_states, attention_mask, position_ids)
-
-    else:
-        fx_g = make_fx(
-            vicuna_layer,
-            decomposition_table=get_decompositions(
-                [
-                    torch.ops.aten.embedding_dense_backward,
-                    torch.ops.aten.native_layer_norm_backward,
-                    torch.ops.aten.slice_backward,
-                    torch.ops.aten.select_backward,
-                    torch.ops.aten.norm.ScalarOpt_dim,
-                    torch.ops.aten.native_group_norm,
-                    torch.ops.aten.upsample_bilinear2d.vec,
-                    torch.ops.aten.split.Tensor,
-                    torch.ops.aten.split_with_sizes,
-                ]
-            ),
-        )(
-            hidden_states,
-            attention_mask,
-            position_ids,
-            past_key_value0,
-            past_key_value1,
-        )
-
-    def _remove_nones(fx_g: torch.fx.GraphModule) -> List[int]:
-        removed_indexes = []
-        for node in fx_g.graph.nodes:
-            if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
-                node_arg = node.args[0]
-                if isinstance(node_arg, (list, tuple)):
-                    node_arg = list(node_arg)
-                    node_args_len = len(node_arg)
-                    for i in range(node_args_len):
-                        curr_index = node_args_len - (i + 1)
-                        if node_arg[curr_index] is None:
-                            removed_indexes.append(curr_index)
-                            node_arg.pop(curr_index)
-                    node.args = (tuple(node_arg),)
-                    break
-
-        if len(removed_indexes) > 0:
-            fx_g.graph.lint()
-            fx_g.graph.eliminate_dead_code()
-            fx_g.recompile()
-        removed_indexes.sort()
-        return removed_indexes
-
-    def _unwrap_single_tuple_return(fx_g: torch.fx.GraphModule) -> bool:
-        """
-        Replace tuple with tuple element in functions that return one-element tuples.
-        Returns true if an unwrapping took place, and false otherwise.
-        """
-        unwrapped_tuple = False
-        for node in fx_g.graph.nodes:
-            if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
-                node_arg = node.args[0]
-                if isinstance(node_arg, tuple):
-                    if len(node_arg) == 1:
-                        node.args = (node_arg[0],)
-                        unwrapped_tuple = True
-                        break
-
-        if unwrapped_tuple:
-            fx_g.graph.lint()
-            fx_g.recompile()
-        return unwrapped_tuple
-
-    def transform_fx(fx_g):
-        for node in fx_g.graph.nodes:
-            if node.op == "call_function":
-                if node.target in [
-                    torch.ops.aten.empty,
-                ]:
-                    # aten.empty should be filled with zeros.
-                    if node.target in [torch.ops.aten.empty]:
-                        with fx_g.graph.inserting_after(node):
-                            new_node = fx_g.graph.call_function(
-                                torch.ops.aten.zero_,
-                                args=(node,),
-                            )
-                            node.append(new_node)
-                            node.replace_all_uses_with(new_node)
-                            new_node.args = (node,)
-
-        fx_g.graph.lint()
-
-    transform_fx(fx_g)
-    fx_g.recompile()
-    removed_none_indexes = _remove_nones(fx_g)
-    was_unwrapped = _unwrap_single_tuple_return(fx_g)
-
-    fx_g.graph.set_codegen(torch.fx.graph.CodeGen())
-    fx_g.recompile()
-
-    print("FX_G recompile")
-
-    def strip_overloads(gm):
-        """
-        Modifies the target of graph nodes in :attr:`gm` to strip overloads.
-        Args:
-            gm(fx.GraphModule): The input Fx graph module to be modified
-        """
-        for node in gm.graph.nodes:
-            if isinstance(node.target, torch._ops.OpOverload):
-                node.target = node.target.overloadpacket
-        gm.recompile()
-
-    strip_overloads(fx_g)
-    ts_g = torch.jit.script(fx_g)
-    return ts_g
-
-
-def get_model_and_tokenizer(path="TheBloke/vicuna-7B-1.1-HF"):
-    kwargs = {"torch_dtype": torch.float}
-    vicuna_model = AutoModelForCausalLM.from_pretrained(path, **kwargs)
-    tokenizer = AutoTokenizer.from_pretrained(path, use_fast=False)
-    return vicuna_model, tokenizer
-
-
-def compile_to_vmfb(inputs, layers, is_first=True):
-    mlirs, modules = [], []
-    for idx, layer in tqdm(enumerate(layers), desc="Getting mlirs"):
-        if is_first:
-            mlir_path = Path(f"{idx}_0.mlir")
-            vmfb_path = Path(f"{idx}_0.vmfb")
-        else:
-            mlir_path = Path(f"{idx}_1.mlir")
-            vmfb_path = Path(f"{idx}_1.vmfb")
-        if vmfb_path.exists():
-            continue
-        if mlir_path.exists():
-            # print(f"Found layer {idx} mlir")
-            f_ = open(mlir_path, "rb")
-            bytecode = f_.read()
-            f_.close()
-        else:
-            hidden_states_placeholder = TensorPlaceholder.like(
-                inputs[0], dynamic_axes=[1]
-            )
-            attention_mask_placeholder = TensorPlaceholder.like(
-                inputs[1], dynamic_axes=[3]
-            )
-            position_ids_placeholder = TensorPlaceholder.like(
-                inputs[2], dynamic_axes=[1]
-            )
-            if not is_first:
-                pkv0_placeholder = TensorPlaceholder.like(
-                    inputs[3], dynamic_axes=[2]
-                )
-                pkv1_placeholder = TensorPlaceholder.like(
-                    inputs[4], dynamic_axes=[2]
-                )
-            print(f"Compiling layer {idx} mlir")
-            if is_first:
-                ts_g = compile_vicuna_layer(
-                    layer, inputs[0], inputs[1], inputs[2]
-                )
-                module = torch_mlir.compile(
-                    ts_g,
-                    (
-                        hidden_states_placeholder,
-                        inputs[1],
-                        inputs[2],
-                    ),
-                    torch_mlir.OutputType.LINALG_ON_TENSORS,
-                    use_tracing=False,
-                    verbose=False,
-                )
-            else:
-                ts_g = compile_vicuna_layer(
-                    layer,
-                    inputs[0],
-                    inputs[1],
-                    inputs[2],
-                    inputs[3],
-                    inputs[4],
-                )
-                module = torch_mlir.compile(
-                    ts_g,
-                    (
-                        inputs[0],
-                        attention_mask_placeholder,
-                        inputs[2],
-                        pkv0_placeholder,
-                        pkv1_placeholder,
-                    ),
-                    torch_mlir.OutputType.LINALG_ON_TENSORS,
-                    use_tracing=False,
-                    verbose=False,
-                )
-
-            # bytecode_stream = BytesIO()
-            # module.operation.write_bytecode(bytecode_stream)
-            # bytecode = bytecode_stream.getvalue()
-
-            if is_first:
-                module = write_in_dynamic_inputs0(str(module), 137)
-                bytecode = module.encode("UTF-8")
-                bytecode_stream = BytesIO(bytecode)
-                bytecode = bytecode_stream.read()
-
-            else:
-                module = write_in_dynamic_inputs1(str(module), 138)
-                if idx in [0, 5, 6, 7]:
-                    module_str = module
-                    module_str = module_str.splitlines()
-                    new_lines = []
-                    for line in module_str:
-                        if len(line) < 1000:
-                            new_lines.append(line)
-                        else:
-                            new_lines.append(line[:999])
-                    module_str = "\n".join(new_lines)
-                    f1_ = open(f"{idx}_1_test.mlir", "w+")
-                    f1_.write(module_str)
-                    f1_.close()
-
-                bytecode = module.encode("UTF-8")
-                bytecode_stream = BytesIO(bytecode)
-                bytecode = bytecode_stream.read()
-
-            f_ = open(mlir_path, "wb")
-            f_.write(bytecode)
-            f_.close()
-        mlirs.append(bytecode)
-
-    for idx, layer in tqdm(enumerate(layers), desc="compiling modules"):
-        if is_first:
-            vmfb_path = Path(f"{idx}_0.vmfb")
-            if idx < 25:
-                device = "cpu"
-            else:
-                device = "cpu"
-            if vmfb_path.exists():
-                # print(f"Found layer {idx} vmfb")
-                module = SharkInference(
-                    None, device=device, mlir_dialect="tm_tensor"
-                )
-                module.load_module(vmfb_path)
-            else:
-                print(f"Compiling layer {idx} vmfb")
-                module = SharkInference(
-                    mlirs[idx], device=device, mlir_dialect="tm_tensor"
-                )
-                module.save_module(
-                    module_name=f"{idx}_0",
-                    extra_args=[
-                        "--iree-hal-dump-executable-sources-to=ies",
-                        "--iree-vm-target-truncate-unsupported-floats",
-                        "--iree-codegen-check-ir-before-llvm-conversion=false",
-                        "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
-                    ],
-                )
-                module.load_module(vmfb_path)
-            modules.append(module)
-        else:
-            vmfb_path = Path(f"{idx}_1.vmfb")
-            if idx < 25:
-                device = "vulkan"
-            else:
-                device = "cpu"
-            if vmfb_path.exists():
-                # print(f"Found layer {idx} vmfb")
-                module = SharkInference(
-                    None, device=device, mlir_dialect="tm_tensor"
-                )
-                module.load_module(vmfb_path)
-            else:
-                print(f"Compiling layer {idx} vmfb")
-                module = SharkInference(
-                    mlirs[idx], device=device, mlir_dialect="tm_tensor"
-                )
-                module.save_module(
-                    module_name=f"{idx}_1",
-                    extra_args=[
-                        "--iree-hal-dump-executable-sources-to=ies",
-                        "--iree-vm-target-truncate-unsupported-floats",
-                        "--iree-codegen-check-ir-before-llvm-conversion=false",
-                        "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
-                    ],
-                )
-                module.load_module(vmfb_path)
-            modules.append(module)
-
-    return mlirs, modules
-
-
-def get_sharded_model():
-    # SAMPLE_INPUT_LEN is used for creating mlir with dynamic inputs, which is currently an increadibly hacky proccess
-    # please don't change it
-    SAMPLE_INPUT_LEN = 137
-    vicuna_model = get_model_and_tokenizer()[0]
-
-    placeholder_input0 = (
-        torch.zeros([1, SAMPLE_INPUT_LEN, 4096]),
-        torch.zeros([1, 1, SAMPLE_INPUT_LEN, SAMPLE_INPUT_LEN]),
-        torch.zeros([1, SAMPLE_INPUT_LEN], dtype=torch.int64),
-    )
-
-    placeholder_input1 = (
-        torch.zeros([1, 1, 4096]),
-        torch.zeros([1, 1, 1, SAMPLE_INPUT_LEN + 1]),
-        torch.zeros([1, 1], dtype=torch.int64),
-        torch.zeros([1, 32, SAMPLE_INPUT_LEN, 128]),
-        torch.zeros([1, 32, SAMPLE_INPUT_LEN, 128]),
-    )
-
-    layers0 = [FirstVicunaLayer(layer) for layer in vicuna_model.model.layers]
-    _, modules0 = compile_to_vmfb(placeholder_input0, layers0, is_first=True)
-    shark_layers0 = [CompiledFirstVicunaLayer(m) for m in modules0]
-
-    layers1 = [SecondVicunaLayer(layer) for layer in vicuna_model.model.layers]
-    _, modules1 = compile_to_vmfb(placeholder_input1, layers1, is_first=False)
-    shark_layers1 = [CompiledSecondVicunaLayer(m) for m in modules1]
-
-    sharded_model = ShardedVicunaModel(
-        vicuna_model, shark_layers0, shark_layers1
-    )
-    return sharded_model
-
-
-if __name__ == "__main__":
-    prompt_history = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
-    prologue_prompt = "ASSISTANT:\n"
-    sharded_model = get_sharded_model()
-    tokenizer = get_model_and_tokenizer()[1]
-    past_key_values = None
-    while True:
-        print("\n\n")
-        user_prompt = input("User: ")
-        prompt_history = (
-            prompt_history + "USER:\n" + user_prompt + prologue_prompt
-        )
-        prompt = prompt_history.strip()
-        input_ids = tokenizer(prompt).input_ids
-        tokens = input_ids
-        prompt = print("Robot:", end=" ")
-        new_sentence = []
-        max_response_len = 1000
-        for iteration in range(max_response_len):
-            original_input_ids = input_ids
-            input_id_len = len(input_ids)
-            input_ids = torch.tensor(input_ids)
-            input_ids = input_ids.reshape([1, input_id_len])
-
-            if iteration == 0:
-                output = sharded_model.forward(input_ids, is_first=True)
-            else:
-                output = sharded_model.forward(
-                    input_ids, past_key_values=past_key_values, is_first=False
-                )
-            logits = output["logits"]
-            past_key_values = output["past_key_values"]
-            new_token = int(torch.argmax(logits[:, -1, :], dim=1)[0])
-            if new_token == 2:
-                break
-            new_sentence += [new_token]
-            tokens.append(new_token)
-            original_input_ids.append(new_token)
-            input_ids = [new_token]
-
-        for i in range(len(tokens)):
-            if type(tokens[i]) != int:
-                tokens[i] = int(tokens[i][0])
-        new_sentence_str = tokenizer.decode(new_sentence)
-        print(new_sentence_str)
-        prompt_history += f"\n{new_sentence_str}\n"

apps/language_models/scripts/sharded_vicuna_fp32_web.py

@@ -1,777 +0,0 @@
-import sys
-import warnings
-import gradio as gr
-import time
-
-warnings.filterwarnings("ignore")
-sys.path.insert(0, "D:\S\SB\I\python_packages\iree_compiler")
-sys.path.insert(0, "D:\S\SB\I\python_packages\iree_runtime")
-import torch
-import torch_mlir
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from torch.fx.experimental.proxy_tensor import make_fx
-from torch._decomp import get_decompositions
-from typing import List
-from io import BytesIO
-from pathlib import Path
-from shark.shark_downloader import download_public_file
-from shark.shark_importer import transform_fx as transform_fx_
-import re
-from shark.shark_inference import SharkInference
-from tqdm import tqdm
-from torch_mlir import TensorPlaceholder
-from apps.stable_diffusion.web.ui.utils import available_devices
-
-
-class FirstVicunaLayer(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-
-    def forward(self, hidden_states, attention_mask, position_ids):
-        outputs = self.model(
-            hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            use_cache=True,
-        )
-        next_hidden_states = outputs[0]
-        past_key_value_out0, past_key_value_out1 = (
-            outputs[-1][0],
-            outputs[-1][1],
-        )
-
-        return (
-            next_hidden_states,
-            past_key_value_out0,
-            past_key_value_out1,
-        )
-
-
-class SecondVicunaLayer(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        position_ids,
-        past_key_value0,
-        past_key_value1,
-    ):
-        outputs = self.model(
-            hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=(
-                past_key_value0,
-                past_key_value1,
-            ),
-            use_cache=True,
-        )
-        next_hidden_states = outputs[0]
-        past_key_value_out0, past_key_value_out1 = (
-            outputs[-1][0],
-            outputs[-1][1],
-        )
-
-        return (
-            next_hidden_states,
-            past_key_value_out0,
-            past_key_value_out1,
-        )
-
-
-class CompiledFirstVicunaLayer(torch.nn.Module):
-    def __init__(self, shark_module):
-        super().__init__()
-        self.model = shark_module
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        position_ids,
-        past_key_value=None,
-        output_attentions=False,
-        use_cache=True,
-    ):
-        hidden_states = hidden_states.detach()
-        attention_mask = attention_mask.detach()
-        position_ids = position_ids.detach()
-        output = self.model(
-            "forward",
-            (
-                hidden_states,
-                attention_mask,
-                position_ids,
-            ),
-        )
-
-        output0 = torch.tensor(output[0])
-        output1 = torch.tensor(output[1])
-        output2 = torch.tensor(output[2])
-
-        return (
-            output0,
-            (
-                output1,
-                output2,
-            ),
-        )
-
-
-class CompiledSecondVicunaLayer(torch.nn.Module):
-    def __init__(self, shark_module):
-        super().__init__()
-        self.model = shark_module
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        position_ids,
-        past_key_value,
-        output_attentions=False,
-        use_cache=True,
-    ):
-        hidden_states = hidden_states.detach()
-        attention_mask = attention_mask.detach()
-        position_ids = position_ids.detach()
-        pkv0 = past_key_value[0].detach()
-        pkv1 = past_key_value[1].detach()
-        output = self.model(
-            "forward",
-            (
-                hidden_states,
-                attention_mask,
-                position_ids,
-                pkv0,
-                pkv1,
-            ),
-        )
-
-        output0 = torch.tensor(output[0])
-        output1 = torch.tensor(output[1])
-        output2 = torch.tensor(output[2])
-
-        return (
-            output0,
-            (
-                output1,
-                output2,
-            ),
-        )
-
-
-class ShardedVicunaModel(torch.nn.Module):
-    def __init__(self, model, layers0, layers1):
-        super().__init__()
-        self.model = model
-        assert len(layers0) == len(model.model.layers)
-        # self.model.model.layers = torch.nn.modules.container.ModuleList(layers0)
-        self.model.model.config.use_cache = True
-        self.model.model.config.output_attentions = False
-        self.layers0 = layers0
-        self.layers1 = layers1
-
-    def forward(
-        self,
-        input_ids,
-        is_first=True,
-        past_key_values=None,
-        attention_mask=None,
-    ):
-        if is_first:
-            self.model.model.layers = torch.nn.modules.container.ModuleList(
-                self.layers0
-            )
-            return self.model.forward(input_ids, attention_mask=attention_mask)
-        else:
-            self.model.model.layers = torch.nn.modules.container.ModuleList(
-                self.layers1
-            )
-            return self.model.forward(
-                input_ids,
-                attention_mask=attention_mask,
-                past_key_values=past_key_values,
-            )
-
-
-def write_in_dynamic_inputs0(module, dynamic_input_size):
-    new_lines = []
-    for line in module.splitlines():
-        line = re.sub(f"{dynamic_input_size}x", "?x", line)
-        if "?x" in line:
-            line = re.sub("tensor.empty\(\)", "tensor.empty(%dim)", line)
-        line = re.sub(f" {dynamic_input_size},", " %dim,", line)
-        if "tensor.empty" in line and "?x?" in line:
-            line = re.sub(
-                "tensor.empty\(%dim\)", "tensor.empty(%dim, %dim)", line
-            )
-        if "arith.cmpi" in line:
-            line = re.sub(f"c{dynamic_input_size}", "dim", line)
-        new_lines.append(line)
-    new_module = "\n".join(new_lines)
-    return new_module
-
-
-def write_in_dynamic_inputs1(module, dynamic_input_size):
-    new_lines = []
-    for line in module.splitlines():
-        if "dim_42 =" in line:
-            continue
-        if f"%c{dynamic_input_size}_i64 =" in line:
-            new_lines.append(
-                "%dim_42 = tensor.dim %arg1, %c3 : tensor<1x1x1x?xf32>"
-            )
-            new_lines.append(
-                f"%dim_42_i64 = arith.index_cast %dim_42 : index to i64"
-            )
-            continue
-        line = re.sub(f"{dynamic_input_size}x", "?x", line)
-        if "?x" in line:
-            line = re.sub("tensor.empty\(\)", "tensor.empty(%dim_42)", line)
-        line = re.sub(f" {dynamic_input_size},", " %dim_42,", line)
-        if "tensor.empty" in line and "?x?" in line:
-            line = re.sub(
-                "tensor.empty\(%dim_42\)",
-                "tensor.empty(%dim_42, %dim_42)",
-                line,
-            )
-        if "arith.cmpi" in line:
-            line = re.sub(f"c{dynamic_input_size}", "dim_42", line)
-        new_lines.append(line)
-    new_module = "\n".join(new_lines)
-    return new_module
-
-
-def compile_vicuna_layer(
-    vicuna_layer,
-    hidden_states,
-    attention_mask,
-    position_ids,
-    past_key_value0=None,
-    past_key_value1=None,
-):
-    hidden_states_placeholder = TensorPlaceholder.like(
-        hidden_states, dynamic_axes=[1]
-    )
-    attention_mask_placeholder = TensorPlaceholder.like(
-        attention_mask, dynamic_axes=[2, 3]
-    )
-    position_ids_placeholder = TensorPlaceholder.like(
-        position_ids, dynamic_axes=[1]
-    )
-
-    if past_key_value0 is None and past_key_value1 is None:
-        fx_g = make_fx(
-            vicuna_layer,
-            decomposition_table=get_decompositions(
-                [
-                    torch.ops.aten.embedding_dense_backward,
-                    torch.ops.aten.native_layer_norm_backward,
-                    torch.ops.aten.slice_backward,
-                    torch.ops.aten.select_backward,
-                    torch.ops.aten.norm.ScalarOpt_dim,
-                    torch.ops.aten.native_group_norm,
-                    torch.ops.aten.upsample_bilinear2d.vec,
-                    torch.ops.aten.split.Tensor,
-                    torch.ops.aten.split_with_sizes,
-                ]
-            ),
-        )(hidden_states, attention_mask, position_ids)
-
-    else:
-        fx_g = make_fx(
-            vicuna_layer,
-            decomposition_table=get_decompositions(
-                [
-                    torch.ops.aten.embedding_dense_backward,
-                    torch.ops.aten.native_layer_norm_backward,
-                    torch.ops.aten.slice_backward,
-                    torch.ops.aten.select_backward,
-                    torch.ops.aten.norm.ScalarOpt_dim,
-                    torch.ops.aten.native_group_norm,
-                    torch.ops.aten.upsample_bilinear2d.vec,
-                    torch.ops.aten.split.Tensor,
-                    torch.ops.aten.split_with_sizes,
-                ]
-            ),
-        )(
-            hidden_states,
-            attention_mask,
-            position_ids,
-            past_key_value0,
-            past_key_value1,
-        )
-
-    def _remove_nones(fx_g: torch.fx.GraphModule) -> List[int]:
-        removed_indexes = []
-        for node in fx_g.graph.nodes:
-            if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
-                node_arg = node.args[0]
-                if isinstance(node_arg, (list, tuple)):
-                    node_arg = list(node_arg)
-                    node_args_len = len(node_arg)
-                    for i in range(node_args_len):
-                        curr_index = node_args_len - (i + 1)
-                        if node_arg[curr_index] is None:
-                            removed_indexes.append(curr_index)
-                            node_arg.pop(curr_index)
-                    node.args = (tuple(node_arg),)
-                    break
-
-        if len(removed_indexes) > 0:
-            fx_g.graph.lint()
-            fx_g.graph.eliminate_dead_code()
-            fx_g.recompile()
-        removed_indexes.sort()
-        return removed_indexes
-
-    def _unwrap_single_tuple_return(fx_g: torch.fx.GraphModule) -> bool:
-        """
-        Replace tuple with tuple element in functions that return one-element tuples.
-        Returns true if an unwrapping took place, and false otherwise.
-        """
-        unwrapped_tuple = False
-        for node in fx_g.graph.nodes:
-            if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
-                node_arg = node.args[0]
-                if isinstance(node_arg, tuple):
-                    if len(node_arg) == 1:
-                        node.args = (node_arg[0],)
-                        unwrapped_tuple = True
-                        break
-
-        if unwrapped_tuple:
-            fx_g.graph.lint()
-            fx_g.recompile()
-        return unwrapped_tuple
-
-    def transform_fx(fx_g):
-        for node in fx_g.graph.nodes:
-            if node.op == "call_function":
-                if node.target in [
-                    torch.ops.aten.empty,
-                ]:
-                    # aten.empty should be filled with zeros.
-                    if node.target in [torch.ops.aten.empty]:
-                        with fx_g.graph.inserting_after(node):
-                            new_node = fx_g.graph.call_function(
-                                torch.ops.aten.zero_,
-                                args=(node,),
-                            )
-                            node.append(new_node)
-                            node.replace_all_uses_with(new_node)
-                            new_node.args = (node,)
-
-        fx_g.graph.lint()
-
-    transform_fx(fx_g)
-    fx_g.recompile()
-    removed_none_indexes = _remove_nones(fx_g)
-    was_unwrapped = _unwrap_single_tuple_return(fx_g)
-
-    fx_g.graph.set_codegen(torch.fx.graph.CodeGen())
-    fx_g.recompile()
-
-    print("FX_G recompile")
-
-    def strip_overloads(gm):
-        """
-        Modifies the target of graph nodes in :attr:`gm` to strip overloads.
-        Args:
-            gm(fx.GraphModule): The input Fx graph module to be modified
-        """
-        for node in gm.graph.nodes:
-            if isinstance(node.target, torch._ops.OpOverload):
-                node.target = node.target.overloadpacket
-        gm.recompile()
-
-    strip_overloads(fx_g)
-    ts_g = torch.jit.script(fx_g)
-    return ts_g
-
-
-path = "TheBloke/vicuna-7B-1.1-HF"
-kwargs = {"torch_dtype": torch.float}
-vicuna_model = AutoModelForCausalLM.from_pretrained(path, **kwargs)
-tokenizer = AutoTokenizer.from_pretrained(path, use_fast=False)
-
-
-def compile_to_vmfb(inputs, layers, is_first=True):
-    mlirs, modules = [], []
-    for idx, layer in tqdm(enumerate(layers), desc="Getting mlirs"):
-        if is_first:
-            mlir_path = Path(f"{idx}_0.mlir")
-            vmfb_path = Path(f"{idx}_0.vmfb")
-        else:
-            mlir_path = Path(f"{idx}_1.mlir")
-            vmfb_path = Path(f"{idx}_1.vmfb")
-        if vmfb_path.exists():
-            continue
-        if mlir_path.exists():
-            # print(f"Found layer {idx} mlir")
-            f_ = open(mlir_path, "rb")
-            bytecode = f_.read()
-            f_.close()
-        else:
-            hidden_states_placeholder = TensorPlaceholder.like(
-                inputs[0], dynamic_axes=[1]
-            )
-            attention_mask_placeholder = TensorPlaceholder.like(
-                inputs[1], dynamic_axes=[3]
-            )
-            position_ids_placeholder = TensorPlaceholder.like(
-                inputs[2], dynamic_axes=[1]
-            )
-            if not is_first:
-                pkv0_placeholder = TensorPlaceholder.like(
-                    inputs[3], dynamic_axes=[2]
-                )
-                pkv1_placeholder = TensorPlaceholder.like(
-                    inputs[4], dynamic_axes=[2]
-                )
-            print(f"Compiling layer {idx} mlir")
-            if is_first:
-                ts_g = compile_vicuna_layer(
-                    layer, inputs[0], inputs[1], inputs[2]
-                )
-                module = torch_mlir.compile(
-                    ts_g,
-                    (
-                        hidden_states_placeholder,
-                        inputs[1],
-                        inputs[2],
-                    ),
-                    torch_mlir.OutputType.LINALG_ON_TENSORS,
-                    use_tracing=False,
-                    verbose=False,
-                )
-            else:
-                ts_g = compile_vicuna_layer(
-                    layer,
-                    inputs[0],
-                    inputs[1],
-                    inputs[2],
-                    inputs[3],
-                    inputs[4],
-                )
-                module = torch_mlir.compile(
-                    ts_g,
-                    (
-                        inputs[0],
-                        attention_mask_placeholder,
-                        inputs[2],
-                        pkv0_placeholder,
-                        pkv1_placeholder,
-                    ),
-                    torch_mlir.OutputType.LINALG_ON_TENSORS,
-                    use_tracing=False,
-                    verbose=False,
-                )
-
-            # bytecode_stream = BytesIO()
-            # module.operation.write_bytecode(bytecode_stream)
-            # bytecode = bytecode_stream.getvalue()
-
-            if is_first:
-                module = write_in_dynamic_inputs0(str(module), 137)
-                bytecode = module.encode("UTF-8")
-                bytecode_stream = BytesIO(bytecode)
-                bytecode = bytecode_stream.read()
-
-            else:
-                module = write_in_dynamic_inputs1(str(module), 138)
-                if idx in [0, 5, 6, 7]:
-                    module_str = module
-                    module_str = module_str.splitlines()
-                    new_lines = []
-                    for line in module_str:
-                        if len(line) < 1000:
-                            new_lines.append(line)
-                        else:
-                            new_lines.append(line[:999])
-                    module_str = "\n".join(new_lines)
-                    f1_ = open(f"{idx}_1_test.mlir", "w+")
-                    f1_.write(module_str)
-                    f1_.close()
-
-                bytecode = module.encode("UTF-8")
-                bytecode_stream = BytesIO(bytecode)
-                bytecode = bytecode_stream.read()
-
-            f_ = open(mlir_path, "wb")
-            f_.write(bytecode)
-            f_.close()
-        mlirs.append(bytecode)
-
-    for idx, layer in tqdm(enumerate(layers), desc="compiling modules"):
-        if is_first:
-            vmfb_path = Path(f"{idx}_0.vmfb")
-            if idx < 25:
-                device = "cpu"
-            else:
-                device = "cpu"
-            if vmfb_path.exists():
-                # print(f"Found layer {idx} vmfb")
-                module = SharkInference(
-                    None, device=device, mlir_dialect="tm_tensor"
-                )
-                module.load_module(vmfb_path)
-            else:
-                print(f"Compiling layer {idx} vmfb")
-                module = SharkInference(
-                    mlirs[idx], device=device, mlir_dialect="tm_tensor"
-                )
-                module.save_module(
-                    module_name=f"{idx}_0",
-                    extra_args=[
-                        "--iree-hal-dump-executable-sources-to=ies",
-                        "--iree-vm-target-truncate-unsupported-floats",
-                        "--iree-codegen-check-ir-before-llvm-conversion=false",
-                        "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
-                    ],
-                )
-                module.load_module(vmfb_path)
-            modules.append(module)
-        else:
-            vmfb_path = Path(f"{idx}_1.vmfb")
-            if idx < 25:
-                device = "vulkan"
-            else:
-                device = "cpu"
-            if vmfb_path.exists():
-                # print(f"Found layer {idx} vmfb")
-                module = SharkInference(
-                    None, device=device, mlir_dialect="tm_tensor"
-                )
-                module.load_module(vmfb_path)
-            else:
-                print(f"Compiling layer {idx} vmfb")
-                module = SharkInference(
-                    mlirs[idx], device=device, mlir_dialect="tm_tensor"
-                )
-                module.save_module(
-                    module_name=f"{idx}_1",
-                    extra_args=[
-                        "--iree-hal-dump-executable-sources-to=ies",
-                        "--iree-vm-target-truncate-unsupported-floats",
-                        "--iree-codegen-check-ir-before-llvm-conversion=false",
-                        "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
-                    ],
-                )
-                module.load_module(vmfb_path)
-            modules.append(module)
-
-    return mlirs, modules
-
-
-def get_sharded_model():
-    # SAMPLE_INPUT_LEN is used for creating mlir with dynamic inputs, which is currently an increadibly hacky proccess
-    # please don't change it
-    SAMPLE_INPUT_LEN = 137
-    global vicuna_model
-
-    placeholder_input0 = (
-        torch.zeros([1, SAMPLE_INPUT_LEN, 4096]),
-        torch.zeros([1, 1, SAMPLE_INPUT_LEN, SAMPLE_INPUT_LEN]),
-        torch.zeros([1, SAMPLE_INPUT_LEN], dtype=torch.int64),
-    )
-
-    placeholder_input1 = (
-        torch.zeros([1, 1, 4096]),
-        torch.zeros([1, 1, 1, SAMPLE_INPUT_LEN + 1]),
-        torch.zeros([1, 1], dtype=torch.int64),
-        torch.zeros([1, 32, SAMPLE_INPUT_LEN, 128]),
-        torch.zeros([1, 32, SAMPLE_INPUT_LEN, 128]),
-    )
-
-    layers0 = [FirstVicunaLayer(layer) for layer in vicuna_model.model.layers]
-    _, modules0 = compile_to_vmfb(placeholder_input0, layers0, is_first=True)
-    shark_layers0 = [CompiledFirstVicunaLayer(m) for m in modules0]
-
-    layers1 = [SecondVicunaLayer(layer) for layer in vicuna_model.model.layers]
-    _, modules1 = compile_to_vmfb(placeholder_input1, layers1, is_first=False)
-    shark_layers1 = [CompiledSecondVicunaLayer(m) for m in modules1]
-
-    sharded_model = ShardedVicunaModel(
-        vicuna_model, shark_layers0, shark_layers1
-    )
-    return sharded_model
-
-
-sharded_model = get_sharded_model()
-
-
-def user(message, history):
-    print("msg=", message)
-    print("history=", history)
-    # Append the user's message to the conversation history
-    return "", history + [[message, ""]]
-
-
-def chat(curr_system_message, history):
-    global sharded_model
-    past_key_values = None
-    messages = curr_system_message + "".join(
-        [
-            "".join(["<|USER|>" + item[0], "<|ASSISTANT|>" + item[1]])
-            for item in history
-        ]
-    )
-    print(messages)
-    prompt = messages.strip()
-    input_ids = tokenizer(prompt).input_ids
-    tokens = input_ids
-    new_sentence = []
-    max_response_len = 1000
-    partial_sentence = []
-    partial_text = ""
-    start_time = time.time()
-    for iteration in range(max_response_len):
-        original_input_ids = input_ids
-        input_id_len = len(input_ids)
-        input_ids = torch.tensor(input_ids)
-        input_ids = input_ids.reshape([1, input_id_len])
-
-        if iteration == 0:
-            output = sharded_model.forward(input_ids, is_first=True)
-        else:
-            output = sharded_model.forward(
-                input_ids, past_key_values=past_key_values, is_first=False
-            )
-        logits = output["logits"]
-        past_key_values = output["past_key_values"]
-        new_token = int(torch.argmax(logits[:, -1, :], dim=1)[0])
-        if new_token == 2:
-            break
-        new_sentence += [new_token]
-        partial_sentence += [new_token]
-        if iteration > 0 and iteration % 2 == 0:
-            new_text = tokenizer.decode(partial_sentence)
-            partial_sentence = []
-            print(new_text, " ")
-            partial_text += new_text + " "
-            history[-1][1] = partial_text
-            yield history
-
-        tokens.append(new_token)
-        original_input_ids.append(new_token)
-        input_ids = [new_token]
-    end_time = time.time()
-    print(
-        f"Total time taken to generated response is {end_time-start_time} seconds"
-    )
-
-    for i in range(len(tokens)):
-        if type(tokens[i]) != int:
-            tokens[i] = int(tokens[i][0])
-    new_sentence_str = tokenizer.decode(new_sentence)
-    print(new_sentence_str)
-    history[-1][1] = new_sentence_str
-    return history
-
-
-system_msg = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
-# history_eg = [["hi hello how are you", ""]]
-# print(chat(system_msg, history_eg))
-
-with gr.Blocks(title="Chatbot") as vicuna_chat:
-    with gr.Row():
-        model = gr.Dropdown(
-            label="Select Model",
-            value="TheBloke/vicuna-7B-1.1-HF",
-            choices=[
-                "TheBloke/vicuna-7B-1.1-HF",
-            ],
-        )
-        device_value = None
-        for d in available_devices:
-            if "vulkan" in d:
-                device_value = d
-                break
-
-        device = gr.Dropdown(
-            label="Device",
-            value=device_value if device_value else available_devices[0],
-            interactive=False,
-            choices=available_devices,
-        )
-    chatbot = gr.Chatbot().style(height=500)
-    with gr.Row():
-        with gr.Column():
-            msg = gr.Textbox(
-                label="Chat Message Box",
-                placeholder="Chat Message Box",
-                show_label=False,
-            ).style(container=False)
-        with gr.Column():
-            with gr.Row():
-                submit = gr.Button("Submit")
-                stop = gr.Button("Stop")
-                clear = gr.Button("Clear")
-    system_msg = gr.Textbox(
-        system_msg, label="System Message", interactive=False, visible=False
-    )
-
-    submit_event = msg.submit(
-        fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False
-    ).then(
-        fn=chat,
-        inputs=[system_msg, chatbot],
-        outputs=[chatbot],
-        queue=True,
-    )
-    submit_click_event = submit.click(
-        fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False
-    ).then(
-        fn=chat,
-        inputs=[system_msg, chatbot],
-        outputs=[chatbot],
-        queue=True,
-    )
-    stop.click(
-        fn=None,
-        inputs=None,
-        outputs=None,
-        cancels=[submit_event, submit_click_event],
-        queue=False,
-    )
-    clear.click(lambda: None, None, [chatbot], queue=False)
-
-import argparse
-
-p = argparse.ArgumentParser(
-    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
-)
-p.add_argument(
-    "--share",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="flag for generating a public URL",
-)
-p.add_argument(
-    "--server_port",
-    type=int,
-    default=8080,
-    help="flag for setting server port",
-)
-args, unknown = p.parse_known_args()
-
-vicuna_chat.queue()
-vicuna_chat.launch(
-    share=args.share,
-    inbrowser=True,
-    server_name="0.0.0.0",
-    server_port=args.server_port,
-)

apps/language_models/scripts/sharded_vicunia.py

@@ -1,303 +0,0 @@
-import torch
-import argparse
-import torch_mlir
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from torch.fx.experimental.proxy_tensor import make_fx
-from torch._decomp import get_decompositions
-from typing import List
-from io import BytesIO
-from pathlib import Path
-from shark.shark_downloader import download_public_file
-from shark.shark_importer import transform_fx as transform_fx_
-import re
-from shark.shark_inference import SharkInference
-from tqdm import tqdm
-
-parser = argparse.ArgumentParser(
-    prog="ProgramName",
-    description="What the program does",
-    epilog="Text at the bottom of help",
-)
-
-parser.add_argument("--precision", "-p", default="fp32", help="fp32, fp16")
-parser.add_argument(
-    "--device", "-d", default="vulkan", help="vulkan, cpu, cuda"
-)
-
-
-class VicunaLayer(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-
-    def forward(self, hidden_states, attention_mask, position_ids):
-        outputs = self.model(
-            hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-        )
-        next_hidden_states = outputs[0]
-        return next_hidden_states
-
-
-class CompiledVicunaLayer(torch.nn.Module):
-    def __init__(self, shark_module):
-        super().__init__()
-        self.model = shark_module
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        position_ids,
-        past_key_value=None,
-        output_attentions=False,
-        use_cache=False,
-    ):
-        hidden_states = hidden_states.detach()
-        attention_mask = attention_mask.detach()
-        position_ids = position_ids.detach()
-        output = self.model(
-            "forward",
-            (
-                hidden_states,
-                attention_mask,
-                position_ids,
-            ),
-        )
-
-        print(output)
-
-        output = torch.tensor(output)
-
-        return (output,)
-
-
-class ShardedVicunaModel(torch.nn.Module):
-    def __init__(self, model, layers):
-        super().__init__()
-        self.model = model
-        assert len(layers) == len(model.model.layers)
-        self.model.model.layers = torch.nn.modules.container.ModuleList(layers)
-        self.model.model.config.use_cache = False
-        self.model.model.config.output_attentions = False
-
-    def forward(self, input_ids, attention_mask=None):
-        return self.model.forward(input_ids, attention_mask=attention_mask)
-
-
-def compile_vicuna_layer(
-    vicuna_layer, hidden_states, attention_mask, position_ids
-):
-    fx_g = make_fx(
-        vicuna_layer,
-        decomposition_table=get_decompositions(
-            [
-                torch.ops.aten.embedding_dense_backward,
-                torch.ops.aten.native_layer_norm_backward,
-                torch.ops.aten.slice_backward,
-                torch.ops.aten.select_backward,
-                torch.ops.aten.norm.ScalarOpt_dim,
-                torch.ops.aten.native_group_norm,
-                torch.ops.aten.upsample_bilinear2d.vec,
-                torch.ops.aten.split.Tensor,
-                torch.ops.aten.split_with_sizes,
-            ]
-        ),
-    )(hidden_states, attention_mask, position_ids)
-
-    def _remove_nones(fx_g: torch.fx.GraphModule) -> List[int]:
-        removed_indexes = []
-        for node in fx_g.graph.nodes:
-            if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
-                node_arg = node.args[0]
-                if isinstance(node_arg, (list, tuple)):
-                    node_arg = list(node_arg)
-                    node_args_len = len(node_arg)
-                    for i in range(node_args_len):
-                        curr_index = node_args_len - (i + 1)
-                        if node_arg[curr_index] is None:
-                            removed_indexes.append(curr_index)
-                            node_arg.pop(curr_index)
-                    node.args = (tuple(node_arg),)
-                    break
-
-        if len(removed_indexes) > 0:
-            fx_g.graph.lint()
-            fx_g.graph.eliminate_dead_code()
-            fx_g.recompile()
-        removed_indexes.sort()
-        return removed_indexes
-
-    def _unwrap_single_tuple_return(fx_g: torch.fx.GraphModule) -> bool:
-        """
-        Replace tuple with tuple element in functions that return one-element tuples.
-        Returns true if an unwrapping took place, and false otherwise.
-        """
-        unwrapped_tuple = False
-        for node in fx_g.graph.nodes:
-            if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
-                node_arg = node.args[0]
-                if isinstance(node_arg, tuple):
-                    if len(node_arg) == 1:
-                        node.args = (node_arg[0],)
-                        unwrapped_tuple = True
-                        break
-
-        if unwrapped_tuple:
-            fx_g.graph.lint()
-            fx_g.recompile()
-        return unwrapped_tuple
-
-    def transform_fx(fx_g):
-        for node in fx_g.graph.nodes:
-            if node.op == "call_function":
-                if node.target in [
-                    torch.ops.aten.empty,
-                ]:
-                    # aten.empty should be filled with zeros.
-                    if node.target in [torch.ops.aten.empty]:
-                        with fx_g.graph.inserting_after(node):
-                            new_node = fx_g.graph.call_function(
-                                torch.ops.aten.zero_,
-                                args=(node,),
-                            )
-                            node.append(new_node)
-                            node.replace_all_uses_with(new_node)
-                            new_node.args = (node,)
-
-        fx_g.graph.lint()
-
-    transform_fx(fx_g)
-    if args.precision == "fp16":
-        fx_g = fx_g.half()
-    fx_g.recompile()
-    removed_none_indexes = _remove_nones(fx_g)
-    was_unwrapped = _unwrap_single_tuple_return(fx_g)
-
-    fx_g.graph.set_codegen(torch.fx.graph.CodeGen())
-    fx_g.recompile()
-
-    print("FX_G recompile")
-
-    def strip_overloads(gm):
-        """
-        Modifies the target of graph nodes in :attr:`gm` to strip overloads.
-        Args:
-            gm(fx.GraphModule): The input Fx graph module to be modified
-        """
-        for node in gm.graph.nodes:
-            if isinstance(node.target, torch._ops.OpOverload):
-                node.target = node.target.overloadpacket
-        gm.recompile()
-
-    strip_overloads(fx_g)
-    ts_g = torch.jit.script(fx_g)
-    return ts_g
-
-
-path = "TheBloke/vicuna-7B-1.1-HF"
-kwargs = {"torch_dtype": torch.float32}
-vicuna_model = AutoModelForCausalLM.from_pretrained(
-    path, low_cpu_mem_usage=True, **kwargs
-)
-tokenizer = AutoTokenizer.from_pretrained(path, use_fast=False)
-
-print(type(vicuna_model.model.layers))
-
-
-def compile_to_vmfb(inputs, layers):
-    mlirs, modules = [], []
-    for idx, layer in tqdm(enumerate(layers), desc="Getting mlirs"):
-        mlir_path = Path(f"{idx}.mlir")
-        if mlir_path.exists():
-            # print(f"Found layer {idx} mlir")
-            f_ = open(mlir_path, "rb")
-            bytecode = f_.read()
-            f_.close()
-        else:
-            print(f"Compiling layer {idx} mlir")
-            ts_g = compile_vicuna_layer(layer, inputs[0], inputs[1], inputs[2])
-            module = torch_mlir.compile(
-                ts_g,
-                inputs,
-                torch_mlir.OutputType.LINALG_ON_TENSORS,
-                use_tracing=False,
-                verbose=False,
-            )
-            bytecode_stream = BytesIO()
-            module.operation.write_bytecode(bytecode_stream)
-            bytecode = bytecode_stream.getvalue()
-            f_ = open(mlir_path, "wb")
-            f_.write(bytecode)
-            f_.close()
-        mlirs.append(bytecode)
-
-    for idx, layer in tqdm(enumerate(layers), desc="compiling modules"):
-        device = args.device if idx < 25 else "cpu"
-        vmfb_path = Path(f"{idx}.vmfb")
-        if vmfb_path.exists():
-            # print(f"Found layer {idx} vmfb")
-            module = SharkInference(
-                None, device=device, mlir_dialect="tm_tensor"
-            )
-            module.load_module(vmfb_path)
-        else:
-            print(f"Compiling layer {idx} vmfb")
-            module = SharkInference(
-                mlirs[idx], device=device, mlir_dialect="tm_tensor"
-            )
-            module.save_module("", f"{idx}")
-            module.load_module(vmfb_path)
-        modules.append(module)
-    return mlirs, modules
-
-
-if __name__ == "__main__":
-    args = parser.parse_args()
-    # prompt = input("Enter Prompt: ")
-    dtype = torch.float32 if args.precision == "fp32" else torch.float16
-    placeholder_input = (
-        torch.zeros([1, 256, 4096], dtype=dtype),
-        torch.zeros([1, 1, 256, 256], dtype=dtype),
-        torch.zeros([1, 256], dtype=torch.int64),
-    )
-
-    _, modules = compile_to_vmfb(placeholder_input, vicuna_model.model.layers)
-
-    shark_layers = [CompiledVicunaLayer(m) for m in modules]
-
-    sharded_model = ShardedVicunaModel(vicuna_model, shark_layers)
-    prompt = "It was a dark and stormy"
-    prompt = prompt.strip()
-    input_ids = tokenizer(prompt).input_ids
-    original_input_ids = input_ids
-    input_id_len = len(input_ids)
-    pad_len = 256 - input_id_len
-    attention_mask = torch.ones([1, input_id_len], dtype=torch.int64)
-    input_ids = torch.nn.functional.pad(
-        torch.tensor(input_ids), (0, pad_len), mode="constant", value=259
-    )
-    input_ids = input_ids.reshape([1, 256])
-    attention_mask = torch.nn.functional.pad(
-        torch.tensor(attention_mask),
-        (0, pad_len),
-        mode="constant",
-        value=0,
-    )
-
-    # print(input_ids)
-    if args.precision == "fp16":
-        input_ids = input_ids.to(torch.float16)
-    print(attention_mask)
-
-    logits = sharded_model.forward(input_ids, attention_mask=attention_mask)[
-        "logits"
-    ]
-    print(logits)

apps/language_models/scripts/stablelm.py

@@ -1,27 +1,15 @@
 import torch
 import torch_mlir
 from transformers import (
-    AutoModelForCausalLM,
     AutoTokenizer,
-    pipeline,
     StoppingCriteria,
-    StoppingCriteriaList,
-    TextIteratorStreamer,
 )
-import time
-import numpy as np
-from torch.nn import functional as F
-import os
-from threading import Thread
-from torch.fx.experimental.proxy_tensor import make_fx
-from torch._decomp import get_decompositions
-from typing import List
 from io import BytesIO
 from pathlib import Path
-from shark.shark_downloader import download_public_file
-
-from shark.shark_inference import SharkInference
-from pathlib import Path
+from apps.language_models.utils import (
+    get_torch_mlir_module_bytecode,
+    get_vmfb_from_path,
+)
 
 
 class StopOnTokens(StoppingCriteria):
@@ -51,142 +39,36 @@ def user(message, history):
     return "", history + [[message, ""]]
 
 
-def get_torch_mlir_module_bytecode(model, model_inputs):
-    fx_g = make_fx(
-        model,
-        decomposition_table=get_decompositions(
-            [
-                torch.ops.aten.embedding_dense_backward,
-                torch.ops.aten.native_layer_norm_backward,
-                torch.ops.aten.slice_backward,
-                torch.ops.aten.select_backward,
-                torch.ops.aten.norm.ScalarOpt_dim,
-                torch.ops.aten.native_group_norm,
-                torch.ops.aten.upsample_bilinear2d.vec,
-                torch.ops.aten.split.Tensor,
-                torch.ops.aten.split_with_sizes,
-            ]
-        ),
-        # tracing_mode='symbolic',
-    )(*model_inputs)
-    print("Got FX_G")
-
-    def _remove_nones(fx_g: torch.fx.GraphModule) -> List[int]:
-        removed_indexes = []
-        for node in fx_g.graph.nodes:
-            if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
-                node_arg = node.args[0]
-                if isinstance(node_arg, (list, tuple)):
-                    node_arg = list(node_arg)
-                    node_args_len = len(node_arg)
-                    for i in range(node_args_len):
-                        curr_index = node_args_len - (i + 1)
-                        if node_arg[curr_index] is None:
-                            removed_indexes.append(curr_index)
-                            node_arg.pop(curr_index)
-                    node.args = (tuple(node_arg),)
-                    break
-
-        if len(removed_indexes) > 0:
-            fx_g.graph.lint()
-            fx_g.graph.eliminate_dead_code()
-            fx_g.recompile()
-        removed_indexes.sort()
-        return removed_indexes
-
-    def _unwrap_single_tuple_return(fx_g: torch.fx.GraphModule) -> bool:
-        """
-        Replace tuple with tuple element in functions that return one-element tuples.
-        Returns true if an unwrapping took place, and false otherwise.
-        """
-        unwrapped_tuple = False
-        for node in fx_g.graph.nodes:
-            if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
-                node_arg = node.args[0]
-                if isinstance(node_arg, tuple):
-                    if len(node_arg) == 1:
-                        node.args = (node_arg[0],)
-                        unwrapped_tuple = True
-                        break
-
-        if unwrapped_tuple:
-            fx_g.graph.lint()
-            fx_g.recompile()
-        return unwrapped_tuple
-
-    def transform_fx(fx_g):
-        for node in fx_g.graph.nodes:
-            if node.op == "call_function":
-                if node.target in [
-                    torch.ops.aten.empty,
-                ]:
-                    # aten.empty should be filled with zeros.
-                    if node.target in [torch.ops.aten.empty]:
-                        with fx_g.graph.inserting_after(node):
-                            new_node = fx_g.graph.call_function(
-                                torch.ops.aten.zero_,
-                                args=(node,),
-                            )
-                            node.append(new_node)
-                            node.replace_all_uses_with(new_node)
-                            new_node.args = (node,)
-
-        fx_g.graph.lint()
-
-    transform_fx(fx_g)
-    fx_g.recompile()
-    removed_none_indexes = _remove_nones(fx_g)
-    was_unwrapped = _unwrap_single_tuple_return(fx_g)
-
-    fx_g.graph.set_codegen(torch.fx.graph.CodeGen())
-    fx_g.recompile()
-
-    print("FX_G recompile")
-
-    def strip_overloads(gm):
-        """
-        Modifies the target of graph nodes in :attr:`gm` to strip overloads.
-        Args:
-            gm(fx.GraphModule): The input Fx graph module to be modified
-        """
-        for node in gm.graph.nodes:
-            if isinstance(node.target, torch._ops.OpOverload):
-                node.target = node.target.overloadpacket
-        gm.recompile()
-
-    strip_overloads(fx_g)
-    ts_g = torch.jit.script(fx_g)
-    print("Got TS_G")
-    return ts_g
-
-
-def compile_stableLM(model, model_inputs, model_name, model_vmfb_name):
-    # ADD Device Arg
+def compile_stableLM(
+    model,
+    model_inputs,
+    model_name,
+    model_vmfb_name,
+    device="cuda",
+    precision="fp32",
+):
     from shark.shark_inference import SharkInference
 
-    vmfb_path = Path(model_vmfb_name + ".vmfb")
-    if vmfb_path.exists():
-        print("Loading ", vmfb_path)
-        shark_module = SharkInference(
-            None, device="cuda", mlir_dialect="tm_tensor"
-        )
-        shark_module.load_module(vmfb_path)
-        print("Successfully loaded vmfb")
+    # device = "cuda"  # "cpu"
+    # TODO: vmfb and mlir name should include precision and device
+    vmfb_path = (
+        Path(model_name + f"_{device}.vmfb")
+        if model_vmfb_name is None
+        else Path(model_vmfb_name)
+    )
+    shark_module = get_vmfb_from_path(
+        vmfb_path, device, mlir_dialect="tm_tensor"
+    )
+    if shark_module is not None:
         return shark_module
 
     mlir_path = Path(model_name + ".mlir")
     print(
-        f"[DEBUG] mlir path { mlir_path} {'exists' if mlir_path.exists() else 'does not exist'}"
+        f"[DEBUG] mlir path {mlir_path} {'exists' if mlir_path.exists() else 'does not exist'}"
     )
     if mlir_path.exists():
-        with open(mlir_path) as f:
-            bytecode = f.read("rb")
+        with open(mlir_path, "rb") as f:
+            bytecode = f.read()
     else:
         ts_graph = get_torch_mlir_module_bytecode(model, model_inputs)
         module = torch_mlir.compile(
@@ -205,13 +87,13 @@ def compile_stableLM(model, model_inputs, model_name, model_vmfb_name):
     f_.close()
 
     shark_module = SharkInference(
-        mlir_module=bytecode, device="cuda", mlir_dialect="tm_tensor"
+        mlir_module=bytecode, device=device, mlir_dialect="tm_tensor"
     )
     shark_module.compile()
 
-    import os
-
-    path = shark_module.save_module(os.getcwd(), model_vmfb_name, [])
+    path = shark_module.save_module(
+        vmfb_path.parent.absolute(), vmfb_path.stem
+    )
     print("Saved vmfb at ", str(path))
 
     return shark_module
@@ -244,60 +126,85 @@ def get_tokenizer():
     model_path = "stabilityai/stablelm-tuned-alpha-3b"
     tok = AutoTokenizer.from_pretrained(model_path)
     tok.add_special_tokens({"pad_token": "<PAD>"})
-    print(f"Sucessfully loaded the tokenizer to the memory")
+    print("Sucessfully loaded the tokenizer to the memory")
     return tok
 
 
-# sharkStableLM = compile_stableLM(None, tuple([input_ids, attention_mask]), "stableLM_linalg_f32_seqLen256", "/home/shark/vivek/stableLM_shark_f32_seqLen256")
+# sharkStableLM = compile_stableLM
+# (
+#   None,
+#   tuple([input_ids, attention_mask]),
+#   "stableLM_linalg_f32_seqLen256",
+#   "/home/shark/vivek/stableLM_shark_f32_seqLen256"
+# )
 def generate(
     new_text,
-    streamer,
     max_new_tokens,
-    do_sample,
-    top_p,
-    top_k,
-    temperature,
-    num_beams,
-    stopping_criteria,
     sharkStableLM,
-    tok=None,
-    input_ids=torch.randint(3, (1, 256)),
-    attention_mask=torch.randint(3, (1, 256)),
+    tokenizer=None,
 ):
-    if tok == None:
-        tok = get_tokenizer
-    # Construct the input message string for the model by concatenating the current system message and conversation history
+    if tokenizer is None:
+        tokenizer = get_tokenizer()
+    # Construct the input message string for the model by
+    # concatenating the current system message and conversation history
     # Tokenize the messages string
-    # sharkStableLM = compile_stableLM(None, tuple([input_ids, attention_mask]), "stableLM_linalg_f32_seqLen256", "/home/shark/vivek/stableLM_shark_f32_seqLen256")
+    # sharkStableLM = compile_stableLM
+    # (
+    #   None,
+    #   tuple([input_ids, attention_mask]),
+    #   "stableLM_linalg_f32_seqLen256",
+    #   "/home/shark/vivek/stableLM_shark_f32_seqLen256"
+    # )
     words_list = []
     for i in range(max_new_tokens):
-        numWords = len(new_text.split())
+        # numWords = len(new_text.split())
         # if(numWords>220):
         #  break
-        model_inputs = tok(
-            [new_text],
-            padding="max_length",
-            max_length=MAX_SEQUENCE_LENGTH,
-            truncation=True,
-            return_tensors="pt",
+        params = {
+            "new_text": new_text,
+        }
+        generated_token_op = generate_new_token(
+            sharkStableLM, tokenizer, params
         )
-        sum_attentionmask = torch.sum(model_inputs.attention_mask)
-        # sharkStableLM = compile_stableLM(None, tuple([input_ids, attention_mask]), "stableLM_linalg_f32_seqLen256", "/home/shark/vivek/stableLM_shark_f32_seqLen256")
-        output = sharkStableLM(
-            "forward", [model_inputs.input_ids, model_inputs.attention_mask]
-        )
-        output = torch.from_numpy(output)
-        next_toks = torch.topk(output, 1)
-        if shouldStop(next_toks.indices):
+        detok = generated_token_op["detok"]
+        stop_generation = generated_token_op["stop_generation"]
+        if stop_generation:
             break
-        #        streamer.put(next_toks.indices[0][int(sum_attentionmask)-1])
-        new_word = tok.decode(
-            next_toks.indices[0][int(sum_attentionmask) - 1],
-            skip_special_tokens=True,
-        )
-        print(new_word, end="", flush=True)
-        words_list.append(new_word)
-        if new_word == "":
+        print(detok, end="", flush=True)
+        words_list.append(detok)
+        if detok == "":
             break
-        new_text = new_text + new_word
+        new_text = new_text + detok
     return words_list
+
+
+def generate_new_token(shark_model, tokenizer, params):
+    new_text = params["new_text"]
+    model_inputs = tokenizer(
+        [new_text],
+        padding="max_length",
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+        return_tensors="pt",
+    )
+    sum_attentionmask = torch.sum(model_inputs.attention_mask)
+    # sharkStableLM = compile_stableLM(None, tuple([input_ids, attention_mask]), "stableLM_linalg_f32_seqLen256", "/home/shark/vivek/stableLM_shark_f32_seqLen256")
+    output = shark_model(
+        "forward", [model_inputs.input_ids, model_inputs.attention_mask]
+    )
+    output = torch.from_numpy(output)
+    next_toks = torch.topk(output, 1)
+    stop_generation = False
+    if shouldStop(next_toks.indices):
+        stop_generation = True
+    new_token = next_toks.indices[0][int(sum_attentionmask) - 1]
+    detok = tokenizer.decode(
+        new_token,
+        skip_special_tokens=True,
+    )
+    ret_dict = {
+        "new_token": new_token,
+        "detok": detok,
+        "stop_generation": stop_generation,
+    }
+    return ret_dict

apps/language_models/scripts/standalone_vicuna.py

@@ -1,695 +0,0 @@
-import torch
-import torch_mlir
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from torch.fx.experimental.proxy_tensor import make_fx
-from torch._decomp import get_decompositions
-from typing import List
-from io import BytesIO
-from pathlib import Path
-from shark.shark_downloader import download_public_file
-from shark.shark_importer import transform_fx as transform_fx_
-import re
-
-
-def get_tank_vicuna_mlir(num):
-    # name can be 1 or 2 for first and second vicuna model
-    mname = {1: "FirstVicuna", 2: "SecondVicuna"}
-    tank_url = "gs://shark_tank/FastChat/"
-    download_public_file(tank_url, mname[num])
-    print(f"Downloaded model : {mname[num]} from tank")
-
-
-def get_torch_mlir_module_bytecode(model, model_inputs):
-    fx_g = make_fx(
-        model,
-        decomposition_table=get_decompositions(
-            [
-                torch.ops.aten.embedding_dense_backward,
-                torch.ops.aten.native_layer_norm_backward,
-                torch.ops.aten.slice_backward,
-                torch.ops.aten.select_backward,
-                torch.ops.aten.norm.ScalarOpt_dim,
-                torch.ops.aten.native_group_norm,
-                torch.ops.aten.upsample_bilinear2d.vec,
-                torch.ops.aten.split.Tensor,
-                torch.ops.aten.split_with_sizes,
-            ]
-        ),
-    )(*model_inputs)
-
-    print("Got FX_G")
-
-    def _remove_nones(fx_g: torch.fx.GraphModule) -> List[int]:
-        removed_indexes = []
-        for node in fx_g.graph.nodes:
-            if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
-                node_arg = node.args[0]
-                if isinstance(node_arg, (list, tuple)):
-                    node_arg = list(node_arg)
-                    node_args_len = len(node_arg)
-                    for i in range(node_args_len):
-                        curr_index = node_args_len - (i + 1)
-                        if node_arg[curr_index] is None:
-                            removed_indexes.append(curr_index)
-                            node_arg.pop(curr_index)
-                    node.args = (tuple(node_arg),)
-                    break
-
-        if len(removed_indexes) > 0:
-            fx_g.graph.lint()
-            fx_g.graph.eliminate_dead_code()
-            fx_g.recompile()
-        removed_indexes.sort()
-        return removed_indexes
-
-    def _unwrap_single_tuple_return(fx_g: torch.fx.GraphModule) -> bool:
-        """
-        Replace tuple with tuple element in functions that return one-element tuples.
-        Returns true if an unwrapping took place, and false otherwise.
-        """
-        unwrapped_tuple = False
-        for node in fx_g.graph.nodes:
-            if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
-                node_arg = node.args[0]
-                if isinstance(node_arg, tuple):
-                    if len(node_arg) == 1:
-                        node.args = (node_arg[0],)
-                        unwrapped_tuple = True
-                        break
-
-        if unwrapped_tuple:
-            fx_g.graph.lint()
-            fx_g.recompile()
-        return unwrapped_tuple
-
-    def transform_fx(fx_g):
-        for node in fx_g.graph.nodes:
-            if node.op == "call_function":
-                if node.target in [
-                    torch.ops.aten.empty,
-                ]:
-                    # aten.empty should be filled with zeros.
-                    if node.target in [torch.ops.aten.empty]:
-                        with fx_g.graph.inserting_after(node):
-                            new_node = fx_g.graph.call_function(
-                                torch.ops.aten.zero_,
-                                args=(node,),
-                            )
-                            node.append(new_node)
-                            node.replace_all_uses_with(new_node)
-                            new_node.args = (node,)
-
-        fx_g.graph.lint()
-
-    transform_fx(fx_g)
-    fx_g.recompile()
-    removed_none_indexes = _remove_nones(fx_g)
-    was_unwrapped = _unwrap_single_tuple_return(fx_g)
-
-    fx_g.graph.set_codegen(torch.fx.graph.CodeGen())
-    fx_g.recompile()
-
-    print("FX_G recompile")
-
-    def strip_overloads(gm):
-        """
-        Modifies the target of graph nodes in :attr:`gm` to strip overloads.
-        Args:
-            gm(fx.GraphModule): The input Fx graph module to be modified
-        """
-        for node in gm.graph.nodes:
-            if isinstance(node.target, torch._ops.OpOverload):
-                node.target = node.target.overloadpacket
-        gm.recompile()
-
-    strip_overloads(fx_g)
-    ts_g = torch.jit.script(fx_g)
-    print("Got TS_G")
-
-    return ts_g
-
-
-def compile_vicuna(model, model_inputs, model_name, model_vmfb_name):
-    # ADD Device Arg
-    from shark.shark_inference import SharkInference
-
-    vmfb_path = Path(model_vmfb_name + ".vmfb")
-    if vmfb_path.exists():
-        shark_module = SharkInference(
-            None, device="cuda", mlir_dialect="tm_tensor"
-        )
-        shark_module.load_module(vmfb_path)
-        return shark_module
-
-    mlir_path = Path(model_name + ".mlir")
-    print(
-        f"[DEBUG] mlir path { mlir_path} {'exists' if mlir_path.exists() else 'does not exist'}"
-    )
-    if mlir_path.exists():
-        with open(mlir_path, "rb") as f:
-            bytecode = f.read()
-    else:
-        ts_graph = get_torch_mlir_module_bytecode(model, model_inputs)
-        # model_inputs = list(model_inputs)
-        # model_inputs[0] = torch_mlir.TensorPlaceholder.like(model_inputs[0], dynamic_axes=[1])
-        # model_inputs = tuple(model_inputs)
-        module = torch_mlir.compile(
-            ts_graph,
-            [*model_inputs],
-            torch_mlir.OutputType.LINALG_ON_TENSORS,
-            use_tracing=False,
-            verbose=False,
-        )
-
-        def remove_constant_dim(line):
-            if "19x" in line:
-                line = re.sub("19x", "?x", line)
-                line = re.sub("tensor.empty\(\)", "tensor.empty(%dim)", line)
-            if "tensor.empty" in line and "?x?" in line:
-                line = re.sub(
-                    "tensor.empty\(%dim\)", "tensor.empty(%dim, %dim)", line
-                )
-            if "arith.cmpi" in line:
-                line = re.sub("c19", "dim", line)
-            if " 19," in line:
-                line = re.sub(" 19,", " %dim,", line)
-            return line
-
-        bytecode_stream = BytesIO()
-        module.operation.write_bytecode(bytecode_stream)
-        bytecode = bytecode_stream.getvalue()
-    f_ = open(model_name + ".mlir", "wb")
-    f_.write(bytecode)
-    print("Saved mlir")
-    f_.close()
-
-    shark_module = SharkInference(
-        mlir_module=bytecode, device="cuda", mlir_dialect="tm_tensor"
-    )
-    # shark_module.compile()
-
-    import os
-
-    path = shark_module.save_module(os.getcwd(), model_vmfb_name, [])
-    print("Saved vmfb at ", str(path))
-
-    return shark_module
-
-
-kwargs = {"torch_dtype": torch.float32}  # 16
-model_path = "TheBloke/vicuna-7B-1.1-HF"
-
-
-# Requires input_ids as tensor(1x40)
-class FirstVicuna(torch.nn.Module):
-    def __init__(self, model_path):
-        super().__init__()
-        self.model = AutoModelForCausalLM.from_pretrained(
-            model_path, low_cpu_mem_usage=True, **kwargs
-        )  # .cuda().half()
-
-    def forward(self, input_ids, attention_mask):
-        # input_len = input_id_len
-        # input_ids = input_ids[:,:input_len].reshape([1,input_len])
-        op = self.model(
-            input_ids=input_ids, use_cache=True, attention_mask=attention_mask
-        )
-        return_vals = []
-        return_vals.append(op.logits)
-        temp_past_key_values = op.past_key_values
-        for item in temp_past_key_values:
-            return_vals.append(item[0])
-            return_vals.append(item[1])
-        return tuple(return_vals)
-
-
-# Uncomment this after verifying that SecondVicuna compiles as well.
-# Might have to cast to_numpy.
-
-
-# Requires input_ids as tensor(1x1),
-#          past_key_values = 32 length tuple containing tuple of tensor pairs, which is same as output
-#                            of firstVicuna[1:]
-class SecondVicuna_(torch.nn.Module):
-    def __init__(self, model_path):
-        super().__init__()
-        self.model = AutoModelForCausalLM.from_pretrained(
-            model_path, low_cpu_mem_usage=True, **kwargs
-        )
-
-    def forward(self, input_tuple):
-        # input_ids = input_tuple[0]
-        # input_tuple = torch.unbind(pkv, dim=0)
-        past_key_values = [
-            (
-                input_tuple[i],
-                input_tuple[i + 1],
-            )
-            for i in range(0, len(input_tuple) - 1, 2)
-        ]
-        # for e1, e2 in zip(input_tuple, input_tuple[1:]):
-        #    past_key_values.append(tuple(e1, e2))
-        past_key_values = tuple(past_key_values)
-        op = self.model(
-            input_ids=token, use_cache=True, past_key_values=past_key_values
-        )
-        return_vals = []
-        return_vals.append(op.logits)
-        temp_past_key_values = op.past_key_values
-        for item in temp_past_key_values:
-            return_vals.append(item[0])
-            return_vals.append(item[1])
-        return tuple(return_vals)
-
-
-class SecondVicuna(torch.nn.Module):
-    def __init__(self, model_path):
-        super().__init__()
-        self.model = AutoModelForCausalLM.from_pretrained(
-            model_path, low_cpu_mem_usage=True, **kwargs
-        )  # .cuda().half()
-
-    def forward(
-        self,
-        i0,
-        i1,
-        i2,
-        i3,
-        i4,
-        i5,
-        i6,
-        i7,
-        i8,
-        i9,
-        i10,
-        i11,
-        i12,
-        i13,
-        i14,
-        i15,
-        i16,
-        i17,
-        i18,
-        i19,
-        i20,
-        i21,
-        i22,
-        i23,
-        i24,
-        i25,
-        i26,
-        i27,
-        i28,
-        i29,
-        i30,
-        i31,
-        i32,
-        i33,
-        i34,
-        i35,
-        i36,
-        i37,
-        i38,
-        i39,
-        i40,
-        i41,
-        i42,
-        i43,
-        i44,
-        i45,
-        i46,
-        i47,
-        i48,
-        i49,
-        i50,
-        i51,
-        i52,
-        i53,
-        i54,
-        i55,
-        i56,
-        i57,
-        i58,
-        i59,
-        i60,
-        i61,
-        i62,
-        i63,
-        i64,
-    ):
-        # input_ids = input_tuple[0]
-        # input_tuple = torch.unbind(pkv, dim=0)
-        token = i0
-        past_key_values = (
-            (i1, i2),
-            (
-                i3,
-                i4,
-            ),
-            (
-                i5,
-                i6,
-            ),
-            (
-                i7,
-                i8,
-            ),
-            (
-                i9,
-                i10,
-            ),
-            (
-                i11,
-                i12,
-            ),
-            (
-                i13,
-                i14,
-            ),
-            (
-                i15,
-                i16,
-            ),
-            (
-                i17,
-                i18,
-            ),
-            (
-                i19,
-                i20,
-            ),
-            (
-                i21,
-                i22,
-            ),
-            (
-                i23,
-                i24,
-            ),
-            (
-                i25,
-                i26,
-            ),
-            (
-                i27,
-                i28,
-            ),
-            (
-                i29,
-                i30,
-            ),
-            (
-                i31,
-                i32,
-            ),
-            (
-                i33,
-                i34,
-            ),
-            (
-                i35,
-                i36,
-            ),
-            (
-                i37,
-                i38,
-            ),
-            (
-                i39,
-                i40,
-            ),
-            (
-                i41,
-                i42,
-            ),
-            (
-                i43,
-                i44,
-            ),
-            (
-                i45,
-                i46,
-            ),
-            (
-                i47,
-                i48,
-            ),
-            (
-                i49,
-                i50,
-            ),
-            (
-                i51,
-                i52,
-            ),
-            (
-                i53,
-                i54,
-            ),
-            (
-                i55,
-                i56,
-            ),
-            (
-                i57,
-                i58,
-            ),
-            (
-                i59,
-                i60,
-            ),
-            (
-                i61,
-                i62,
-            ),
-            (
-                i63,
-                i64,
-            ),
-        )
-        # for e1, e2 in zip(input_tuple, input_tuple[1:]):
-        #    past_key_values.append(tuple(e1, e2))
-        op = self.model(
-            input_ids=token, use_cache=True, past_key_values=past_key_values
-        )
-        return_vals = []
-        return_vals.append(op.logits)
-        temp_past_key_values = op.past_key_values
-        for item in temp_past_key_values:
-            return_vals.append(item[0])
-            return_vals.append(item[1])
-        return tuple(return_vals)
-
-
-class wrapper(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-
-    def forward(self, input_ids):
-        pkv = [
-            torch.rand([1, 32, 40, 128], dtype=torch.float32)
-            for _ in range(64)
-        ]
-        return self.model(input_ids, past_key_values=pkv)
-
-
-if __name__ == "__main__":
-    import sys
-
-    vicuna_number = 1
-
-    # input_tuple = (torch.ones([1,1], dtype=torch.int),) + tuple(torch.rand([1, 32, 40, 128], dtype=torch.float32) for _ in range(64))
-    # input_tuple = torch.rand([1,2])
-    # secondVicuna = SecondVicuna(model_path)
-    # shark_second_vicuna = compile_vicuna(secondVicuna, (input_tuple,), "second_vicuna.mlir", "second_vicuna")
-
-    tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
-    # prompt = "INPUT: The SQL command to extract all the users whose name starts with A is:"
-    prompt = "".join(["0" for _ in range(254)])
-    input_ids = tokenizer(prompt).input_ids
-    # print("Got input_ids from the tokenizer")
-
-    if vicuna_number == 1:
-        prompt = input("Enter Prompt: ")
-        prompt = prompt.strip()
-        input_ids = tokenizer(prompt).input_ids
-        original_input_ids = input_ids
-        input_id_len = len(input_ids)
-        pad_len = 256 - input_id_len
-        attention_mask = torch.ones([1, input_id_len], dtype=torch.int64)
-        input_ids = torch.nn.functional.pad(
-            torch.tensor(input_ids), (0, pad_len), mode="constant", value=259
-        )
-        input_ids = input_ids.reshape([1, 256])
-        attention_mask = torch.nn.functional.pad(
-            torch.tensor(attention_mask),
-            (0, pad_len),
-            mode="constant",
-            value=0,
-        )
-
-        firstVicuna = FirstVicuna(model_path)
-
-        prompt2 = "".join(["0" for _ in range(254)])
-        input_ids2 = tokenizer(prompt2).input_ids
-        input_ids2 = torch.tensor(input_ids2).reshape([1, 256])
-        # firstVicunaInput = tuple([torch.as_tensor([input_ids])])#.cuda()
-        # firstVicunaCompileInput = (input_ids2, torch.tensor([input_id_len]))
-        firstVicunaCompileInput = (input_ids2, attention_mask)
-        len_ = int(torch.tensor([input_id_len]))
-        # firstVicunaInput = (input_ids,int(torch.tensor([input_id_len])), )
-        firstVicunaInput = (
-            input_ids,
-            attention_mask,
-        )
-
-        shark_first_vicuna = compile_vicuna(
-            firstVicuna,
-            firstVicunaCompileInput,
-            "first_vicuna",
-            "first_vicuna",
-        )
-        # input_ids = torch.tensor(input_ids)
-
-        # output_first_vicuna = shark_first_vicuna("forward", (input_ids.reshape([1, input_ids.shape[0]]),))
-        output_first_vicuna = shark_first_vicuna("forward", firstVicunaInput)
-        output_first_vicuna_tensor = torch.tensor(output_first_vicuna[1:])
-        torch.save(output_first_vicuna_tensor, "outpt_first_vicuna_tensor.pt")
-        logits_first_vicuna = torch.tensor(output_first_vicuna[0])
-        torch.save(logits_first_vicuna, "logits_first_vicuna_tensor.pt")
-        # output_non_shark_first_vicuna = firstVicuna.forward(firstVicunaInput[0])
-
-        for i in range(40):
-            original_input_ids.append(
-                torch.argmax(logits_first_vicuna[:, len_ + i - 1, :], dim=1)
-            )
-            print(
-                torch.argmax(logits_first_vicuna[:, len_ + i - 1, :], dim=1),
-                tokenizer.decode(
-                    torch.argmax(
-                        logits_first_vicuna[:, len_ + i - 1, :], dim=1
-                    )
-                ),
-            )
-            input_id_len = len(original_input_ids)
-            pad_len = 256 - input_id_len
-            attention_mask = torch.ones([1, input_id_len], dtype=torch.int64)
-            input_ids = torch.nn.functional.pad(
-                torch.tensor(original_input_ids),
-                (0, pad_len),
-                mode="constant",
-                value=259,
-            )
-            input_ids = input_ids.reshape([1, 256])
-            attention_mask = torch.nn.functional.pad(
-                torch.tensor(attention_mask),
-                (0, pad_len),
-                mode="constant",
-                value=0,
-            )
-            firstVicunaInput = (
-                input_ids,
-                attention_mask,
-            )
-            output_first_vicuna = shark_first_vicuna(
-                "forward", firstVicunaInput
-            )
-            output_first_vicuna_tensor = torch.tensor(output_first_vicuna[1:])
-            logits_first_vicuna = torch.tensor(output_first_vicuna[0])
-
-        print(
-            tokenizer.decode(
-                torch.argmax(logits_first_vicuna[:, len_ - 1, :], dim=1)
-            )
-        )
-
-    if vicuna_number == 2:
-        # last_token_logits = output_first_vicuna[0][0][-1]
-        # print("SHARK firstVicuna = ", str(last_token_logits))
-        # print("NonSHARK firstVicuna = ", str(output_non_shark_first_vicuna[0][0][-1]))
-
-        # temperature = 0.7
-        # probs = torch.softmax(torch.tensor(last_token_logits / temperature, dim=-1))
-        # token = torch.tensor(int(torch.multinomial(probs, num_samples=1))).reshape([1,1])
-        # token = torch.ones([1,1], dtype=torch.int64)#.cuda()
-        # pkvt = []
-        # for i in range(64):
-        #    pkvt.append(torch.randn(1, 32, 40, 128, dtype=torch.float32))
-        # pkvt = tuple(pkvt)
-
-        # token = torch.ones([1,1], dtype=torch.int64)#.cuda()
-        output_first_vicuna = torch.load("outpt_first_vicuna_tensor.pt")
-        logits_first_vicuna = torch.load("logits_first_vicuna_tensor.pt")
-        print(logits_first_vicuna.shape)
-
-        for i in range(logits_first_vicuna.shape[1]):
-            token = torch.argmax(
-                torch.tensor(logits_first_vicuna)[:, i, :], dim=1
-            ).reshape([1, 1])
-            print(token, tokenizer.decode(token[0][0]))
-
-        token = torch.argmax(
-            torch.tensor(logits_first_vicuna)[:, 8, :], dim=1
-        ).reshape([1, 1])
-        print(logits_first_vicuna)
-        print(torch.tensor(logits_first_vicuna)[:, -1, :])
-        print(token, tokenizer.decode(token[0][0]))
-
-        result = [tokenizer.decode(token[0][0])]
-
-        pkvt = tuple(torch.tensor(x) for x in output_first_vicuna)
-        # pkv = torch.stack(pkvt, dim=0)
-        secondVicuna = SecondVicuna(model_path)
-        # del shark_first_vicuna
-        # del output_first_vicuna
-        # torch.cuda.empty_cache()
-        shark_second_vicuna = compile_vicuna(
-            secondVicuna, (token,) + pkvt, "second_vicuna", "second_vicuna"
-        )
-
-        print(len(pkvt))
-
-        output_second_vicuna = shark_second_vicuna("forward", (token,) + pkvt)
-
-        import time
-
-        f_ = open("all-logit-outputs.txt", "w+")
-
-        print(output_second_vicuna[0].shape)
-
-        for _ in range(10):
-            f_.write(
-                f"{_}:------------------------------------------------------------------------\n"
-            )
-            t1 = time.time()
-            start_point = output_second_vicuna[1].shape[2] - 256
-            for j in range(output_second_vicuna[0].shape[1]):
-                token_test = torch.argmax(
-                    torch.tensor(output_second_vicuna[0])[:, j, :], dim=1
-                ).reshape([1, 1])
-                sym = token_test, tokenizer.decode(token_test[0][0])
-                f_.write(f"{i}: {token_test} | {sym}")
-            token = torch.argmax(
-                torch.tensor(output_second_vicuna[0])[:, -1, :], dim=1
-            ).reshape([1, 1])
-            # print(token, tokenizer.decode(token[0][0]))
-            result.append(tokenizer.decode(token[0][0]))
-            truncated_outputs = tuple(
-                x[:, :, :256, :] for x in output_second_vicuna[1:]
-            )
-            output_second_vicuna = shark_second_vicuna(
-                "forward", (token,) + truncated_outputs
-            )
-            # print(f"Token Generated in {time.time() - t1} seconds")
-            f_.write("\n")
-
-        f_.close()
-
-        print(result)

apps/language_models/scripts/vicuna.py

@@ -0,0 +1,122 @@
+import argparse
+from pathlib import Path
+from apps.language_models.src.pipelines import vicuna_pipeline as vp
+from apps.language_models.src.pipelines import vicuna_sharded_pipeline as vsp
+import torch
+
+
+parser = argparse.ArgumentParser(
+    prog="vicuna runner",
+    description="runs a vicuna model",
+)
+
+parser.add_argument(
+    "--precision", "-p", default="fp32", help="fp32, fp16, int8, int4"
+)
+parser.add_argument("--device", "-d", default="cuda", help="vulkan, cpu, cuda")
+parser.add_argument(
+    "--first_vicuna_vmfb_path", default=None, help="path to first vicuna vmfb"
+)
+parser.add_argument(
+    "-s",
+    "--sharded",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Run model as sharded",
+)
+# TODO: sharded config
+
+parser.add_argument(
+    "--second_vicuna_vmfb_path",
+    default=None,
+    help="path to second vicuna vmfb",
+)
+parser.add_argument(
+    "--first_vicuna_mlir_path",
+    default=None,
+    help="path to first vicuna mlir file",
+)
+parser.add_argument(
+    "--second_vicuna_mlir_path",
+    default=None,
+    help="path to second vicuna mlir",
+)
+
+parser.add_argument(
+    "--load_mlir_from_shark_tank",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="download precompile mlir from shark tank",
+)
+parser.add_argument(
+    "--cli",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Run model in cli mode",
+)
+
+if __name__ == "__main__":
+    args, unknown = parser.parse_known_args()
+
+    vic = None
+    if not args.sharded:
+        first_vic_mlir_path = (
+            Path(f"first_vicuna_{args.precision}.mlir")
+            if args.first_vicuna_mlir_path is None
+            else Path(args.first_vicuna_mlir_path)
+        )
+        second_vic_mlir_path = (
+            Path(f"second_vicuna_{args.precision}.mlir")
+            if args.second_vicuna_mlir_path is None
+            else Path(args.second_vicuna_mlir_path)
+        )
+        first_vic_vmfb_path = (
+            Path(
+                f"first_vicuna_{args.precision}_{args.device.replace('://', '_')}.vmfb"
+            )
+            if args.first_vicuna_vmfb_path is None
+            else Path(args.first_vicuna_vmfb_path)
+        )
+        second_vic_vmfb_path = (
+            Path(
+                f"second_vicuna_{args.precision}_{args.device.replace('://', '_')}.vmfb"
+            )
+            if args.second_vicuna_vmfb_path is None
+            else Path(args.second_vicuna_vmfb_path)
+        )
+        vic = vp.Vicuna(
+            "vicuna",
+            device=args.device,
+            precision=args.precision,
+            first_vicuna_mlir_path=first_vic_mlir_path,
+            second_vicuna_mlir_path=second_vic_mlir_path,
+            first_vicuna_vmfb_path=first_vic_vmfb_path,
+            second_vicuna_vmfb_path=second_vic_vmfb_path,
+            load_mlir_from_shark_tank=args.load_mlir_from_shark_tank,
+        )
+    else:
+        vic = vsp.Vicuna(
+            "vicuna",
+            device=args.device,
+            precision=args.precision,
+        )
+    prompt_history = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
+    prologue_prompt = "ASSISTANT:\n"
+
+    import gc
+
+    while True:
+        # TODO: Add break condition from user input
+        user_prompt = input("User: ")
+        prompt_history = (
+            prompt_history + "USER:\n" + user_prompt + prologue_prompt
+        )
+        prompt = prompt_history.strip()
+        res_str = vic.generate(prompt, cli=True)
+        torch.cuda.empty_cache()
+        gc.collect()
+        print(
+            "\n-----\nAssistant: Here's the complete formatted reply:\n",
+            res_str,
+        )
+        prompt_history += f"\n{res_str}\n"

apps/language_models/src/model_wrappers/falcon_model.py

@@ -0,0 +1,22 @@
+import torch
+
+
+class FalconModel(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, input_ids, attention_mask):
+        input_dict = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "past_key_values": None,
+            "use_cache": True,
+        }
+        output = self.model(
+            **input_dict,
+            return_dict=True,
+            output_attentions=False,
+            output_hidden_states=False,
+        )[0]
+        return output[:, -1, :]

apps/language_models/src/model_wrappers/stablelm_model.py

@@ -0,0 +1,15 @@
+import torch
+
+
+class StableLMModel(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, input_ids, attention_mask):
+        combine_input_dict = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
+        output = self.model(**combine_input_dict)
+        return output.logits

apps/language_models/src/model_wrappers/vicuna_model.py

@@ -0,0 +1,261 @@
+import torch
+from transformers import AutoModelForCausalLM
+
+
+class FirstVicuna(torch.nn.Module):
+    def __init__(self, model_path):
+        super().__init__()
+        kwargs = {"torch_dtype": torch.float32}
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_path, low_cpu_mem_usage=True, **kwargs
+        )
+
+    def forward(self, input_ids):
+        op = self.model(input_ids=input_ids, use_cache=True)
+        return_vals = []
+        return_vals.append(op.logits)
+        temp_past_key_values = op.past_key_values
+        for item in temp_past_key_values:
+            return_vals.append(item[0])
+            return_vals.append(item[1])
+        return tuple(return_vals)
+
+
+class SecondVicuna(torch.nn.Module):
+    def __init__(self, model_path):
+        super().__init__()
+        kwargs = {"torch_dtype": torch.float32}
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_path, low_cpu_mem_usage=True, **kwargs
+        )
+
+    def forward(
+        self,
+        i0,
+        i1,
+        i2,
+        i3,
+        i4,
+        i5,
+        i6,
+        i7,
+        i8,
+        i9,
+        i10,
+        i11,
+        i12,
+        i13,
+        i14,
+        i15,
+        i16,
+        i17,
+        i18,
+        i19,
+        i20,
+        i21,
+        i22,
+        i23,
+        i24,
+        i25,
+        i26,
+        i27,
+        i28,
+        i29,
+        i30,
+        i31,
+        i32,
+        i33,
+        i34,
+        i35,
+        i36,
+        i37,
+        i38,
+        i39,
+        i40,
+        i41,
+        i42,
+        i43,
+        i44,
+        i45,
+        i46,
+        i47,
+        i48,
+        i49,
+        i50,
+        i51,
+        i52,
+        i53,
+        i54,
+        i55,
+        i56,
+        i57,
+        i58,
+        i59,
+        i60,
+        i61,
+        i62,
+        i63,
+        i64,
+    ):
+        # input_ids = input_tuple[0]
+        # input_tuple = torch.unbind(pkv, dim=0)
+        token = i0
+        past_key_values = (
+            (i1, i2),
+            (
+                i3,
+                i4,
+            ),
+            (
+                i5,
+                i6,
+            ),
+            (
+                i7,
+                i8,
+            ),
+            (
+                i9,
+                i10,
+            ),
+            (
+                i11,
+                i12,
+            ),
+            (
+                i13,
+                i14,
+            ),
+            (
+                i15,
+                i16,
+            ),
+            (
+                i17,
+                i18,
+            ),
+            (
+                i19,
+                i20,
+            ),
+            (
+                i21,
+                i22,
+            ),
+            (
+                i23,
+                i24,
+            ),
+            (
+                i25,
+                i26,
+            ),
+            (
+                i27,
+                i28,
+            ),
+            (
+                i29,
+                i30,
+            ),
+            (
+                i31,
+                i32,
+            ),
+            (
+                i33,
+                i34,
+            ),
+            (
+                i35,
+                i36,
+            ),
+            (
+                i37,
+                i38,
+            ),
+            (
+                i39,
+                i40,
+            ),
+            (
+                i41,
+                i42,
+            ),
+            (
+                i43,
+                i44,
+            ),
+            (
+                i45,
+                i46,
+            ),
+            (
+                i47,
+                i48,
+            ),
+            (
+                i49,
+                i50,
+            ),
+            (
+                i51,
+                i52,
+            ),
+            (
+                i53,
+                i54,
+            ),
+            (
+                i55,
+                i56,
+            ),
+            (
+                i57,
+                i58,
+            ),
+            (
+                i59,
+                i60,
+            ),
+            (
+                i61,
+                i62,
+            ),
+            (
+                i63,
+                i64,
+            ),
+        )
+        op = self.model(
+            input_ids=token, use_cache=True, past_key_values=past_key_values
+        )
+        return_vals = []
+        return_vals.append(op.logits)
+        temp_past_key_values = op.past_key_values
+        for item in temp_past_key_values:
+            return_vals.append(item[0])
+            return_vals.append(item[1])
+        return tuple(return_vals)
+
+
+class CombinedModel(torch.nn.Module):
+    def __init__(
+        self,
+        first_vicuna_model_path="TheBloke/vicuna-7B-1.1-HF",
+        second_vicuna_model_path="TheBloke/vicuna-7B-1.1-HF",
+    ):
+        super().__init__()
+        self.first_vicuna = FirstVicuna(first_vicuna_model_path)
+        self.second_vicuna = SecondVicuna(second_vicuna_model_path)
+
+    def forward(self, input_ids):
+        first_output = self.first_vicuna(input_ids=input_ids, use_cache=True)
+        logits = first_output[0]
+        pkv = first_output[1:]
+
+        token = torch.argmax(torch.tensor(logits)[:, -1, :], dim=1)
+        token = token.to(torch.int64).reshape([1, 1])
+        secondVicunaInput = (token,) + tuple(pkv)
+        second_output = self.second_vicuna(secondVicunaInput)
+        return second_output

apps/language_models/src/model_wrappers/vicuna_sharded_model.py

@@ -0,0 +1,178 @@
+import torch
+
+
+class FirstVicunaLayer(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, hidden_states, attention_mask, position_ids):
+        outputs = self.model(
+            hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            use_cache=True,
+        )
+        next_hidden_states = outputs[0]
+        past_key_value_out0, past_key_value_out1 = (
+            outputs[-1][0],
+            outputs[-1][1],
+        )
+
+        return (
+            next_hidden_states,
+            past_key_value_out0,
+            past_key_value_out1,
+        )
+
+
+class SecondVicunaLayer(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        position_ids,
+        past_key_value0,
+        past_key_value1,
+    ):
+        outputs = self.model(
+            hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=(
+                past_key_value0,
+                past_key_value1,
+            ),
+            use_cache=True,
+        )
+        next_hidden_states = outputs[0]
+        past_key_value_out0, past_key_value_out1 = (
+            outputs[-1][0],
+            outputs[-1][1],
+        )
+
+        return (
+            next_hidden_states,
+            past_key_value_out0,
+            past_key_value_out1,
+        )
+
+
+class CompiledFirstVicunaLayer(torch.nn.Module):
+    def __init__(self, shark_module):
+        super().__init__()
+        self.model = shark_module
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        position_ids,
+        past_key_value=None,
+        output_attentions=False,
+        use_cache=True,
+    ):
+        hidden_states = hidden_states.detach()
+        attention_mask = attention_mask.detach()
+        position_ids = position_ids.detach()
+        output = self.model(
+            "forward",
+            (
+                hidden_states,
+                attention_mask,
+                position_ids,
+            ),
+        )
+
+        output0 = torch.tensor(output[0])
+        output1 = torch.tensor(output[1])
+        output2 = torch.tensor(output[2])
+
+        return (
+            output0,
+            (
+                output1,
+                output2,
+            ),
+        )
+
+
+class CompiledSecondVicunaLayer(torch.nn.Module):
+    def __init__(self, shark_module):
+        super().__init__()
+        self.model = shark_module
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        position_ids,
+        past_key_value,
+        output_attentions=False,
+        use_cache=True,
+    ):
+        hidden_states = hidden_states.detach()
+        attention_mask = attention_mask.detach()
+        position_ids = position_ids.detach()
+        pkv0 = past_key_value[0].detach()
+        pkv1 = past_key_value[1].detach()
+        output = self.model(
+            "forward",
+            (
+                hidden_states,
+                attention_mask,
+                position_ids,
+                pkv0,
+                pkv1,
+            ),
+        )
+
+        output0 = torch.tensor(output[0])
+        output1 = torch.tensor(output[1])
+        output2 = torch.tensor(output[2])
+
+        return (
+            output0,
+            (
+                output1,
+                output2,
+            ),
+        )
+
+
+class ShardedVicunaModel(torch.nn.Module):
+    def __init__(self, model, layers0, layers1):
+        super().__init__()
+        self.model = model
+        assert len(layers0) == len(model.model.layers)
+        # self.model.model.layers = torch.nn.modules.container.ModuleList(layers0)
+        self.model.model.config.use_cache = True
+        self.model.model.config.output_attentions = False
+        self.layers0 = layers0
+        self.layers1 = layers1
+
+    def forward(
+        self,
+        input_ids,
+        is_first=True,
+        past_key_values=None,
+        attention_mask=None,
+    ):
+        if is_first:
+            self.model.model.layers = torch.nn.modules.container.ModuleList(
+                self.layers0
+            )
+            return self.model.forward(input_ids, attention_mask=attention_mask)
+        else:
+            self.model.model.layers = torch.nn.modules.container.ModuleList(
+                self.layers1
+            )
+            return self.model.forward(
+                input_ids,
+                attention_mask=attention_mask,
+                past_key_values=past_key_values,
+            )

apps/language_models/src/pipelines/SharkLLMBase.py

@@ -0,0 +1,41 @@
+from abc import ABC, abstractmethod
+
+
+class SharkLLMBase(ABC):
+    def __init__(
+        self, model_name, hf_model_path=None, max_num_tokens=512
+    ) -> None:
+        self.model_name = model_name
+        self.hf_model_path = hf_model_path
+        self.max_num_tokens = max_num_tokens
+        self.shark_model = None
+        self.device = "cpu"
+        self.precision = "fp32"
+
+    @classmethod
+    @abstractmethod
+    def compile(self):
+        pass
+
+    @classmethod
+    @abstractmethod
+    def generate(self, prompt):
+        pass
+
+    @classmethod
+    @abstractmethod
+    def generate_new_token(self, params):
+        pass
+
+    @classmethod
+    @abstractmethod
+    def get_tokenizer(self):
+        pass
+
+    @classmethod
+    @abstractmethod
+    def get_src_model(self):
+        pass
+
+    def load_init_from_config(self):
+        pass

apps/language_models/src/pipelines/falcon_pipeline.py

@@ -0,0 +1,473 @@
+from apps.language_models.src.model_wrappers.falcon_model import FalconModel
+from apps.language_models.src.pipelines.SharkLLMBase import SharkLLMBase
+from apps.language_models.utils import (
+    get_vmfb_from_path,
+)
+from io import BytesIO
+from pathlib import Path
+from contextlib import redirect_stdout
+from shark.shark_downloader import download_public_file
+from shark.shark_importer import import_with_fx
+from shark.shark_inference import SharkInference
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers.generation import (
+    GenerationConfig,
+    LogitsProcessorList,
+    StoppingCriteriaList,
+)
+import copy
+
+import re
+import torch
+import torch_mlir
+import os
+import argparse
+
+parser = argparse.ArgumentParser(
+    prog="falcon runner",
+    description="runs a falcon model",
+)
+
+parser.add_argument(
+    "--precision", "-p", default="fp32", help="fp32, fp16, int8, int4"
+)
+parser.add_argument("--device", "-d", default="cuda", help="vulkan, cpu, cuda")
+parser.add_argument(
+    "--falcon_vmfb_path", default=None, help="path to falcon's vmfb"
+)
+parser.add_argument(
+    "--falcon_mlir_path",
+    default=None,
+    help="path to falcon's mlir file",
+)
+
+parser.add_argument(
+    "--load_mlir_from_shark_tank",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="download precompile mlir from shark tank",
+)
+parser.add_argument(
+    "--cli",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="Run model in cli mode",
+)
+
+
+class Falcon(SharkLLMBase):
+    def __init__(
+        self,
+        model_name,
+        hf_model_path="tiiuae/falcon-7b-instruct",
+        max_num_tokens=150,
+        device="cuda",
+        precision="fp32",
+        falcon_mlir_path=Path("falcon.mlir"),
+        falcon_vmfb_path=Path("falcon.vmfb"),
+    ) -> None:
+        super().__init__(model_name, hf_model_path, max_num_tokens)
+        self.max_padding_length = 100
+        self.device = device
+        self.precision = precision
+        self.falcon_vmfb_path = falcon_vmfb_path
+        self.falcon_mlir_path = falcon_mlir_path
+        self.tokenizer = self.get_tokenizer()
+        self.shark_model = self.compile()
+        self.src_model = self.get_src_model()
+
+    def get_tokenizer(self):
+        tokenizer = AutoTokenizer.from_pretrained(
+            self.hf_model_path, trust_remote_code=True
+        )
+        tokenizer.padding_side = "left"
+        tokenizer.pad_token_id = 11
+        return tokenizer
+
+    def get_src_model(self):
+        print("Loading src model")
+        kwargs = {"torch_dtype": torch.float, "trust_remote_code": True}
+        falcon_model = AutoModelForCausalLM.from_pretrained(
+            self.hf_model_path, **kwargs
+        )
+        return falcon_model
+
+    def compile_falcon(self):
+        vmfb = get_vmfb_from_path(self.falcon_vmfb_path, self.device, "linalg")
+        if vmfb is not None:
+            return vmfb
+
+        print(
+            f"[DEBUG] vmfb not found at {self.falcon_vmfb_path.absolute()}. Trying to work with"
+            f"[DEBUG] mlir path { self.falcon_mlir_path} {'exists' if self.falcon_mlir_path.exists() else 'does not exist'}"
+        )
+        if self.falcon_mlir_path.exists():
+            with open(self.falcon_mlir_path, "rb") as f:
+                bytecode = f.read()
+        else:
+            mlir_generated = False
+            if args.load_mlir_from_shark_tank:
+                if self.precision == "fp32":
+                    # download MLIR from shark_tank for fp32
+                    download_public_file(
+                        "gs://shark_tank/falcon/7b/cuda/falcon.mlir",
+                        self.falcon_mlir_path.absolute(),
+                        single_file=True,
+                    )
+                    if self.falcon_mlir_path.exists():
+                        with open(self.falcon_mlir_path, "rb") as f:
+                            bytecode = f.read()
+                        mlir_generated = True
+                    else:
+                        raise ValueError(
+                            f"MLIR not found at {self.falcon_mlir_path.absolute()}"
+                            " after downloading! Please check path and try again"
+                        )
+                else:
+                    print(
+                        "Only fp32 mlir added to tank, generating mlir on device."
+                    )
+
+            if not mlir_generated:
+                compilation_input_ids = torch.randint(
+                    low=1, high=10000, size=(1, 100)
+                )
+                compilation_attention_mask = torch.ones(
+                    1, 100, dtype=torch.int64
+                )
+                falconCompileInput = (
+                    compilation_input_ids,
+                    compilation_attention_mask,
+                )
+                model = FalconModel(self.src_model)
+
+                print(f"[DEBUG] generating torchscript graph")
+                ts_graph = import_with_fx(
+                    model,
+                    falconCompileInput,
+                    is_f16=self.precision == "fp16",
+                    f16_input_mask=[False, False],
+                    mlir_type="torchscript",
+                )
+                del model
+                print(f"[DEBUG] generating torch mlir")
+
+                module = torch_mlir.compile(
+                    ts_graph,
+                    [*falconCompileInput],
+                    torch_mlir.OutputType.LINALG_ON_TENSORS,
+                    use_tracing=False,
+                    verbose=False,
+                )
+                del ts_graph
+
+                print(f"[DEBUG] converting to bytecode")
+                bytecode_stream = BytesIO()
+                module.operation.write_bytecode(bytecode_stream)
+                bytecode = bytecode_stream.getvalue()
+                del module
+
+                print(f"[DEBUG] writing mlir to file")
+                with open(f"{self.model_name}.mlir", "wb") as f_:
+                    with redirect_stdout(f_):
+                        print(module.operation.get_asm())
+                f_.close()
+
+        shark_module = SharkInference(
+            mlir_module=bytecode, device=self.device, mlir_dialect="linalg"
+        )
+        path = shark_module.save_module(
+            self.falcon_vmfb_path.parent.absolute(),
+            self.falcon_vmfb_path.stem,
+            extra_args=[
+                "--iree-hal-dump-executable-sources-to=ies",
+                "--iree-vm-target-truncate-unsupported-floats",
+                "--iree-codegen-check-ir-before-llvm-conversion=false",
+                "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
+            ],
+        )
+        print("Saved falcon vmfb at ", str(path))
+        shark_module.load_module(path)
+
+        return shark_module
+
+    def compile(self):
+        if (
+            not self.falcon_vmfb_path.exists()
+            and self.device == "cuda"
+            and self.precision == "fp32"
+        ):
+            download_public_file(
+                "gs://shark_tank/falcon/7b/cuda/falcon.vmfb",
+                self.falcon_vmfb_path.absolute(),
+                single_file=True,
+            )
+
+        falcon_shark_model = self.compile_falcon()
+        return falcon_shark_model
+
+    def generate(self, prompt):
+        model_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.max_padding_length,
+            add_special_tokens=False,
+            return_tensors="pt",
+        )
+        model_inputs["prompt_text"] = prompt
+
+        input_ids = model_inputs["input_ids"]
+        attention_mask = model_inputs.get("attention_mask", None)
+
+        # Allow empty prompts
+        if input_ids.shape[1] == 0:
+            input_ids = None
+            attention_mask = None
+            in_b = 1
+        else:
+            in_b = input_ids.shape[0]
+
+        generate_kwargs = {
+            "max_length": self.max_num_tokens,
+            "do_sample": True,
+            "top_k": 10,
+            "num_return_sequences": 1,
+            "eos_token_id": 11,
+        }
+        generate_kwargs["input_ids"] = input_ids
+        generate_kwargs["attention_mask"] = attention_mask
+        generation_config_ = GenerationConfig.from_model_config(
+            self.src_model.config
+        )
+        generation_config = copy.deepcopy(generation_config_)
+        model_kwargs = generation_config.update(**generate_kwargs)
+
+        logits_processor = LogitsProcessorList()
+        stopping_criteria = StoppingCriteriaList()
+
+        eos_token_id = generation_config.eos_token_id
+        generation_config.pad_token_id = eos_token_id
+
+        (
+            inputs_tensor,
+            model_input_name,
+            model_kwargs,
+        ) = self.src_model._prepare_model_inputs(
+            None, generation_config.bos_token_id, model_kwargs
+        )
+        batch_size = inputs_tensor.shape[0]
+
+        model_kwargs["output_attentions"] = generation_config.output_attentions
+        model_kwargs[
+            "output_hidden_states"
+        ] = generation_config.output_hidden_states
+        model_kwargs["use_cache"] = generation_config.use_cache
+
+        input_ids = (
+            inputs_tensor
+            if model_input_name == "input_ids"
+            else model_kwargs.pop("input_ids")
+        )
+
+        self.logits_processor = self.src_model._get_logits_processor(
+            generation_config=generation_config,
+            input_ids_seq_length=input_ids.shape[-1],
+            encoder_input_ids=inputs_tensor,
+            prefix_allowed_tokens_fn=None,
+            logits_processor=logits_processor,
+        )
+
+        self.stopping_criteria = self.src_model._get_stopping_criteria(
+            generation_config=generation_config,
+            stopping_criteria=stopping_criteria,
+        )
+
+        self.logits_warper = self.src_model._get_logits_warper(
+            generation_config
+        )
+
+        (
+            self.input_ids,
+            self.model_kwargs,
+        ) = self.src_model._expand_inputs_for_generation(
+            input_ids=input_ids,
+            expand_size=generation_config.num_return_sequences,  # 1
+            is_encoder_decoder=self.src_model.config.is_encoder_decoder,  # False
+            **model_kwargs,
+        )
+
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        self.eos_token_id_tensor = (
+            torch.tensor(eos_token_id) if eos_token_id is not None else None
+        )
+
+        self.pad_token_id = generation_config.pad_token_id
+        self.eos_token_id = eos_token_id
+
+        output_scores = generation_config.output_scores  # False
+        output_attentions = generation_config.output_attentions  # False
+        output_hidden_states = generation_config.output_hidden_states  # False
+        return_dict_in_generate = (
+            generation_config.return_dict_in_generate  # False
+        )
+
+        # init attention / hidden states / scores tuples
+        self.scores = (
+            () if (return_dict_in_generate and output_scores) else None
+        )
+        decoder_attentions = (
+            () if (return_dict_in_generate and output_attentions) else None
+        )
+        cross_attentions = (
+            () if (return_dict_in_generate and output_attentions) else None
+        )
+        decoder_hidden_states = (
+            () if (return_dict_in_generate and output_hidden_states) else None
+        )
+
+        # keep track of which sequences are already finished
+        self.unfinished_sequences = torch.ones(
+            input_ids.shape[0], dtype=torch.long, device=input_ids.device
+        )
+
+        all_text = prompt
+
+        for i in range(self.max_num_tokens - 1):
+            next_token = self.generate_new_token()
+            new_word = self.tokenizer.decode(
+                next_token.cpu().numpy(),
+                add_special_tokens=False,
+                skip_special_tokens=True,
+                clean_up_tokenization_spaces=True,
+            )
+
+            all_text = all_text + new_word
+
+            print(f"{new_word}", end="", flush=True)
+
+            # if eos_token was found in one sentence, set sentence to finished
+            if self.eos_token_id_tensor is not None:
+                self.unfinished_sequences = self.unfinished_sequences.mul(
+                    next_token.tile(self.eos_token_id_tensor.shape[0], 1)
+                    .ne(self.eos_token_id_tensor.unsqueeze(1))
+                    .prod(dim=0)
+                )
+                # stop when each sentence is finished
+                if (
+                    self.unfinished_sequences.max() == 0
+                    or self.stopping_criteria(input_ids, self.scores)
+                ):
+                    break
+
+        torch.cuda.empty_cache()
+        gc.collect()
+
+        return all_text
+
+    def generate_new_token(self):
+        model_inputs = self.src_model.prepare_inputs_for_generation(
+            self.input_ids, **self.model_kwargs
+        )
+        outputs = torch.from_numpy(
+            self.shark_model(
+                "forward",
+                (model_inputs["input_ids"], model_inputs["attention_mask"]),
+            )
+        )
+        next_token_logits = outputs
+
+        # pre-process distribution
+        next_token_scores = self.logits_processor(
+            self.input_ids, next_token_logits
+        )
+        next_token_scores = self.logits_warper(
+            self.input_ids, next_token_scores
+        )
+
+        # sample
+        probs = torch.nn.functional.softmax(next_token_scores, dim=-1)
+
+        next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
+
+        # finished sentences should have their next token be a padding token
+        if self.eos_token_id is not None:
+            if self.pad_token_id is None:
+                raise ValueError(
+                    "If `eos_token_id` is defined, make sure that `pad_token_id` is defined."
+                )
+            next_token = (
+                next_token * self.unfinished_sequences
+                + self.pad_token_id * (1 - self.unfinished_sequences)
+            )
+
+        self.input_ids = torch.cat(
+            [self.input_ids, next_token[:, None]], dim=-1
+        )
+
+        self.model_kwargs["past_key_values"] = None
+        if "attention_mask" in self.model_kwargs:
+            attention_mask = self.model_kwargs["attention_mask"]
+            self.model_kwargs["attention_mask"] = torch.cat(
+                [
+                    attention_mask,
+                    attention_mask.new_ones((attention_mask.shape[0], 1)),
+                ],
+                dim=-1,
+            )
+
+        self.input_ids = self.input_ids[:, 1:]
+        self.model_kwargs["attention_mask"] = self.model_kwargs[
+            "attention_mask"
+        ][:, 1:]
+
+        return next_token
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+
+    falcon_mlir_path = (
+        Path("falcon.mlir")
+        if args.falcon_mlir_path is None
+        else Path(args.falcon_mlir_path)
+    )
+    falcon_vmfb_path = (
+        Path("falcon.vmfb")
+        if args.falcon_vmfb_path is None
+        else Path(args.falcon_vmfb_path)
+    )
+
+    falcon = Falcon(
+        "falcon",
+        device=args.device,
+        precision=args.precision,
+        falcon_mlir_path=falcon_mlir_path,
+        falcon_vmfb_path=falcon_vmfb_path,
+    )
+
+    import gc
+
+    default_prompt_text = "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:"
+    continue_execution = True
+
+    while continue_execution:
+        use_default_prompt = input(
+            "\nDo you wish to use the default prompt text? True or False?: "
+        )
+        if use_default_prompt:
+            prompt = default_prompt_text
+        else:
+            prompt = input("Please enter the prompt text: ")
+        print("\nPrompt Text: ", prompt)
+
+        res_str = falcon.generate(prompt)
+        torch.cuda.empty_cache()
+        gc.collect()
+        print(
+            "\n\n-----\nHere's the complete formatted result: \n\n",
+            res_str,
+        )
+        continue_execution = input(
+            "\nDo you wish to run script one more time? True or False?: "
+        )

apps/language_models/src/pipelines/stablelm_pipeline.py

@@ -0,0 +1,185 @@
+import torch
+import torch_mlir
+from transformers import AutoTokenizer, StoppingCriteria, AutoModelForCausalLM
+from io import BytesIO
+from pathlib import Path
+from apps.language_models.utils import (
+    get_torch_mlir_module_bytecode,
+    get_vmfb_from_path,
+)
+from apps.language_models.src.pipelines.SharkLLMBase import SharkLLMBase
+from apps.language_models.src.model_wrappers.stablelm_model import (
+    StableLMModel,
+)
+
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(
+        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
+    ) -> bool:
+        stop_ids = [50278, 50279, 50277, 1, 0]
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:
+                return True
+        return False
+
+
+class SharkStableLM(SharkLLMBase):
+    def __init__(
+        self,
+        model_name,
+        hf_model_path="stabilityai/stablelm-tuned-alpha-3b",
+        max_num_tokens=512,
+        device="cuda",
+        precision="fp32",
+    ) -> None:
+        super().__init__(model_name, hf_model_path, max_num_tokens)
+        self.max_sequence_len = 256
+        self.device = device
+        self.precision = precision
+        self.tokenizer = self.get_tokenizer()
+        self.shark_model = self.compile()
+
+    def shouldStop(self, tokens):
+        stop_ids = [50278, 50279, 50277, 1, 0]
+        for stop_id in stop_ids:
+            if tokens[0][-1] == stop_id:
+                return True
+        return False
+
+    def get_src_model(self):
+        model = AutoModelForCausalLM.from_pretrained(
+            self.hf_model_path, torch_dtype=torch.float32
+        )
+        return model
+
+    def get_model_inputs(self):
+        input_ids = torch.randint(3, (1, self.max_sequence_len))
+        attention_mask = torch.randint(3, (1, self.max_sequence_len))
+        return input_ids, attention_mask
+
+    def compile(self):
+        tmp_model_name = (
+            f"stableLM_linalg_{self.precision}_seqLen{self.max_sequence_len}"
+        )
+
+        # device = "cuda"  # "cpu"
+        # TODO: vmfb and mlir name should include precision and device
+        model_vmfb_name = None
+        vmfb_path = (
+            Path(tmp_model_name + f"_{self.device}.vmfb")
+            if model_vmfb_name is None
+            else Path(model_vmfb_name)
+        )
+        shark_module = get_vmfb_from_path(
+            vmfb_path, self.device, mlir_dialect="tm_tensor"
+        )
+        if shark_module is not None:
+            return shark_module
+
+        mlir_path = Path(tmp_model_name + ".mlir")
+        print(
+            f"[DEBUG] mlir path {mlir_path} {'exists' if mlir_path.exists() else 'does not exist'}"
+        )
+        if mlir_path.exists():
+            with open(mlir_path, "rb") as f:
+                bytecode = f.read()
+        else:
+            model = StableLMModel(self.get_src_model())
+            model_inputs = self.get_model_inputs()
+            ts_graph = get_torch_mlir_module_bytecode(model, model_inputs)
+            module = torch_mlir.compile(
+                ts_graph,
+                [*model_inputs],
+                torch_mlir.OutputType.LINALG_ON_TENSORS,
+                use_tracing=False,
+                verbose=False,
+            )
+            bytecode_stream = BytesIO()
+            module.operation.write_bytecode(bytecode_stream)
+            bytecode = bytecode_stream.getvalue()
+        f_ = open(tmp_model_name + ".mlir", "wb")
+        f_.write(bytecode)
+        print("Saved mlir")
+        f_.close()
+
+        from shark.shark_inference import SharkInference
+
+        shark_module = SharkInference(
+            mlir_module=bytecode, device=self.device, mlir_dialect="tm_tensor"
+        )
+        shark_module.compile()
+
+        path = shark_module.save_module(
+            vmfb_path.parent.absolute(), vmfb_path.stem
+        )
+        print("Saved vmfb at ", str(path))
+
+        return shark_module
+
+    def get_tokenizer(self):
+        tok = AutoTokenizer.from_pretrained(self.hf_model_path)
+        tok.add_special_tokens({"pad_token": "<PAD>"})
+        # print("[DEBUG] Sucessfully loaded the tokenizer to the memory")
+        return tok
+
+    def generate(self, prompt):
+        words_list = []
+        for i in range(self.max_num_tokens):
+            params = {
+                "new_text": prompt,
+            }
+
+            generated_token_op = self.generate_new_token(params)
+
+            detok = generated_token_op["detok"]
+            stop_generation = generated_token_op["stop_generation"]
+
+            if stop_generation:
+                break
+
+            print(detok, end="", flush=True)  # this is for CLI and DEBUG
+            words_list.append(detok)
+            if detok == "":
+                break
+            prompt = prompt + detok
+        return words_list
+
+    def generate_new_token(self, params):
+        new_text = params["new_text"]
+        model_inputs = self.tokenizer(
+            [new_text],
+            padding="max_length",
+            max_length=self.max_sequence_len,
+            truncation=True,
+            return_tensors="pt",
+        )
+        sum_attentionmask = torch.sum(model_inputs.attention_mask)
+        output = self.shark_model(
+            "forward", [model_inputs.input_ids, model_inputs.attention_mask]
+        )
+        output = torch.from_numpy(output)
+        next_toks = torch.topk(output, 1)
+        stop_generation = False
+        if self.shouldStop(next_toks.indices):
+            stop_generation = True
+        new_token = next_toks.indices[0][int(sum_attentionmask) - 1]
+        detok = self.tokenizer.decode(
+            new_token,
+            skip_special_tokens=True,
+        )
+        ret_dict = {
+            "new_token": new_token,
+            "detok": detok,
+            "stop_generation": stop_generation,
+        }
+        return ret_dict
+
+
+# Initialize a StopOnTokens object
+system_prompt = """<|SYSTEM|># StableLM Tuned (Alpha version)
+- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
+- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
+- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
+- StableLM will refuse to participate in anything that could harm a human.
+"""

apps/language_models/src/pipelines/vicuna_pipeline.py

@@ -0,0 +1,559 @@
+from apps.language_models.src.model_wrappers.vicuna_model import (
+    FirstVicuna,
+    SecondVicuna,
+)
+from apps.language_models.src.pipelines.SharkLLMBase import SharkLLMBase
+from apps.language_models.utils import (
+    get_vmfb_from_path,
+)
+
+from io import BytesIO
+from pathlib import Path
+from shark.shark_downloader import download_public_file
+from shark.shark_importer import import_with_fx
+from shark.shark_inference import SharkInference
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+import re
+import torch
+import torch_mlir
+import os
+
+
+class Vicuna(SharkLLMBase):
+    def __init__(
+        self,
+        model_name,
+        hf_model_path="TheBloke/vicuna-7B-1.1-HF",
+        max_num_tokens=512,
+        device="cuda",
+        precision="fp32",
+        first_vicuna_mlir_path=Path("first_vicuna.mlir"),
+        second_vicuna_mlir_path=Path("second_vicuna.mlir"),
+        first_vicuna_vmfb_path=Path("first_vicuna.vmfb"),
+        second_vicuna_vmfb_path=Path("second_vicuna.vmfb"),
+        load_mlir_from_shark_tank=True,
+    ) -> None:
+        super().__init__(model_name, hf_model_path, max_num_tokens)
+        self.max_sequence_length = 256
+        self.device = device
+        self.precision = precision
+        self.first_vicuna_vmfb_path = first_vicuna_vmfb_path
+        self.second_vicuna_vmfb_path = second_vicuna_vmfb_path
+        self.first_vicuna_mlir_path = first_vicuna_mlir_path
+        self.second_vicuna_mlir_path = second_vicuna_mlir_path
+        self.tokenizer = self.get_tokenizer()
+        self.shark_model = self.compile()
+        self.load_mlir_from_shark_tank = load_mlir_from_shark_tank
+
+    def get_tokenizer(self):
+        tokenizer = AutoTokenizer.from_pretrained(
+            self.hf_model_path, use_fast=False
+        )
+        return tokenizer
+
+    def get_src_model(self):
+        kwargs = {"torch_dtype": torch.float}
+        vicuna_model = AutoModelForCausalLM.from_pretrained(
+            self.hf_model_path, **kwargs
+        )
+        return vicuna_model
+
+    def compile_first_vicuna(self):
+        vmfb = get_vmfb_from_path(
+            self.first_vicuna_vmfb_path, self.device, "tm_tensor"
+        )
+        if vmfb is not None:
+            return vmfb
+
+        # Compilation path needs some more work before it is functional
+
+        print(
+            f"[DEBUG] vmfb not found at {self.first_vicuna_vmfb_path.absolute()}. Trying to work with"
+            f"[DEBUG] mlir path { self.first_vicuna_mlir_path} {'exists' if self.first_vicuna_mlir_path.exists() else 'does not exist'}"
+        )
+        if self.first_vicuna_mlir_path.exists():
+            with open(self.first_vicuna_mlir_path, "rb") as f:
+                bytecode = f.read()
+        else:
+            mlir_generated = False
+            if self.load_mlir_from_shark_tank:
+                if self.precision == "fp32":
+                    # download MLIR from shark_tank for fp32
+                    download_public_file(
+                        "gs://shark_tank/vicuna/unsharded/mlir/first_vicuna.mlir",
+                        self.first_vicuna_mlir_path.absolute(),
+                        single_file=True,
+                    )
+                    if self.first_vicuna_mlir_path.exists():
+                        with open(self.first_vicuna_mlir_path, "rb") as f:
+                            bytecode = f.read()
+                        mlir_generated = True
+                    else:
+                        raise ValueError(
+                            f"MLIR not found at {self.first_vicuna_mlir_path.absolute()}"
+                            " after downloading! Please check path and try again"
+                        )
+                else:
+                    print(
+                        "Only fp32 mlir added to tank, generating mlir on device."
+                    )
+
+            if not mlir_generated:
+                compilation_prompt = "".join(["0" for _ in range(17)])
+                compilation_input_ids = self.tokenizer(
+                    compilation_prompt
+                ).input_ids
+                compilation_input_ids = torch.tensor(
+                    compilation_input_ids
+                ).reshape([1, 19])
+                firstVicunaCompileInput = (compilation_input_ids,)
+                model = FirstVicuna(self.hf_model_path)
+
+                print(f"[DEBUG] generating torchscript graph")
+                ts_graph = import_with_fx(
+                    model,
+                    firstVicunaCompileInput,
+                    is_f16=self.precision == "fp16",
+                    f16_input_mask=[False, False],
+                    mlir_type="torchscript",
+                )
+                del model
+                print(f"[DEBUG] generating torch mlir")
+
+                firstVicunaCompileInput = list(firstVicunaCompileInput)
+                firstVicunaCompileInput[0] = torch_mlir.TensorPlaceholder.like(
+                    firstVicunaCompileInput[0], dynamic_axes=[1]
+                )
+                firstVicunaCompileInput = tuple(firstVicunaCompileInput)
+                module = torch_mlir.compile(
+                    ts_graph,
+                    [*firstVicunaCompileInput],
+                    torch_mlir.OutputType.LINALG_ON_TENSORS,
+                    use_tracing=False,
+                    verbose=False,
+                )
+                del ts_graph
+
+                def remove_constant_dim(line):
+                    if "19x" in line:
+                        line = re.sub("19x", "?x", line)
+                        line = re.sub(
+                            "tensor.empty\(\)", "tensor.empty(%dim)", line
+                        )
+                    if "tensor.empty" in line and "?x?" in line:
+                        line = re.sub(
+                            "tensor.empty\(%dim\)",
+                            "tensor.empty(%dim, %dim)",
+                            line,
+                        )
+                    if "arith.cmpi" in line:
+                        line = re.sub("c19", "dim", line)
+                    if " 19," in line:
+                        line = re.sub(" 19,", " %dim,", line)
+                    return line
+
+                module = str(module)
+                new_lines = []
+
+                print(f"[DEBUG] rewriting torch_mlir file")
+                for line in module.splitlines():
+                    line = remove_constant_dim(line)
+                    if "%0 = tensor.empty(%dim) : tensor<?xi64>" in line:
+                        new_lines.append(
+                            "%dim = tensor.dim %arg0, %c1 : tensor<1x?xi64>"
+                        )
+                    if (
+                        "%dim = tensor.dim %arg0, %c1 : tensor<1x?xi64>"
+                        in line
+                    ):
+                        continue
+
+                    new_lines.append(line)
+
+                module = "\n".join(new_lines)
+
+                print(f"[DEBUG] converting to bytecode")
+                del new_lines
+                module = module.encode("UTF-8")
+                module = BytesIO(module)
+                bytecode = module.read()
+                del module
+
+                print(f"[DEBUG] writing mlir to file")
+                f_ = open(self.first_vicuna_mlir_path, "wb")
+                f_.write(bytecode)
+                f_.close()
+
+        shark_module = SharkInference(
+            mlir_module=bytecode, device=self.device, mlir_dialect="tm_tensor"
+        )
+        path = shark_module.save_module(
+            self.first_vicuna_vmfb_path.parent.absolute(),
+            self.first_vicuna_vmfb_path.stem,
+            extra_args=[
+                "--iree-hal-dump-executable-sources-to=ies",
+                "--iree-vm-target-truncate-unsupported-floats",
+                "--iree-codegen-check-ir-before-llvm-conversion=false",
+                "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
+            ],
+        )
+        print("Saved first vic vmfb at vmfb at ", str(path))
+        shark_module.load_module(path)
+
+        return shark_module
+
+    def compile_second_vicuna(self):
+        vmfb = get_vmfb_from_path(
+            self.second_vicuna_vmfb_path, self.device, "tm_tensor"
+        )
+        if vmfb is not None:
+            return vmfb
+
+        # Compilation path needs some more work before it is functional
+        print(
+            f"[DEBUG] mlir path {self.second_vicuna_mlir_path} {'exists' if self.second_vicuna_mlir_path.exists() else 'does not exist'}"
+        )
+        if self.second_vicuna_mlir_path.exists():
+            with open(self.second_vicuna_mlir_path, "rb") as f:
+                bytecode = f.read()
+        else:
+            mlir_generated = False
+            if self.load_mlir_from_shark_tank:
+                if self.precision == "fp32":
+                    # download MLIR from shark_tank for fp32
+                    download_public_file(
+                        "gs://shark_tank/vicuna/unsharded/mlir/second_vicuna.mlir",
+                        self.second_vicuna_mlir_path.absolute(),
+                        single_file=True,
+                    )
+                    if self.second_vicuna_mlir_path.exists():
+                        with open(self.second_vicuna_mlir_path, "rb") as f:
+                            bytecode = f.read()
+                        mlir_generated = True
+                    else:
+                        raise ValueError(
+                            f"MLIR not found at {self.second_vicuna_mlir_path.absolute()}"
+                            " after downloading! Please check path and try again"
+                        )
+                else:
+                    print(
+                        "Only fp32 mlir added to tank, generating mlir on device."
+                    )
+
+            if not mlir_generated:
+                compilation_input_ids = torch.zeros([1, 1], dtype=torch.int64)
+                pkv = tuple(
+                    (torch.zeros([1, 32, 19, 128], dtype=torch.float32))
+                    for _ in range(64)
+                )
+                secondVicunaCompileInput = (compilation_input_ids,) + pkv
+                model = SecondVicuna(self.hf_model_path)
+                ts_graph = import_with_fx(
+                    model,
+                    secondVicunaCompileInput,
+                    is_f16=self.precision == "fp16",
+                    f16_input_mask=[False, False],
+                    mlir_type="torchscript",
+                )
+                secondVicunaCompileInput = list(secondVicunaCompileInput)
+                for i in range(len(secondVicunaCompileInput)):
+                    if i != 0:
+                        secondVicunaCompileInput[
+                            i
+                        ] = torch_mlir.TensorPlaceholder.like(
+                            secondVicunaCompileInput[i], dynamic_axes=[2]
+                        )
+                secondVicunaCompileInput = tuple(secondVicunaCompileInput)
+                module = torch_mlir.compile(
+                    ts_graph,
+                    [*secondVicunaCompileInput],
+                    torch_mlir.OutputType.LINALG_ON_TENSORS,
+                    use_tracing=False,
+                    verbose=False,
+                )
+
+                def remove_constant_dim(line):
+                    if "c19_i64" in line:
+                        line = re.sub("c19_i64", "dim_i64", line)
+                    if "19x" in line:
+                        line = re.sub("19x", "?x", line)
+                        line = re.sub(
+                            "tensor.empty\(\)", "tensor.empty(%dim)", line
+                        )
+                    if "tensor.empty" in line and "?x?" in line:
+                        line = re.sub(
+                            "tensor.empty\(%dim\)",
+                            "tensor.empty(%dim, %dim)",
+                            line,
+                        )
+                    if "arith.cmpi" in line:
+                        line = re.sub("c19", "dim", line)
+                    if " 19," in line:
+                        line = re.sub(" 19,", " %dim,", line)
+                    if "20x" in line:
+                        line = re.sub("20x", "?x", line)
+                        line = re.sub(
+                            "tensor.empty\(\)", "tensor.empty(%dimp1)", line
+                        )
+                    if " 20," in line:
+                        line = re.sub(" 20,", " %dimp1,", line)
+                    return line
+
+                module_str = str(module)
+                new_lines = []
+
+                for line in module_str.splitlines():
+                    if "%c19_i64 = arith.constant 19 : i64" in line:
+                        new_lines.append("%c2 = arith.constant 2 : index")
+                        new_lines.append(
+                            "%dim_4_int = tensor.dim %arg1, %c2 : tensor<1x32x?x128xf32>"
+                        )
+                        new_lines.append(
+                            "%dim_i64 = arith.index_cast %dim_4_int : index to i64"
+                        )
+                        continue
+                    if "%c2 = arith.constant 2 : index" in line:
+                        continue
+                    if "%c20_i64 = arith.constant 20 : i64" in line:
+                        new_lines.append("%c1_i64 = arith.constant 1 : i64")
+                        new_lines.append(
+                            "%c20_i64 = arith.addi %dim_i64, %c1_i64 : i64"
+                        )
+                        new_lines.append(
+                            "%dimp1 = arith.index_cast %c20_i64 : i64 to index"
+                        )
+                        continue
+                    line = remove_constant_dim(line)
+                    new_lines.append(line)
+
+                module_str = "\n".join(new_lines)
+                bytecode = module_str.encode("UTF-8")
+                bytecode_stream = BytesIO(bytecode)
+                bytecode = bytecode_stream.read()
+                f_ = open(self.second_vicuna_mlir_path, "wb")
+                f_.write(bytecode)
+                f_.close()
+
+        shark_module = SharkInference(
+            mlir_module=bytecode, device=self.device, mlir_dialect="tm_tensor"
+        )
+
+        path = shark_module.save_module(
+            self.second_vicuna_vmfb_path.parent.absolute(),
+            self.second_vicuna_vmfb_path.stem,
+            extra_args=[
+                "--iree-hal-dump-executable-sources-to=ies",
+                "--iree-vm-target-truncate-unsupported-floats",
+                "--iree-codegen-check-ir-before-llvm-conversion=false",
+                "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
+            ],
+        )
+        print("Saved vmfb at ", str(path))
+        shark_module.load_module(self.second_vicuna_vmfb_path)
+
+        # self.shark_module = shark_module
+
+        return shark_module
+
+    def compile(self):
+        # Cannot load both the models in the memory at once
+        # due to memory constraints, hence on demand compilation
+        # is being used until the space is enough for both models
+
+        # Testing : DO NOT Download Vmfbs if not found. Modify later
+        # download vmfbs for A100
+        if (
+            not self.first_vicuna_vmfb_path.exists()
+            and self.device == "cuda"
+            and self.precision == "fp32"
+        ):
+            download_public_file(
+                "gs://shark_tank/vicuna/unsharded/first_vicuna.vmfb",
+                self.first_vicuna_vmfb_path.absolute(),
+                single_file=True,
+            )
+        else:
+            # get first vic
+            # TODO: Remove after testing to avoid memory overload
+            # fvic_shark_model = self.compile_first_vicuna()
+            pass
+        if (
+            not self.second_vicuna_vmfb_path.exists()
+            and self.device == "cuda"
+            and self.precision == "fp32"
+        ):
+            download_public_file(
+                "gs://shark_tank/vicuna/unsharded/second_vicuna.vmfb",
+                self.second_vicuna_vmfb_path.absolute(),
+                single_file=True,
+            )
+        else:
+            # get second vic
+            # TODO: Remove after testing to avoid memory overload
+            # svic_shark_model = self.compile_second_vicuna()
+            pass
+
+        # get first vic
+        # fvic_shark_model = self.compile_first_vicuna()
+        # get second vic
+        # svic_shark_model = self.compile_second_vicuna()
+        # return tuple of shark_modules
+        # return fvic_shark_model, svic_shark_model
+        return None
+        # return tuple of shark_modules once mem is supported
+        # return fvic_shark_model, svic_shark_model
+
+    def generate(self, prompt, cli=False):
+        # TODO: refactor for cleaner integration
+        import gc
+
+        res = []
+        res_tokens = []
+        params = {
+            "prompt": prompt,
+            "is_first": True,
+            "fv": self.compile_first_vicuna(),
+        }
+
+        generated_token_op = self.generate_new_token(params=params)
+
+        token = generated_token_op["token"]
+        logits = generated_token_op["logits"]
+        pkv = generated_token_op["pkv"]
+        detok = generated_token_op["detok"]
+
+        res.append(detok)
+        res_tokens.append(token)
+        if cli:
+            print(f"Assistant: {detok}", end=" ", flush=True)
+
+        # Clear First Vic from Memory (main and cuda)
+        del params
+        torch.cuda.empty_cache()
+        gc.collect()
+
+        sec_vic = self.compile_second_vicuna()
+        for _ in range(self.max_num_tokens - 2):
+            params = {
+                "prompt": None,
+                "is_first": False,
+                "logits": logits,
+                "pkv": pkv,
+                "sv": sec_vic,
+            }
+
+            generated_token_op = self.generate_new_token(params=params)
+
+            token = generated_token_op["token"]
+            logits = generated_token_op["logits"]
+            pkv = generated_token_op["pkv"]
+            detok = generated_token_op["detok"]
+
+            if token == 2:
+                break
+            res_tokens.append(token)
+            if detok == "<0x0A>":
+                res.append("\n")
+                if cli:
+                    print("\n", end="", flush=True)
+            else:
+                res.append(detok)
+                if cli:
+                    print(f"{detok}", end=" ", flush=True)
+        del sec_vic, pkv, logits
+        torch.cuda.empty_cache()
+        gc.collect()
+
+        for i in range(len(res_tokens)):
+            if type(res_tokens[i]) != int:
+                res_tokens[i] = int(res_tokens[i][0])
+
+        res_str = self.tokenizer.decode(res_tokens)
+        # print(f"[DEBUG] final output : \n{res_str}")
+        return res_str
+
+    def generate_new_token(self, params, debug=False):
+        def forward_first(first_vic, prompt, cache_outputs=False):
+            input_ids = self.tokenizer(prompt).input_ids
+            input_id_len = len(input_ids)
+            input_ids = torch.tensor(input_ids)
+            input_ids = input_ids.reshape([1, input_id_len])
+            firstVicunaInput = (input_ids,)
+            assert first_vic is not None
+            output_first_vicuna = first_vic("forward", firstVicunaInput)
+            output_first_vicuna_tensor = torch.tensor(output_first_vicuna[1:])
+            logits_first_vicuna = torch.tensor(output_first_vicuna[0])
+            if cache_outputs:
+                torch.save(
+                    logits_first_vicuna, "logits_first_vicuna_tensor.pt"
+                )
+                torch.save(
+                    output_first_vicuna_tensor, "output_first_vicuna_tensor.pt"
+                )
+            token = torch.argmax(
+                torch.tensor(logits_first_vicuna)[:, -1, :], dim=1
+            )
+            return token, logits_first_vicuna, output_first_vicuna_tensor
+
+        def forward_second(sec_vic, inputs=None, load_inputs=False):
+            if inputs is not None:
+                logits = inputs[0]
+                pkv = inputs[1:]
+            elif load_inputs:
+                pkv = torch.load("output_first_vicuna_tensor.pt")
+                pkv = tuple(torch.tensor(x) for x in pkv)
+                logits = torch.load("logits_first_vicuna_tensor.pt")
+            else:
+                print(
+                    "Either inputs must be given, or load_inputs must be true"
+                )
+                return None
+            token = torch.argmax(torch.tensor(logits)[:, -1, :], dim=1)
+            token = token.to(torch.int64).reshape([1, 1])
+            secondVicunaInput = (token,) + tuple(pkv)
+
+            secondVicunaOutput = sec_vic("forward", secondVicunaInput)
+            new_pkv = secondVicunaOutput[1:]
+            new_logits = secondVicunaOutput[0]
+            new_token = torch.argmax(torch.tensor(new_logits)[:, -1, :], dim=1)
+            return new_token, new_logits, new_pkv
+
+        is_first = params["is_first"]
+
+        if is_first:
+            prompt = params["prompt"]
+            fv = params["fv"]
+            token, logits, pkv = forward_first(
+                fv,  # self.shark_model[0],
+                prompt=prompt,
+                cache_outputs=False,
+            )
+        else:
+            _logits = params["logits"]
+            _pkv = params["pkv"]
+            inputs = (_logits,) + tuple(_pkv)
+            sv = params["sv"]
+            token, logits, pkv = forward_second(
+                sv,  # self.shark_model[1],
+                inputs=inputs,
+                load_inputs=False,
+            )
+
+        detok = self.tokenizer.decode(token)
+        if debug:
+            print(
+                f"[DEBUG] is_first: {is_first} |"
+                f" token : {token} | detok : {detok}"
+            )
+        ret_dict = {
+            "token": token,
+            "logits": logits,
+            "pkv": pkv,
+            "detok": detok,
+        }
+        return ret_dict
+
+    def autocomplete(self, prompt):
+        # use First vic alone to complete a story / prompt / sentence.
+        pass

apps/language_models/src/pipelines/vicuna_sharded_pipeline.py

@@ -0,0 +1,408 @@
+from apps.language_models.src.model_wrappers.vicuna_sharded_model import (
+    FirstVicunaLayer,
+    SecondVicunaLayer,
+    CompiledFirstVicunaLayer,
+    CompiledSecondVicunaLayer,
+    ShardedVicunaModel,
+)
+from apps.language_models.src.pipelines.SharkLLMBase import SharkLLMBase
+from shark.shark_importer import import_with_fx
+from io import BytesIO
+from pathlib import Path
+from shark.shark_inference import SharkInference
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from tqdm import tqdm
+from torch_mlir import TensorPlaceholder
+
+
+import re
+import torch
+import torch_mlir
+import os
+
+
+class Vicuna(SharkLLMBase):
+    def __init__(
+        self,
+        model_name,
+        hf_model_path="TheBloke/vicuna-7B-1.1-HF",
+        max_num_tokens=512,
+        device="cuda",
+        precision="fp32",
+    ) -> None:
+        super().__init__(model_name, hf_model_path, max_num_tokens)
+        self.max_sequence_length = 256
+        self.device = device
+        self.precision = precision
+        self.tokenizer = self.get_tokenizer()
+        self.shark_model = self.compile()
+
+    def get_tokenizer(self):
+        tokenizer = AutoTokenizer.from_pretrained(
+            self.hf_model_path, use_fast=False
+        )
+        return tokenizer
+
+    def get_src_model(self):
+        kwargs = {"torch_dtype": torch.float}
+        vicuna_model = AutoModelForCausalLM.from_pretrained(
+            self.hf_model_path, **kwargs
+        )
+        return vicuna_model
+
+    def write_in_dynamic_inputs0(self, module, dynamic_input_size):
+        new_lines = []
+        for line in module.splitlines():
+            line = re.sub(f"{dynamic_input_size}x", "?x", line)
+            if "?x" in line:
+                line = re.sub("tensor.empty\(\)", "tensor.empty(%dim)", line)
+            line = re.sub(f" {dynamic_input_size},", " %dim,", line)
+            if "tensor.empty" in line and "?x?" in line:
+                line = re.sub(
+                    "tensor.empty\(%dim\)", "tensor.empty(%dim, %dim)", line
+                )
+            if "arith.cmpi" in line:
+                line = re.sub(f"c{dynamic_input_size}", "dim", line)
+            new_lines.append(line)
+        new_module = "\n".join(new_lines)
+        return new_module
+
+    def write_in_dynamic_inputs1(self, module, dynamic_input_size):
+        new_lines = []
+        for line in module.splitlines():
+            if "dim_42 =" in line:
+                continue
+            if f"%c{dynamic_input_size}_i64 =" in line:
+                new_lines.append(
+                    "%dim_42 = tensor.dim %arg1, %c3 : tensor<1x1x1x?xf32>"
+                )
+                new_lines.append(
+                    f"%dim_42_i64 = arith.index_cast %dim_42 : index to i64"
+                )
+                continue
+            line = re.sub(f"{dynamic_input_size}x", "?x", line)
+            if "?x" in line:
+                line = re.sub(
+                    "tensor.empty\(\)", "tensor.empty(%dim_42)", line
+                )
+            line = re.sub(f" {dynamic_input_size},", " %dim_42,", line)
+            if "tensor.empty" in line and "?x?" in line:
+                line = re.sub(
+                    "tensor.empty\(%dim_42\)",
+                    "tensor.empty(%dim_42, %dim_42)",
+                    line,
+                )
+            if "arith.cmpi" in line:
+                line = re.sub(f"c{dynamic_input_size}", "dim_42", line)
+            new_lines.append(line)
+        new_module = "\n".join(new_lines)
+        return new_module
+
+    def compile_vicuna_layer(
+        self,
+        vicuna_layer,
+        hidden_states,
+        attention_mask,
+        position_ids,
+        past_key_value0=None,
+        past_key_value1=None,
+    ):
+        if past_key_value0 is None and past_key_value1 is None:
+            model_inputs = (hidden_states, attention_mask, position_ids)
+        else:
+            model_inputs = (
+                hidden_states,
+                attention_mask,
+                position_ids,
+                past_key_value0,
+                past_key_value1,
+            )
+        mlir_bytecode = import_with_fx(
+            vicuna_layer,
+            model_inputs,
+            is_f16=self.precision == "fp16",
+            f16_input_mask=[False, False],
+            mlir_type="torchscript",
+        )
+        return mlir_bytecode
+
+    def compile_to_vmfb(self, inputs, layers, is_first=True):
+        mlirs, modules = [], []
+        for idx, layer in tqdm(enumerate(layers), desc="Getting mlirs"):
+            if is_first:
+                mlir_path = Path(f"{idx}_0.mlir")
+                vmfb_path = Path(f"{idx}_0.vmfb")
+            else:
+                mlir_path = Path(f"{idx}_1.mlir")
+                vmfb_path = Path(f"{idx}_1.vmfb")
+            if vmfb_path.exists():
+                continue
+            if mlir_path.exists():
+                # print(f"Found layer {idx} mlir")
+                f_ = open(mlir_path, "rb")
+                bytecode = f_.read()
+                f_.close()
+            else:
+                hidden_states_placeholder = TensorPlaceholder.like(
+                    inputs[0], dynamic_axes=[1]
+                )
+                attention_mask_placeholder = TensorPlaceholder.like(
+                    inputs[1], dynamic_axes=[3]
+                )
+                position_ids_placeholder = TensorPlaceholder.like(
+                    inputs[2], dynamic_axes=[1]
+                )
+                if not is_first:
+                    pkv0_placeholder = TensorPlaceholder.like(
+                        inputs[3], dynamic_axes=[2]
+                    )
+                    pkv1_placeholder = TensorPlaceholder.like(
+                        inputs[4], dynamic_axes=[2]
+                    )
+                print(f"Compiling layer {idx} mlir")
+                if is_first:
+                    ts_g = self.compile_vicuna_layer(
+                        layer, inputs[0], inputs[1], inputs[2]
+                    )
+                    module = torch_mlir.compile(
+                        ts_g,
+                        (
+                            hidden_states_placeholder,
+                            inputs[1],
+                            inputs[2],
+                        ),
+                        torch_mlir.OutputType.LINALG_ON_TENSORS,
+                        use_tracing=False,
+                        verbose=False,
+                    )
+                else:
+                    ts_g = self.compile_vicuna_layer(
+                        layer,
+                        inputs[0],
+                        inputs[1],
+                        inputs[2],
+                        inputs[3],
+                        inputs[4],
+                    )
+                    module = torch_mlir.compile(
+                        ts_g,
+                        (
+                            inputs[0],
+                            attention_mask_placeholder,
+                            inputs[2],
+                            pkv0_placeholder,
+                            pkv1_placeholder,
+                        ),
+                        torch_mlir.OutputType.LINALG_ON_TENSORS,
+                        use_tracing=False,
+                        verbose=False,
+                    )
+
+                # bytecode_stream = BytesIO()
+                # module.operation.write_bytecode(bytecode_stream)
+                # bytecode = bytecode_stream.getvalue()
+
+                if is_first:
+                    module = self.write_in_dynamic_inputs0(str(module), 137)
+                    bytecode = module.encode("UTF-8")
+                    bytecode_stream = BytesIO(bytecode)
+                    bytecode = bytecode_stream.read()
+
+                else:
+                    module = self.write_in_dynamic_inputs1(str(module), 138)
+                    if idx in [0, 5, 6, 7]:
+                        module_str = module
+                        module_str = module_str.splitlines()
+                        new_lines = []
+                        for line in module_str:
+                            if len(line) < 1000:
+                                new_lines.append(line)
+                            else:
+                                new_lines.append(line[:999])
+                        module_str = "\n".join(new_lines)
+                        f1_ = open(f"{idx}_1_test.mlir", "w+")
+                        f1_.write(module_str)
+                        f1_.close()
+
+                    bytecode = module.encode("UTF-8")
+                    bytecode_stream = BytesIO(bytecode)
+                    bytecode = bytecode_stream.read()
+
+                f_ = open(mlir_path, "wb")
+                f_.write(bytecode)
+                f_.close()
+            mlirs.append(bytecode)
+
+        for idx, layer in tqdm(enumerate(layers), desc="compiling modules"):
+            if is_first:
+                vmfb_path = Path(f"{idx}_0.vmfb")
+                if idx < 25:
+                    device = "cpu"
+                else:
+                    device = "cpu"
+                if vmfb_path.exists():
+                    # print(f"Found layer {idx} vmfb")
+                    module = SharkInference(
+                        None, device=device, mlir_dialect="tm_tensor"
+                    )
+                    module.load_module(vmfb_path)
+                else:
+                    print(f"Compiling layer {idx} vmfb")
+                    module = SharkInference(
+                        mlirs[idx], device=device, mlir_dialect="tm_tensor"
+                    )
+                    module.save_module(
+                        module_name=f"{idx}_0",
+                        extra_args=[
+                            "--iree-hal-dump-executable-sources-to=ies",
+                            "--iree-vm-target-truncate-unsupported-floats",
+                            "--iree-codegen-check-ir-before-llvm-conversion=false",
+                            "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
+                        ],
+                    )
+                    module.load_module(vmfb_path)
+                modules.append(module)
+            else:
+                vmfb_path = Path(f"{idx}_1.vmfb")
+                if idx < 25:
+                    device = "cpu"
+                else:
+                    device = "cpu"
+                if vmfb_path.exists():
+                    # print(f"Found layer {idx} vmfb")
+                    module = SharkInference(
+                        None, device=device, mlir_dialect="tm_tensor"
+                    )
+                    module.load_module(vmfb_path)
+                else:
+                    print(f"Compiling layer {idx} vmfb")
+                    module = SharkInference(
+                        mlirs[idx], device=device, mlir_dialect="tm_tensor"
+                    )
+                    module.save_module(
+                        module_name=f"{idx}_1",
+                        extra_args=[
+                            "--iree-hal-dump-executable-sources-to=ies",
+                            "--iree-vm-target-truncate-unsupported-floats",
+                            "--iree-codegen-check-ir-before-llvm-conversion=false",
+                            "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
+                        ],
+                    )
+                    module.load_module(vmfb_path)
+                modules.append(module)
+
+        return mlirs, modules
+
+    def get_sharded_model(self):
+        # SAMPLE_INPUT_LEN is used for creating mlir with dynamic inputs, which is currently an increadibly hacky proccess
+        # please don't change it
+        SAMPLE_INPUT_LEN = 137
+        vicuna_model = self.get_src_model()
+        placeholder_input0 = (
+            torch.zeros([1, SAMPLE_INPUT_LEN, 4096]),
+            torch.zeros([1, 1, SAMPLE_INPUT_LEN, SAMPLE_INPUT_LEN]),
+            torch.zeros([1, SAMPLE_INPUT_LEN], dtype=torch.int64),
+        )
+
+        placeholder_input1 = (
+            torch.zeros([1, 1, 4096]),
+            torch.zeros([1, 1, 1, SAMPLE_INPUT_LEN + 1]),
+            torch.zeros([1, 1], dtype=torch.int64),
+            torch.zeros([1, 32, SAMPLE_INPUT_LEN, 128]),
+            torch.zeros([1, 32, SAMPLE_INPUT_LEN, 128]),
+        )
+
+        layers0 = [
+            FirstVicunaLayer(layer) for layer in vicuna_model.model.layers
+        ]
+        _, modules0 = self.compile_to_vmfb(
+            placeholder_input0, layers0, is_first=True
+        )
+        shark_layers0 = [CompiledFirstVicunaLayer(m) for m in modules0]
+
+        layers1 = [
+            SecondVicunaLayer(layer) for layer in vicuna_model.model.layers
+        ]
+        _, modules1 = self.compile_to_vmfb(
+            placeholder_input1, layers1, is_first=False
+        )
+        shark_layers1 = [CompiledSecondVicunaLayer(m) for m in modules1]
+
+        sharded_model = ShardedVicunaModel(
+            vicuna_model, shark_layers0, shark_layers1
+        )
+        return sharded_model
+
+    def compile(self):
+        return self.get_sharded_model()
+
+    def generate(self, prompt, cli=False):
+        # TODO: refactor for cleaner integration
+
+        tokens_generated = []
+        _past_key_values = None
+        _token = None
+        detoks_generated = []
+        for iteration in range(self.max_num_tokens):
+            params = {
+                "prompt": prompt,
+                "is_first": iteration == 0,
+                "token": _token,
+                "past_key_values": _past_key_values,
+            }
+
+            generated_token_op = self.generate_new_token(params=params)
+
+            _token = generated_token_op["token"]
+            _past_key_values = generated_token_op["past_key_values"]
+            _detok = generated_token_op["detok"]
+
+            if _token == 2:
+                break
+            detoks_generated.append(_detok)
+            tokens_generated.append(_token)
+
+        for i in range(len(tokens_generated)):
+            if type(tokens_generated[i]) != int:
+                tokens_generated[i] = int(tokens_generated[i][0])
+        result_output = self.tokenizer.decode(tokens_generated)
+        return result_output
+
+    def generate_new_token(self, params):
+        is_first = params["is_first"]
+        if is_first:
+            prompt = params["prompt"]
+            input_ids = self.tokenizer(prompt).input_ids
+            input_id_len = len(input_ids)
+            input_ids = torch.tensor(input_ids)
+            input_ids = input_ids.reshape([1, input_id_len])
+            output = self.shark_model.forward(input_ids, is_first=is_first)
+        else:
+            token = params["token"]
+            past_key_values = params["past_key_values"]
+            input_ids = [token]
+            input_id_len = len(input_ids)
+            input_ids = torch.tensor(input_ids)
+            input_ids = input_ids.reshape([1, input_id_len])
+            output = self.shark_model.forward(
+                input_ids, past_key_values=past_key_values, is_first=is_first
+            )
+
+        _logits = output["logits"]
+        _past_key_values = output["past_key_values"]
+        _token = int(torch.argmax(_logits[:, -1, :], dim=1)[0])
+        _detok = self.tokenizer.decode(_token)
+
+        ret_dict = {
+            "token": _token,
+            "detok": _detok,
+            "past_key_values": _past_key_values,
+        }
+
+        print(f" token : {_token} | detok : {_detok}")
+
+        return ret_dict
+
+    def autocomplete(self, prompt):
+        # use First vic alone to complete a story / prompt / sentence.
+        pass

apps/language_models/utils.py

@@ -0,0 +1,25 @@
+import torch
+from torch.fx.experimental.proxy_tensor import make_fx
+from torch._decomp import get_decompositions
+from typing import List
+from pathlib import Path
+
+
+# expects a Path / str as arg
+# returns None if path not found or SharkInference module
+def get_vmfb_from_path(vmfb_path, device, mlir_dialect):
+    if not isinstance(vmfb_path, Path):
+        vmfb_path = Path(vmfb_path)
+
+    from shark.shark_inference import SharkInference
+
+    if not vmfb_path.exists():
+        return None
+
+    print("Loading vmfb from: ", vmfb_path)
+    shark_module = SharkInference(
+        None, device=device, mlir_dialect=mlir_dialect
+    )
+    shark_module.load_module(vmfb_path)
+    print("Successfully loaded vmfb")
+    return shark_module

apps/stable_diffusion/profiling_with_iree.md

@@ -10,7 +10,7 @@ Vulkan AMD:
 iree-compile --iree-input-type=none --iree-hal-target-backends=vulkan --iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 /path/to/input/mlir -o /path/to/output/vmfb
 
 #  add --mlir-print-debuginfo --mlir-print-op-on-diagnostic=true for debug
-#  use –iree-input-type=mhlo for tf models
+#  use –iree-input-type=auto or "mhlo_legacy" or "stablehlo" for TF models
 
 CUDA NVIDIA:
 iree-compile --iree-input-type=none --iree-hal-target-backends=cuda --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 /path/to/input/mlir -o /path/to/output/vmfb

apps/stable_diffusion/shark_sd.spec

@@ -47,6 +47,7 @@ block_cipher = None
 
 hiddenimports = ['shark', 'shark.shark_inference', 'apps']
 hiddenimports += [x for x in collect_submodules("skimage") if "tests" not in x]
+hiddenimports += [x for x in collect_submodules("iree") if "tests" not in x]
 
 a = Analysis(
     ['web/index.py'],

apps/stable_diffusion/shark_sd_cli.spec

@@ -42,6 +42,7 @@ block_cipher = None
 
 hiddenimports = ['shark', 'shark.shark_inference', 'apps']
 hiddenimports += [x for x in collect_submodules("skimage") if "tests" not in x]
+hiddenimports += [x for x in collect_submodules("iree") if "tests" not in x]
 
 a = Analysis(
     ['scripts/main.py'],

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_upscaler.py

@@ -20,6 +20,8 @@ from diffusers import (
 )
 from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
 from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+    SD_STATE_IDLE,
+    SD_STATE_CANCEL,
     StableDiffusionPipeline,
 )
 from apps.stable_diffusion.src.utils import (
@@ -84,6 +86,7 @@ class UpscalerPipeline(StableDiffusionPipeline):
     ):
         super().__init__(scheduler, sd_model, import_mlir, use_lora, ondemand)
         self.low_res_scheduler = low_res_scheduler
+        self.status = SD_STATE_IDLE
 
     def prepare_extra_step_kwargs(self, generator, eta):
         accepts_eta = "eta" in set(
@@ -164,6 +167,7 @@ class UpscalerPipeline(StableDiffusionPipeline):
         latent_history = [latents]
         text_embeddings = torch.from_numpy(text_embeddings).to(dtype)
         text_embeddings_numpy = text_embeddings.detach().numpy()
+        self.status = SD_STATE_IDLE
         self.load_unet()
         for i, t in tqdm(enumerate(total_timesteps)):
             step_start_time = time.time()
@@ -210,6 +214,9 @@ class UpscalerPipeline(StableDiffusionPipeline):
             #  )
             step_time_sum += step_time
 
+            if self.status == SD_STATE_CANCEL:
+                break
+
         if self.ondemand:
             self.unload_unet()
         avg_step_time = step_time_sum / len(total_timesteps)

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_utils.py

@@ -87,7 +87,8 @@ class StableDiffusionPipeline:
         else:
             try:
                 self.text_encoder = get_clip()
-            except:
+            except Exception as e:
+                print(e)
                 print("download pipeline failed, falling back to import_mlir")
                 self.text_encoder = self.sd_model.clip()
 
@@ -104,7 +105,8 @@ class StableDiffusionPipeline:
         else:
             try:
                 self.unet = get_unet()
-            except:
+            except Exception as e:
+                print(e)
                 print("download pipeline failed, falling back to import_mlir")
                 self.unet = self.sd_model.unet()
 
@@ -121,7 +123,8 @@ class StableDiffusionPipeline:
         else:
             try:
                 self.vae = get_vae()
-            except:
+            except Exception as e:
+                print(e)
                 print("download pipeline failed, falling back to import_mlir")
                 self.vae = self.sd_model.vae()
 

apps/stable_diffusion/src/utils/__init__.py

@@ -30,6 +30,8 @@ from apps.stable_diffusion.src.utils.utils import (
     sanitize_seed,
     get_path_stem,
     get_extended_name,
+    get_generated_imgs_path,
+    get_generated_imgs_todays_subdir,
     clear_all,
     save_output_img,
     get_generation_text_info,

apps/stable_diffusion/src/utils/resources/model_db.json

@@ -3,7 +3,7 @@
     "stablediffusion/untuned":"gs://shark_tank/nightly"
   },
   {
-    "stablediffusion/v1_4/unet/fp16/length_64/untuned":"unet_1_64_512_512_fp16_stable-diffusion-2-1-base_vulkan",
+    "stablediffusion/v1_4/unet/fp16/length_64/untuned":"unet_1_64_512_512_fp16_stable-diffusion-v1-4_vulkan",
     "stablediffusion/v1_4/vae/fp16/length_77/untuned":"vae_1_64_512_512_fp16_stable-diffusion-v1-4_vulkan",
     "stablediffusion/v1_4/vae/fp16/length_64/untuned":"vae_1_64_512_512_fp16_stable-diffusion-v1-4_vulkan",
     "stablediffusion/v1_4/clip/fp32/length_64/untuned":"clip_1_64_512_512_fp16_stable-diffusion-v1-4_vulkan",

apps/stable_diffusion/src/utils/sd_annotation.py

@@ -125,6 +125,8 @@ def load_lower_configs(base_model_id=None):
                 config_name = f"{args.annotation_model}_v2_1_768_{args.precision}_{device}.json"
             else:
                 config_name = f"{args.annotation_model}_{version}_{args.precision}_{device}.json"
+        elif spec in ["rdna2"] and version in ["v2_1", "v2_1base", "v1_4"]:
+            config_name = f"{args.annotation_model}_{version}_{args.precision}_{device}_{spec}_{args.width}x{args.height}.json"
         else:
             config_name = f"{args.annotation_model}_{version}_{args.precision}_{device}_{spec}.json"
 

apps/stable_diffusion/src/utils/stable_args.py

@@ -521,6 +521,22 @@ p.add_argument(
     action=argparse.BooleanOptionalAction,
     help="flag for enabling rest API",
 )
+
+p.add_argument(
+    "--output_gallery",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="flag for removing the output gallery tab, and avoid exposing images under --output_dir in the UI",
+)
+
+p.add_argument(
+    "--output_gallery_followlinks",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for whether the output gallery tab in the UI should follow symlinks when listing subdirectorys under --output_dir",
+)
+
+
 ##############################################################################
 ### SD model auto-annotation flags
 ##############################################################################

apps/stable_diffusion/src/utils/utils.py

@@ -83,7 +83,6 @@ def get_shark_model(tank_url, model_name, extra_args=[]):
 
     # Set local shark_tank cache directory.
     shark_args.local_tank_cache = args.local_tank_cache
-
     from shark.shark_downloader import download_model
 
     if "cuda" in args.device:
@@ -338,13 +337,25 @@ def set_init_device_flags():
                 "stabilityai/stable-diffusion-2-1",
                 "stabilityai/stable-diffusion-2-1-base",
             ]
-            or "rdna3" not in args.iree_vulkan_target_triple
+            or "rdna" not in args.iree_vulkan_target_triple
         )
     ):
         args.use_tuned = False
 
+    elif "rdna2" in args.iree_vulkan_target_triple and (
+        base_model_id
+        not in [
+            "stabilityai/stable-diffusion-2-1",
+            "stabilityai/stable-diffusion-2-1-base",
+            "CompVis/stable-diffusion-v1-4",
+        ]
+    ):
+        args.use_tuned = False
+
     if args.use_tuned:
-        print(f"Using tuned models for {base_model_id}/fp16/{args.device}.")
+        print(
+            f"Using tuned models for {base_model_id}(fp16) on device {args.device}."
+        )
     else:
         print("Tuned models are currently not supported for this setting.")
 
@@ -694,11 +705,20 @@ def clear_all():
         shutil.rmtree(os.path.join(home, ".local/shark_tank"))
 
 
+def get_generated_imgs_path() -> Path:
+    return Path(
+        args.output_dir if args.output_dir else Path.cwd(), "generated_imgs"
+    )
+
+
+def get_generated_imgs_todays_subdir() -> str:
+    return dt.now().strftime("%Y%m%d")
+
+
 # save output images and the inputs corresponding to it.
 def save_output_img(output_img, img_seed, extra_info={}):
-    output_path = args.output_dir if args.output_dir else Path.cwd()
     generated_imgs_path = Path(
-        output_path, "generated_imgs", dt.now().strftime("%Y%m%d")
+        get_generated_imgs_path(), get_generated_imgs_todays_subdir()
     )
     generated_imgs_path.mkdir(parents=True, exist_ok=True)
     csv_path = Path(generated_imgs_path, "imgs_details.csv")

apps/stable_diffusion/web/index.py

@@ -1,12 +1,14 @@
 from multiprocessing import Process, freeze_support
 import os
 import sys
-import transformers
+import transformers  # ensures inclusion in pysintaller exe generation
 from apps.stable_diffusion.src import args, clear_all
 import apps.stable_diffusion.web.utils.global_obj as global_obj
 
 if sys.platform == "darwin":
     os.environ["DYLD_LIBRARY_PATH"] = "/usr/local/lib"
+    # import before IREE to avoid MLIR library issues
+    import torch_mlir
 
 if args.clear_all:
     clear_all()
@@ -80,6 +82,8 @@ if __name__ == "__main__":
         txt2img_custom_model,
         txt2img_hf_model_id,
         txt2img_gallery,
+        txt2img_png_info_img,
+        txt2img_status,
         txt2img_sendto_img2img,
         txt2img_sendto_inpaint,
         txt2img_sendto_outpaint,
@@ -89,6 +93,7 @@ if __name__ == "__main__":
         img2img_hf_model_id,
         img2img_gallery,
         img2img_init_image,
+        img2img_status,
         img2img_sendto_inpaint,
         img2img_sendto_outpaint,
         img2img_sendto_upscaler,
@@ -97,6 +102,7 @@ if __name__ == "__main__":
         inpaint_hf_model_id,
         inpaint_gallery,
         inpaint_init_image,
+        inpaint_status,
         inpaint_sendto_img2img,
         inpaint_sendto_outpaint,
         inpaint_sendto_upscaler,
@@ -105,6 +111,7 @@ if __name__ == "__main__":
         outpaint_hf_model_id,
         outpaint_gallery,
         outpaint_init_image,
+        outpaint_status,
         outpaint_sendto_img2img,
         outpaint_sendto_inpaint,
         outpaint_sendto_upscaler,
@@ -113,6 +120,7 @@ if __name__ == "__main__":
         upscaler_hf_model_id,
         upscaler_gallery,
         upscaler_init_image,
+        upscaler_status,
         upscaler_sendto_img2img,
         upscaler_sendto_inpaint,
         upscaler_sendto_outpaint,
@@ -125,6 +133,15 @@ if __name__ == "__main__":
         modelmanager_sendto_outpaint,
         modelmanager_sendto_upscaler,
         stablelm_chat,
+        outputgallery_web,
+        outputgallery_tab_select,
+        outputgallery_watch,
+        outputgallery_filename,
+        outputgallery_sendto_txt2img,
+        outputgallery_sendto_img2img,
+        outputgallery_sendto_inpaint,
+        outputgallery_sendto_outpaint,
+        outputgallery_sendto_upscaler,
     )
 
     # init global sd pipeline and config
@@ -151,6 +168,16 @@ if __name__ == "__main__":
             outputs,
         )
 
+    def register_outputgallery_button(button, selectedid, inputs, outputs):
+        button.click(
+            lambda x: (
+                x,
+                gr.Tabs.update(selected=selectedid),
+            ),
+            inputs,
+            outputs,
+        )
+
     with gr.Blocks(
         css=dark_theme, analytics_enabled=False, title="Stable Diffusion"
     ) as sd_web:
@@ -171,7 +198,23 @@ if __name__ == "__main__":
                 stablelm_chat.render()
             with gr.TabItem(label="LoRA Training(Experimental)", id=7):
                 lora_train_web.render()
+            if args.output_gallery:
+                with gr.TabItem(label="Output Gallery", id=8) as og_tab:
+                    outputgallery_web.render()
 
+                # extra output gallery configuration
+                outputgallery_tab_select(og_tab.select)
+                outputgallery_watch(
+                    [
+                        txt2img_status,
+                        img2img_status,
+                        inpaint_status,
+                        outpaint_status,
+                        upscaler_status,
+                    ]
+                )
+
+        # send to buttons
         register_button_click(
             txt2img_sendto_img2img,
             1,
@@ -268,6 +311,37 @@ if __name__ == "__main__":
             [upscaler_gallery],
             [outpaint_init_image, tabs],
         )
+        if args.output_gallery:
+            register_outputgallery_button(
+                outputgallery_sendto_txt2img,
+                0,
+                [outputgallery_filename],
+                [txt2img_png_info_img, tabs],
+            )
+            register_outputgallery_button(
+                outputgallery_sendto_img2img,
+                1,
+                [outputgallery_filename],
+                [img2img_init_image, tabs],
+            )
+            register_outputgallery_button(
+                outputgallery_sendto_inpaint,
+                2,
+                [outputgallery_filename],
+                [inpaint_init_image, tabs],
+            )
+            register_outputgallery_button(
+                outputgallery_sendto_outpaint,
+                3,
+                [outputgallery_filename],
+                [outpaint_init_image, tabs],
+            )
+            register_outputgallery_button(
+                outputgallery_sendto_upscaler,
+                4,
+                [outputgallery_filename],
+                [upscaler_init_image, tabs],
+            )
         register_modelmanager_button(
             modelmanager_sendto_txt2img,
             0,

apps/stable_diffusion/web/ui/__init__.py

@@ -5,6 +5,8 @@ from apps.stable_diffusion.web.ui.txt2img_ui import (
     txt2img_custom_model,
     txt2img_hf_model_id,
     txt2img_gallery,
+    txt2img_png_info_img,
+    txt2img_status,
     txt2img_sendto_img2img,
     txt2img_sendto_inpaint,
     txt2img_sendto_outpaint,
@@ -18,6 +20,7 @@ from apps.stable_diffusion.web.ui.img2img_ui import (
     img2img_hf_model_id,
     img2img_gallery,
     img2img_init_image,
+    img2img_status,
     img2img_sendto_inpaint,
     img2img_sendto_outpaint,
     img2img_sendto_upscaler,
@@ -30,6 +33,7 @@ from apps.stable_diffusion.web.ui.inpaint_ui import (
     inpaint_hf_model_id,
     inpaint_gallery,
     inpaint_init_image,
+    inpaint_status,
     inpaint_sendto_img2img,
     inpaint_sendto_outpaint,
     inpaint_sendto_upscaler,
@@ -42,6 +46,7 @@ from apps.stable_diffusion.web.ui.outpaint_ui import (
     outpaint_hf_model_id,
     outpaint_gallery,
     outpaint_init_image,
+    outpaint_status,
     outpaint_sendto_img2img,
     outpaint_sendto_inpaint,
     outpaint_sendto_upscaler,
@@ -54,6 +59,7 @@ from apps.stable_diffusion.web.ui.upscaler_ui import (
     upscaler_hf_model_id,
     upscaler_gallery,
     upscaler_init_image,
+    upscaler_status,
     upscaler_sendto_img2img,
     upscaler_sendto_inpaint,
     upscaler_sendto_outpaint,
@@ -69,3 +75,14 @@ from apps.stable_diffusion.web.ui.model_manager import (
 )
 from apps.stable_diffusion.web.ui.lora_train_ui import lora_train_web
 from apps.stable_diffusion.web.ui.stablelm_ui import stablelm_chat
+from apps.stable_diffusion.web.ui.outputgallery_ui import (
+    outputgallery_web,
+    outputgallery_tab_select,
+    outputgallery_watch,
+    outputgallery_filename,
+    outputgallery_sendto_txt2img,
+    outputgallery_sendto_img2img,
+    outputgallery_sendto_inpaint,
+    outputgallery_sendto_outpaint,
+    outputgallery_sendto_upscaler,
+)

apps/stable_diffusion/web/ui/css/sd_dark_theme.css

@@ -230,3 +230,44 @@ footer {
 #top_logo .download {
     display: none;
 }
+
+/* output gallery tab */
+.output_parameters_dataframe tbody td {
+    font-size: small;
+    line-height: var(--line-xs)
+}
+
+#output_refresh_button {
+    max-width: 30px;
+    align-self: end;
+    padding-bottom: 8px;
+}
+
+.outputgallery_sendto {
+    min-width: 7em !important;
+}
+
+/* output gallery should take up most of the viewport height regardless of image size/number */
+#outputgallery_gallery .fixed-height {
+    min-height: 89vh !important;
+}
+
+/* don't stretch non-square images to be square, breaking their aspect ratio */
+#outputgallery_gallery .thumbnail-item.thumbnail-lg > img {
+    object-fit: contain !important;
+}
+
+/* centered logo for when there are no images */
+#top_logo.logo_centered {
+    height: 100%;
+    width: 100%;
+}
+
+#top_logo.logo_centered img{
+    object-fit: scale-down;
+    position: absolute;
+    width: 80%;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+}

apps/stable_diffusion/web/ui/img2img_ui.py

@@ -1,8 +1,6 @@
-from pathlib import Path
 import os
 import torch
 import time
-import sys
 import gradio as gr
 import PIL
 from PIL import Image
@@ -26,10 +24,13 @@ from apps.stable_diffusion.src import (
     get_schedulers,
     set_init_device_flags,
     utils,
-    clear_all,
     save_output_img,
 )
-from apps.stable_diffusion.src.utils import get_generation_text_info
+from apps.stable_diffusion.src.utils import (
+    get_generated_imgs_path,
+    get_generation_text_info,
+)
+from apps.stable_diffusion.web.utils.common_label_calc import status_label
 import numpy as np
 
 
@@ -259,11 +260,17 @@ def img2img_inf(
         if global_obj.get_sd_status() == SD_STATE_CANCEL:
             break
         else:
-            save_output_img(out_imgs[0], img_seed, extra_info)
+            save_output_img(
+                out_imgs[0],
+                img_seed,
+                extra_info,
+            )
             generated_imgs.extend(out_imgs)
-            #  yield generated_imgs, text_output
+            yield generated_imgs, text_output, status_label(
+                "Image-to-Image", current_batch + 1, batch_count, batch_size
+            )
 
-    return generated_imgs, text_output
+    return generated_imgs, text_output, ""
 
 
 def decode_base64_to_image(encoding):
@@ -593,16 +600,13 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
                         show_label=False,
                         elem_id="gallery",
                     ).style(columns=[2], object_fit="contain")
-                    output_dir = (
-                        args.output_dir if args.output_dir else Path.cwd()
-                    )
-                    output_dir = Path(output_dir, "generated_imgs")
                     std_output = gr.Textbox(
-                        value=f"Images will be saved at {output_dir}",
+                        value=f"Images will be saved at {get_generated_imgs_path()}",
                         lines=1,
                         elem_id="std_output",
                         show_label=False,
                     )
+                    img2img_status = gr.Textbox(visible=False)
                 with gr.Row():
                     img2img_sendto_inpaint = gr.Button(value="SendTo Inpaint")
                     img2img_sendto_outpaint = gr.Button(
@@ -640,13 +644,21 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
                 lora_hf_id,
                 ondemand,
             ],
-            outputs=[img2img_gallery, std_output],
+            outputs=[img2img_gallery, std_output, img2img_status],
             show_progress=args.progress_bar,
         )
 
-        prompt_submit = prompt.submit(**kwargs)
-        neg_prompt_submit = negative_prompt.submit(**kwargs)
-        generate_click = stable_diffusion.click(**kwargs)
+        status_kwargs = dict(
+            fn=lambda bc, bs: status_label("Image-to-Image", 0, bc, bs),
+            inputs=[batch_count, batch_size],
+            outputs=img2img_status,
+        )
+
+        prompt_submit = prompt.submit(**status_kwargs).then(**kwargs)
+        neg_prompt_submit = negative_prompt.submit(**status_kwargs).then(
+            **kwargs
+        )
+        generate_click = stable_diffusion.click(**status_kwargs).then(**kwargs)
         stop_batch.click(
             fn=cancel_sd,
             cancels=[prompt_submit, neg_prompt_submit, generate_click],

apps/stable_diffusion/web/ui/inpaint_ui.py

@@ -1,4 +1,3 @@
-from pathlib import Path
 import os
 import torch
 import time
@@ -26,7 +25,11 @@ from apps.stable_diffusion.src import (
     clear_all,
     save_output_img,
 )
-from apps.stable_diffusion.src.utils import get_generation_text_info
+from apps.stable_diffusion.src.utils import (
+    get_generated_imgs_path,
+    get_generation_text_info,
+)
+from apps.stable_diffusion.web.utils.common_label_calc import status_label
 
 
 # set initial values of iree_vulkan_target_triple, use_tuned and import_mlir.
@@ -213,7 +216,9 @@ def inpaint_inf(
         else:
             save_output_img(out_imgs[0], img_seed)
             generated_imgs.extend(out_imgs)
-            yield generated_imgs, text_output
+            yield generated_imgs, text_output, status_label(
+                "Inpaint", i + 1, batch_count, batch_size
+            )
 
     return generated_imgs, text_output
 
@@ -494,16 +499,14 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
                         show_label=False,
                         elem_id="gallery",
                     ).style(columns=[2], object_fit="contain")
-                    output_dir = (
-                        args.output_dir if args.output_dir else Path.cwd()
-                    )
-                    output_dir = Path(output_dir, "generated_imgs")
                     std_output = gr.Textbox(
-                        value=f"Images will be saved at {output_dir}",
+                        value=f"Images will be saved at {get_generated_imgs_path()}",
                         lines=1,
                         elem_id="std_output",
                         show_label=False,
                     )
+                    inpaint_status = gr.Textbox(visible=False)
+
                 with gr.Row():
                     inpaint_sendto_img2img = gr.Button(value="SendTo Img2Img")
                     inpaint_sendto_outpaint = gr.Button(
@@ -541,13 +544,20 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
                 lora_hf_id,
                 ondemand,
             ],
-            outputs=[inpaint_gallery, std_output],
+            outputs=[inpaint_gallery, std_output, inpaint_status],
             show_progress=args.progress_bar,
         )
+        status_kwargs = dict(
+            fn=lambda bc, bs: status_label("Inpaint", 0, bc, bs),
+            inputs=[batch_count, batch_size],
+            outputs=inpaint_status,
+        )
 
-        prompt_submit = prompt.submit(**kwargs)
-        neg_prompt_submit = negative_prompt.submit(**kwargs)
-        generate_click = stable_diffusion.click(**kwargs)
+        prompt_submit = prompt.submit(**status_kwargs).then(**kwargs)
+        neg_prompt_submit = negative_prompt.submit(**status_kwargs).then(
+            **kwargs
+        )
+        generate_click = stable_diffusion.click(**status_kwargs).then(**kwargs)
         stop_batch.click(
             fn=cancel_sd,
             cancels=[prompt_submit, neg_prompt_submit, generate_click],

apps/stable_diffusion/web/ui/outpaint_ui.py

@@ -1,8 +1,6 @@
-from pathlib import Path
 import os
 import torch
 import time
-import sys
 import gradio as gr
 from PIL import Image
 import base64
@@ -23,10 +21,13 @@ from apps.stable_diffusion.src import (
     get_schedulers,
     set_init_device_flags,
     utils,
-    clear_all,
     save_output_img,
 )
-from apps.stable_diffusion.src.utils import get_generation_text_info
+from apps.stable_diffusion.src.utils import (
+    get_generated_imgs_path,
+    get_generation_text_info,
+)
+from apps.stable_diffusion.web.utils.common_label_calc import status_label
 
 
 # set initial values of iree_vulkan_target_triple, use_tuned and import_mlir.
@@ -222,9 +223,11 @@ def outpaint_inf(
         else:
             save_output_img(out_imgs[0], img_seed)
             generated_imgs.extend(out_imgs)
-            yield generated_imgs, text_output
+            yield generated_imgs, text_output, status_label(
+                "Outpaint", i + 1, batch_count, batch_size
+            )
 
-    return generated_imgs, text_output
+    return generated_imgs, text_output, ""
 
 
 def decode_base64_to_image(encoding):
@@ -524,16 +527,13 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
                         show_label=False,
                         elem_id="gallery",
                     ).style(columns=[2], object_fit="contain")
-                    output_dir = (
-                        args.output_dir if args.output_dir else Path.cwd()
-                    )
-                    output_dir = Path(output_dir, "generated_imgs")
                     std_output = gr.Textbox(
-                        value=f"Images will be saved at {output_dir}",
+                        value=f"Images will be saved at {get_generated_imgs_path()}",
                         lines=1,
                         elem_id="std_output",
                         show_label=False,
                     )
+                    outpaint_status = gr.Textbox(visible=False)
                 with gr.Row():
                     outpaint_sendto_img2img = gr.Button(value="SendTo Img2Img")
                     outpaint_sendto_inpaint = gr.Button(value="SendTo Inpaint")
@@ -572,13 +572,20 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
                 lora_hf_id,
                 ondemand,
             ],
-            outputs=[outpaint_gallery, std_output],
+            outputs=[outpaint_gallery, std_output, outpaint_status],
             show_progress=args.progress_bar,
         )
+        status_kwargs = dict(
+            fn=lambda bc, bs: status_label("Outpaint", 0, bc, bs),
+            inputs=[batch_count, batch_size],
+            outputs=outpaint_status,
+        )
 
-        prompt_submit = prompt.submit(**kwargs)
-        neg_prompt_submit = negative_prompt.submit(**kwargs)
-        generate_click = stable_diffusion.click(**kwargs)
+        prompt_submit = prompt.submit(**status_kwargs).then(**kwargs)
+        neg_prompt_submit = negative_prompt.submit(**status_kwargs).then(
+            **kwargs
+        )
+        generate_click = stable_diffusion.click(**status_kwargs).then(**kwargs)
         stop_batch.click(
             fn=cancel_sd,
             cancels=[prompt_submit, neg_prompt_submit, generate_click],

apps/stable_diffusion/web/ui/outputgallery_ui.py

@@ -0,0 +1,434 @@
+import glob
+import gradio as gr
+import os
+from PIL import Image
+
+from apps.stable_diffusion.src import args
+from apps.stable_diffusion.src.utils import (
+    get_generated_imgs_path,
+    get_generated_imgs_todays_subdir,
+)
+from apps.stable_diffusion.web.ui.utils import nodlogo_loc
+from apps.stable_diffusion.web.utils.gradio_configs import (
+    gradio_tmp_galleries_folder,
+)
+from apps.stable_diffusion.web.utils.metadata import displayable_metadata
+
+# -- Functions for file, directory and image info querying
+
+output_dir = get_generated_imgs_path()
+
+
+def outputgallery_filenames(subdir) -> list[str]:
+    new_dir_path = os.path.join(output_dir, subdir)
+    if os.path.exists(new_dir_path):
+        filenames = [
+            glob.glob(new_dir_path + "/" + ext)
+            for ext in ("*.png", "*.jpg", "*.jpeg")
+        ]
+
+        return sorted(sum(filenames, []), key=os.path.getmtime, reverse=True)
+    else:
+        return []
+
+
+def output_subdirs() -> list[str]:
+    # Gets a list of subdirectories of output_dir and below, as relative paths.
+    relative_paths = [
+        os.path.relpath(entry[0], output_dir)
+        for entry in os.walk(
+            output_dir, followlinks=args.output_gallery_followlinks
+        )
+    ]
+
+    # It is less confusing to always including the subdir that will take any images generated
+    # today even if it doesn't exist yet
+    if get_generated_imgs_todays_subdir() not in relative_paths:
+        relative_paths.append(get_generated_imgs_todays_subdir())
+
+    # sort subdirectories so that that the date named ones we probably created in this or
+    # previous sessions come first, sorted with the most recent first. Other subdirs are listed
+    # after.
+    generated_paths = sorted(
+        [path for path in relative_paths if path.isnumeric()], reverse=True
+    )
+    result_paths = generated_paths + sorted(
+        [
+            path
+            for path in relative_paths
+            if (not path.isnumeric()) and path != "."
+        ]
+    )
+
+    return result_paths
+
+
+# clear zero length temporary files that gradio 3.22.0 buggily creates
+# TODO: remove once gradio is upgraded to or past 3.32.0
+def clear_zero_length_temps():
+    zero_length_temps = [
+        os.path.join(root, file)
+        for root, dirs, files in os.walk(gradio_tmp_galleries_folder)
+        for file in files
+        if os.path.getsize(os.path.join(root, file)) == 0
+    ]
+    for file in zero_length_temps:
+        os.remove(file)
+
+
+# --- Define UI layout for Gradio
+
+with gr.Blocks() as outputgallery_web:
+    nod_logo = Image.open(nodlogo_loc)
+
+    with gr.Row(elem_id="outputgallery_gallery"):
+        # needed to workaround gradio issue: https://github.com/gradio-app/gradio/issues/2907
+        dev_null = gr.Textbox("", visible=False)
+
+        gallery_files = gr.State(value=[])
+        subdirectory_paths = gr.State(value=[])
+
+        with gr.Column(scale=6):
+            logo = gr.Image(
+                label="Getting subdirectories...",
+                value=nod_logo,
+                interactive=False,
+                visible=True,
+                show_label=True,
+                elem_id="top_logo",
+                elem_classes="logo_centered",
+            )
+
+            gallery = gr.Gallery(
+                label="",
+                value=gallery_files.value,
+                visible=False,
+                show_label=True,
+            ).style(grid=4)
+            gallery.DEFAULT_TEMP_DIR = gradio_tmp_galleries_folder
+
+        with gr.Column(scale=4):
+            with gr.Box():
+                with gr.Row():
+                    with gr.Column(scale=16, min_width=160):
+                        subdirectories = gr.Dropdown(
+                            label=f"Subdirectories of {output_dir}",
+                            type="value",
+                            choices=subdirectory_paths.value,
+                            value="",
+                            interactive=True,
+                        ).style(container=False)
+                    with gr.Column(
+                        scale=1, min_width=32, elem_id="output_refresh_button"
+                    ):
+                        refresh = gr.Button(
+                            variant="secondary",
+                            value="\u21BB",  # unicode clockwise arrow circle
+                        ).style(size="sm")
+
+            image_columns = gr.Slider(
+                label="Columns shown", value=4, minimum=1, maximum=16, step=1
+            )
+            outputgallery_filename = gr.Textbox(
+                label="Filename", value="None", interactive=False
+            ).style(show_copy_button=True)
+
+            with gr.Accordion(
+                label="Parameter Information", open=False
+            ) as parameters_accordian:
+                image_parameters = gr.DataFrame(
+                    headers=["Parameter", "Value"],
+                    col_count=2,
+                    wrap=True,
+                    elem_classes="output_parameters_dataframe",
+                    value=[["Status", "No image selected"]],
+                )
+
+            with gr.Accordion(label="Send To", open=True):
+                with gr.Row():
+                    outputgallery_sendto_txt2img = gr.Button(
+                        value="Txt2Img",
+                        interactive=False,
+                        elem_classes="outputgallery_sendto",
+                    ).style(size="sm")
+
+                    outputgallery_sendto_img2img = gr.Button(
+                        value="Img2Img",
+                        interactive=False,
+                        elem_classes="outputgallery_sendto",
+                    ).style(size="sm")
+
+                    outputgallery_sendto_inpaint = gr.Button(
+                        value="Inpaint",
+                        interactive=False,
+                        elem_classes="outputgallery_sendto",
+                    ).style(size="sm")
+
+                    outputgallery_sendto_outpaint = gr.Button(
+                        value="Outpaint",
+                        interactive=False,
+                        elem_classes="outputgallery_sendto",
+                    ).style(size="sm")
+
+                    outputgallery_sendto_upscaler = gr.Button(
+                        value="Upscaler",
+                        interactive=False,
+                        elem_classes="outputgallery_sendto",
+                    ).style(size="sm")
+
+    # --- Event handlers
+
+    def on_clear_gallery():
+        clear_zero_length_temps()
+        return [
+            gr.Gallery.update(
+                value=[],
+                visible=False,
+            ),
+            gr.Image.update(
+                visible=True,
+            ),
+        ]
+
+    def on_select_subdir(subdir) -> list:
+        # evt.value is the subdirectory name
+        new_images = outputgallery_filenames(subdir)
+        new_label = (
+            f"{len(new_images)} images in {os.path.join(output_dir, subdir)}"
+        )
+        return [
+            new_images,
+            gr.Gallery.update(
+                value=new_images,
+                label=new_label,
+                visible=len(new_images) > 0,
+            ),
+            gr.Image.update(
+                label=new_label,
+                visible=len(new_images) == 0,
+            ),
+        ]
+
+    def on_refresh(current_subdir: str) -> list:
+        # get an up to date subdirectory list
+        refreshed_subdirs = output_subdirs()
+        # get the images using either the current subdirectory or the most recent valid one
+        new_subdir = (
+            current_subdir
+            if current_subdir in refreshed_subdirs
+            else refreshed_subdirs[0]
+        )
+        new_images = outputgallery_filenames(new_subdir)
+        new_label = f"{len(new_images)} images in {os.path.join(output_dir, new_subdir)}"
+
+        return [
+            gr.Dropdown.update(
+                choices=refreshed_subdirs,
+                value=new_subdir,
+            ),
+            refreshed_subdirs,
+            new_images,
+            gr.Gallery.update(
+                value=new_images, label=new_label, visible=len(new_images) > 0
+            ),
+            gr.Image.update(
+                label=new_label,
+                visible=len(new_images) == 0,
+            ),
+        ]
+
+    def on_new_image(subdir, subdir_paths, status) -> list:
+        # prevent error triggered when an image generates before the tab has even been selected
+        subdir_paths = (
+            subdir_paths
+            if len(subdir_paths) > 0
+            else [get_generated_imgs_todays_subdir()]
+        )
+
+        # only update if the current subdir is the most recent one as new images only go there
+        if subdir_paths[0] == subdir:
+            clear_zero_length_temps()
+            new_images = outputgallery_filenames(subdir)
+            new_label = f"{len(new_images)} images in {os.path.join(output_dir, subdir)} - {status}"
+
+            return [
+                new_images,
+                gr.Gallery.update(
+                    value=new_images,
+                    label=new_label,
+                    visible=len(new_images) > 0,
+                ),
+                gr.Image.update(
+                    label=new_label,
+                    visible=len(new_images) == 0,
+                ),
+            ]
+        else:
+            # otherwise change nothing, (only untyped gradio gr.update() does this)
+            return [gr.update(), gr.update(), gr.update()]
+
+    def on_select_image(images: list[str], evt: gr.SelectData) -> list:
+        # evt.index is an index into the full list of filenames for the current subdirectory
+        filename = images[evt.index]
+        params = displayable_metadata(filename)
+
+        if params:
+            return [
+                filename,
+                list(map(list, params["parameters"].items())),
+            ]
+
+        return [
+            filename,
+            [["Status", "No parameters found"]],
+        ]
+
+    def on_outputgallery_filename_change(filename: str) -> list:
+        exists = filename != "None" and os.path.exists(filename)
+        return [
+            # disable or enable each of the sendto button based on whether an image is selected
+            gr.Button.update(interactive=exists),
+            gr.Button.update(interactive=exists),
+            gr.Button.update(interactive=exists),
+            gr.Button.update(interactive=exists),
+            gr.Button.update(interactive=exists),
+            gr.Button.update(interactive=exists),
+        ]
+
+    # The time first our tab is selected we need to do an initial refresh to populate
+    # the subdirectory select box and the images from the most recent subdirectory.
+    #
+    # We do it at this point rather than setting this up in the controls' definitions
+    # as when you refresh the browser you always get what was *initially* set, which
+    # won't include any new subdirectories or images that might have created since
+    # the application was started. Doing it this way means a browser refresh/reload
+    # always gets the most up to date data.
+    def on_select_tab(subdir_paths):
+        if len(subdir_paths) == 0:
+            return on_refresh("")
+        else:
+            return (
+                # Change nothing, (only untyped gr.update() does this)
+                gr.update(),
+                gr.update(),
+                gr.update(),
+                gr.update(),
+                gr.update(),
+            )
+
+    # Unfortunately as of gradio 3.22.0 gr.update against Galleries doesn't support
+    # things set with .style, nor the elem_classes kwarg so we have to directly set
+    # things up via JavaScript if we want the client to take notice of any of our
+    # changes to the number of columns after it decides to put them back to the
+    # original number when we change something
+    def js_set_columns_in_browser(timeout_length):
+        return f"""
+            (new_cols) => {{
+                setTimeout(() => {{
+                    required_style = "auto ".repeat(new_cols).trim();
+                    gallery = document.querySelector('#outputgallery_gallery .grid-container');
+                    if (gallery) {{
+                        gallery.style.gridTemplateColumns = required_style
+                    }}
+                }}, {timeout_length});
+                return [];      // prevents console error from gradio
+            }}
+        """
+
+    # --- Wire handlers up to the actions
+
+    # - Many actions reset the number of columns shown in the gallery on the browser end,
+    #   so we have to set them back to what we think they should be after the initial
+    #   action.
+    # - None of the actions on this tab trigger inference, and we want the user to be able
+    #   to do them whilst other tabs have ongoing inference running. Waiting in the queue
+    #   behind inference jobs would mean the UI can't fully respond until the inference tasks
+    #   complete, hence queue=False on all of these.
+    set_gallery_columns_immediate = dict(
+        fn=None,
+        inputs=[image_columns],
+        # gradio blanks the UI on Chrome on Linux on gallery select if I don't put an output here
+        outputs=[dev_null],
+        _js=js_set_columns_in_browser(0),
+        queue=False,
+    )
+
+    # setting columns after selecting a gallery item needs a real timeout length for the
+    # number of columns to actually be applied. Not really sure why, maybe something has
+    # to finish animating?
+    set_gallery_columns_delayed = dict(
+        set_gallery_columns_immediate, _js=js_set_columns_in_browser(250)
+    )
+
+    # clearing images when we need to completely change what's in the gallery avoids current
+    # images being shown replacing piecemeal and prevents weirdness and errors if the user
+    # selects an image during the replacement phase.
+    clear_gallery = dict(
+        fn=on_clear_gallery,
+        inputs=None,
+        outputs=[gallery, logo],
+        queue=False,
+    )
+
+    image_columns.change(**set_gallery_columns_immediate)
+
+    subdirectories.select(**clear_gallery).then(
+        on_select_subdir,
+        [subdirectories],
+        [gallery_files, gallery, logo],
+        queue=False,
+    ).then(**set_gallery_columns_immediate)
+
+    refresh.click(**clear_gallery).then(
+        on_refresh,
+        [subdirectories],
+        [subdirectories, subdirectory_paths, gallery_files, gallery, logo],
+        queue=False,
+    ).then(**set_gallery_columns_immediate)
+
+    gallery.select(
+        on_select_image,
+        [gallery_files],
+        [outputgallery_filename, image_parameters],
+        queue=False,
+    ).then(**set_gallery_columns_delayed)
+
+    outputgallery_filename.change(
+        on_outputgallery_filename_change,
+        [outputgallery_filename],
+        [
+            outputgallery_sendto_txt2img,
+            outputgallery_sendto_img2img,
+            outputgallery_sendto_inpaint,
+            outputgallery_sendto_outpaint,
+            outputgallery_sendto_upscaler,
+        ],
+        queue=False,
+    )
+
+    # We should have been given the .select function for our tab, so set it up
+    def outputgallery_tab_select(select):
+        select(
+            fn=on_select_tab,
+            inputs=[subdirectory_paths],
+            outputs=[
+                subdirectories,
+                subdirectory_paths,
+                gallery_files,
+                gallery,
+                logo,
+            ],
+            queue=False,
+        ).then(**set_gallery_columns_immediate)
+
+    # We should have been passed a list of components on other tabs that update
+    # when a new image has generated on that tab, so set things up so the user
+    # will see that new image if they are looking at today's subdirectory
+    def outputgallery_watch(components: gr.Textbox):
+        for component in components:
+            component.change(
+                on_new_image,
+                inputs=[subdirectories, subdirectory_paths, component],
+                outputs=[gallery_files, gallery, logo],
+                queue=False,
+            ).then(**set_gallery_columns_immediate)

apps/stable_diffusion/web/ui/stablelm_ui.py

@@ -1,17 +1,9 @@
 import gradio as gr
 import torch
 import os
-from apps.language_models.scripts.stablelm import (
-    compile_stableLM,
-    StopOnTokens,
-    generate,
-    get_tokenizer,
-    StableLMModel,
-)
+from pathlib import Path
 from transformers import (
     AutoModelForCausalLM,
-    TextIteratorStreamer,
-    StoppingCriteriaList,
 )
 from apps.stable_diffusion.web.ui.utils import available_devices
 
@@ -28,31 +20,39 @@ def user(message, history):
     return "", history + [[message, ""]]
 
 
-input_ids = torch.randint(3, (1, 256))
-attention_mask = torch.randint(3, (1, 256))
-
-
 sharkModel = 0
 sharded_model = 0
+vicuna_model = 0
 
 
 start_message_vicuna = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
 past_key_values = None
 
 
-def chat(curr_system_message, history, model):
+def chat(curr_system_message, history, model, device, precision):
+    print(f"In chat for {model}")
     global sharded_model
     global past_key_values
+    global vicuna_model
     if "vicuna" in model:
-        from apps.language_models.scripts.sharded_vicuna_fp32 import (
-            tokenizer,
-            get_sharded_model,
+        from apps.language_models.src.pipelines.vicuna_pipeline import (
+            Vicuna,
         )
 
-        SAMPLE_INPUT_LEN = 137
         curr_system_message = start_message_vicuna
-        if sharded_model == 0:
-            sharded_model = get_sharded_model()
+        if vicuna_model == 0:
+            first_vic_vmfb_path = Path("first_vicuna.vmfb")
+            second_vic_vmfb_path = Path("second_vicuna.vmfb")
+            if "cuda" in device:
+                device = "cuda"
+            vicuna_model = Vicuna(
+                "vicuna",
+                hf_model_path=model,
+                device=device,
+                precision=precision,
+                first_vicuna_vmfb_path=first_vic_vmfb_path,
+                second_vicuna_vmfb_path=second_vic_vmfb_path,
+            )
         messages = curr_system_message + "".join(
             [
                 "".join(["<|USER|>" + item[0], "<|ASSISTANT|>" + item[1]])
@@ -61,59 +61,30 @@ def chat(curr_system_message, history, model):
         )
         prompt = messages.strip()
         print("prompt = ", prompt)
-        input_ids = tokenizer(prompt).input_ids
-        new_sentence = ""
-        for _ in range(200):
-            original_input_ids = input_ids
-            input_id_len = len(input_ids)
-            pad_len = SAMPLE_INPUT_LEN - input_id_len
-            attention_mask = torch.ones([1, input_id_len], dtype=torch.int64)
-            input_ids = torch.tensor(input_ids)
-            input_ids = input_ids.reshape([1, input_id_len])
-            attention_mask = torch.nn.functional.pad(
-                torch.tensor(attention_mask),
-                (0, pad_len),
-                mode="constant",
-                value=0,
-            )
+        sentence = vicuna_model.generate(prompt)
 
-            if _ == 0:
-                output = sharded_model.forward(input_ids, is_first=True)
-            else:
-                output = sharded_model.forward(
-                    input_ids, past_key_values=past_key_values, is_first=False
-                )
-            logits = output["logits"]
-            past_key_values = output["past_key_values"]
-            new_word = tokenizer.decode(torch.argmax(logits[:, -1, :], dim=1))
-            if new_word == "</s>":
-                break
-            new_sentence += " " + new_word
-            history[-1][1] = new_sentence
+        partial_text = ""
+        for new_text in sentence.split(" "):
+            # print(new_text)
+            partial_text += new_text + " "
+            history[-1][1] = partial_text
+            # Yield an empty string to cleanup the message textbox and the updated conversation history
             yield history
-            next_token = torch.argmax(logits[:, input_id_len - 1, :], dim=1)
-            original_input_ids.append(next_token)
-            input_ids = [next_token]
-        print(new_sentence)
+        history[-1][1] = sentence
         return history
 
+    # else Model is StableLM
     global sharkModel
-    print("In chat")
+    from apps.language_models.src.pipelines.stablelm_pipeline import (
+        SharkStableLM,
+    )
+
     if sharkModel == 0:
-        tok = get_tokenizer()
-        # sharkModel = compile_stableLM(None, tuple([input_ids, attention_mask]), "stableLM_linalg_f32_seqLen256", "/home/shark/disk/phaneesh/stablelm_3b_f32_cuda_2048_newflags.vmfb")
-        m = AutoModelForCausalLM.from_pretrained(
-            "stabilityai/stablelm-tuned-alpha-3b", torch_dtype=torch.float32
-        )
-        stableLMModel = StableLMModel(m)
-        sharkModel = compile_stableLM(
-            stableLMModel,
-            tuple([input_ids, attention_mask]),
-            "stableLM_linalg_f32_seqLen256",
-            os.getcwd(),
-        )
-    # Initialize a StopOnTokens object
-    stop = StopOnTokens()
+        # max_new_tokens=512
+        shark_slm = SharkStableLM(
+            "StableLM"
+        )  # pass elements from UI as required
+
     # Construct the input message string for the model by concatenating the current system message and conversation history
     if len(curr_system_message.split()) > 160:
         print("clearing context")
@@ -124,24 +95,11 @@ def chat(curr_system_message, history, model):
             for item in history
         ]
     )
-    # print(messages)
-    # Tokenize the messages string
-    streamer = TextIteratorStreamer(
-        tok, timeout=10.0, skip_prompt=True, skip_special_tokens=True
-    )
-    generate_kwargs = dict(
-        new_text=messages,
-        streamer=streamer,
-        max_new_tokens=512,
-        do_sample=True,
-        top_p=0.95,
-        top_k=1000,
-        temperature=1.0,
-        num_beams=1,
-        stopping_criteria=StoppingCriteriaList([stop]),
-        sharkStableLM=sharkModel,
-    )
-    words_list = generate(**generate_kwargs)
+
+    generate_kwargs = dict(prompt=messages)
+
+    words_list = shark_slm.generate(**generate_kwargs)
+
     partial_text = ""
     for new_text in words_list:
         # print(new_text)
@@ -162,17 +120,26 @@ with gr.Blocks(title="Chatbot") as stablelm_chat:
                 "TheBloke/vicuna-7B-1.1-HF",
             ],
         )
-        device_value = None
-        for d in available_devices:
-            if "vulkan" in d:
-                device_value = d
-                break
-
+        supported_devices = [
+            device for device in available_devices if "cuda" in device
+        ]
+        enabled = len(supported_devices) > 0
         device = gr.Dropdown(
             label="Device",
-            value=device_value if device_value else available_devices[0],
-            interactive=False,
-            choices=available_devices,
+            value=supported_devices[0]
+            if enabled
+            else "Only CUDA Supported for now",
+            choices=supported_devices,
+            interactive=enabled,
+        )
+        precision = gr.Radio(
+            label="Precision",
+            value="fp32",
+            choices=[
+                "fp16",
+                "fp32",
+            ],
+            visible=True,
         )
     chatbot = gr.Chatbot().style(height=500)
     with gr.Row():
@@ -181,12 +148,13 @@ with gr.Blocks(title="Chatbot") as stablelm_chat:
                 label="Chat Message Box",
                 placeholder="Chat Message Box",
                 show_label=False,
+                interactive=enabled,
             ).style(container=False)
         with gr.Column():
             with gr.Row():
-                submit = gr.Button("Submit")
-                stop = gr.Button("Stop")
-                clear = gr.Button("Clear")
+                submit = gr.Button("Submit", interactive=enabled)
+                stop = gr.Button("Stop", interactive=enabled)
+                clear = gr.Button("Clear", interactive=enabled)
     system_msg = gr.Textbox(
         start_message, label="System Message", interactive=False, visible=False
     )
@@ -195,7 +163,7 @@ with gr.Blocks(title="Chatbot") as stablelm_chat:
         fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False
     ).then(
         fn=chat,
-        inputs=[system_msg, chatbot, model],
+        inputs=[system_msg, chatbot, model, device, precision],
         outputs=[chatbot],
         queue=True,
     )
@@ -203,7 +171,7 @@ with gr.Blocks(title="Chatbot") as stablelm_chat:
         fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False
     ).then(
         fn=chat,
-        inputs=[system_msg, chatbot, model],
+        inputs=[system_msg, chatbot, model, device, precision],
         outputs=[chatbot],
         queue=True,
     )

apps/stable_diffusion/web/ui/txt2img_ui.py

@@ -1,4 +1,3 @@
-from pathlib import Path
 import os
 import torch
 import time
@@ -17,7 +16,8 @@ from apps.stable_diffusion.web.ui.utils import (
     predefined_models,
     cancel_sd,
 )
-from apps.stable_diffusion.web.utils.png_metadata import import_png_metadata
+from apps.stable_diffusion.web.utils.metadata import import_png_metadata
+from apps.stable_diffusion.web.utils.common_label_calc import status_label
 from apps.stable_diffusion.src import (
     args,
     Text2ImagePipeline,
@@ -27,7 +27,10 @@ from apps.stable_diffusion.src import (
     save_output_img,
     prompt_examples,
 )
-from apps.stable_diffusion.src.utils import get_generation_text_info
+from apps.stable_diffusion.src.utils import (
+    get_generated_imgs_path,
+    get_generation_text_info,
+)
 
 # set initial values of iree_vulkan_target_triple, use_tuned and import_mlir.
 init_iree_vulkan_target_triple = args.iree_vulkan_target_triple
@@ -202,9 +205,11 @@ def txt2img_inf(
         else:
             save_output_img(out_imgs[0], img_seed)
             generated_imgs.extend(out_imgs)
-            yield generated_imgs, text_output
+            yield generated_imgs, text_output, status_label(
+                "Text-to-Image", i + 1, batch_count, batch_size
+            )
 
-    return generated_imgs, text_output
+    return generated_imgs, text_output, ""
 
 
 def encode_pil_to_base64(images):
@@ -308,7 +313,7 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
                                 + get_custom_model_files("vae"),
                             )
                     with gr.Column(scale=1, min_width=170):
-                        png_info_img = gr.Image(
+                        txt2img_png_info_img = gr.Image(
                             label="Import PNG info",
                             elem_id="txt2img_prompt_image",
                             type="pil",
@@ -469,16 +474,13 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
                         show_label=False,
                         elem_id="gallery",
                     ).style(columns=[2], object_fit="contain")
-                    output_dir = (
-                        args.output_dir if args.output_dir else Path.cwd()
-                    )
-                    output_dir = Path(output_dir, "generated_imgs")
                     std_output = gr.Textbox(
-                        value=f"Images will be saved at {output_dir}",
+                        value=f"Images will be saved at {get_generated_imgs_path()}",
                         lines=1,
                         elem_id="std_output",
                         show_label=False,
                     )
+                    txt2img_status = gr.Textbox(visible=False)
                 with gr.Row():
                     txt2img_sendto_img2img = gr.Button(value="SendTo Img2Img")
                     txt2img_sendto_inpaint = gr.Button(value="SendTo Inpaint")
@@ -514,22 +516,30 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
                 lora_hf_id,
                 ondemand,
             ],
-            outputs=[txt2img_gallery, std_output],
+            outputs=[txt2img_gallery, std_output, txt2img_status],
             show_progress=args.progress_bar,
         )
 
-        prompt_submit = prompt.submit(**kwargs)
-        neg_prompt_submit = negative_prompt.submit(**kwargs)
-        generate_click = stable_diffusion.click(**kwargs)
+        status_kwargs = dict(
+            fn=lambda bc, bs: status_label("Text-to-Image", 0, bc, bs),
+            inputs=[batch_count, batch_size],
+            outputs=txt2img_status,
+        )
+
+        prompt_submit = prompt.submit(**status_kwargs).then(**kwargs)
+        neg_prompt_submit = negative_prompt.submit(**status_kwargs).then(
+            **kwargs
+        )
+        generate_click = stable_diffusion.click(**status_kwargs).then(**kwargs)
         stop_batch.click(
             fn=cancel_sd,
             cancels=[prompt_submit, neg_prompt_submit, generate_click],
         )
 
-        png_info_img.change(
+        txt2img_png_info_img.change(
             fn=import_png_metadata,
             inputs=[
-                png_info_img,
+                txt2img_png_info_img,
                 prompt,
                 negative_prompt,
                 steps,
@@ -542,7 +552,7 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
                 txt2img_hf_model_id,
             ],
             outputs=[
-                png_info_img,
+                txt2img_png_info_img,
                 prompt,
                 negative_prompt,
                 steps,

apps/stable_diffusion/web/ui/upscaler_ui.py

@@ -1,8 +1,6 @@
-from pathlib import Path
 import os
 import torch
 import time
-import sys
 import gradio as gr
 from PIL import Image
 import base64
@@ -17,16 +15,16 @@ from apps.stable_diffusion.web.ui.utils import (
     predefined_upscaler_models,
     cancel_sd,
 )
+from apps.stable_diffusion.web.utils.common_label_calc import status_label
 from apps.stable_diffusion.src import (
     args,
     UpscalerPipeline,
     get_schedulers,
     set_init_device_flags,
     utils,
-    clear_all,
     save_output_img,
 )
-
+from apps.stable_diffusion.src.utils import get_generated_imgs_path
 
 # set initial values of iree_vulkan_target_triple, use_tuned and import_mlir.
 init_iree_vulkan_target_triple = args.iree_vulkan_target_triple
@@ -66,6 +64,9 @@ def upscaler_inf(
         Config,
     )
     import apps.stable_diffusion.web.utils.global_obj as global_obj
+    from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+        SD_STATE_CANCEL,
+    )
 
     args.prompts = [prompt]
     args.negative_prompts = [negative_prompt]
@@ -202,13 +203,24 @@ def upscaler_inf(
                     args.use_base_vae,
                     cpu_scheduling,
                 )
-                high_res_img.paste(upscaled_image[0], (j * 4, i * 4))
+                if global_obj.get_sd_status() == SD_STATE_CANCEL:
+                    break
+                else:
+                    high_res_img.paste(upscaled_image[0], (j * 4, i * 4))
 
-        save_output_img(high_res_img, img_seed, extra_info)
-        generated_imgs.append(high_res_img)
-        seeds.append(img_seed)
-        global_obj.get_sd_obj().log += "\n"
-        yield generated_imgs, global_obj.get_sd_obj().log
+            if global_obj.get_sd_status() == SD_STATE_CANCEL:
+                break
+
+        if global_obj.get_sd_status() == SD_STATE_CANCEL:
+            break
+        else:
+            save_output_img(high_res_img, img_seed, extra_info)
+            generated_imgs.append(high_res_img)
+            seeds.append(img_seed)
+            global_obj.get_sd_obj().log += "\n"
+            yield generated_imgs, global_obj.get_sd_obj().log, status_label(
+                "Upscaler", current_batch + 1, batch_count, batch_size
+            )
 
     total_time = time.time() - start_time
     text_output = f"prompt={args.prompts}"
@@ -220,7 +232,7 @@ def upscaler_inf(
     text_output += global_obj.get_sd_obj().log
     text_output += f"\nTotal image generation time: {total_time:.4f}sec"
 
-    yield generated_imgs, text_output
+    yield generated_imgs, text_output, ""
 
 
 def decode_base64_to_image(encoding):
@@ -495,16 +507,14 @@ with gr.Blocks(title="Upscaler") as upscaler_web:
                         show_label=False,
                         elem_id="gallery",
                     ).style(columns=[2], object_fit="contain")
-                    output_dir = (
-                        args.output_dir if args.output_dir else Path.cwd()
-                    )
-                    output_dir = Path(output_dir, "generated_imgs")
                     std_output = gr.Textbox(
-                        value=f"Images will be saved at {output_dir}",
+                        value=f"Images will be saved at {get_generated_imgs_path()}",
                         lines=1,
                         elem_id="std_output",
                         show_label=False,
                     )
+                    upscaler_status = gr.Textbox(visible=False)
+
                 with gr.Row():
                     upscaler_sendto_img2img = gr.Button(value="SendTo Img2Img")
                     upscaler_sendto_inpaint = gr.Button(value="SendTo Inpaint")
@@ -539,13 +549,21 @@ with gr.Blocks(title="Upscaler") as upscaler_web:
                 lora_hf_id,
                 ondemand,
             ],
-            outputs=[upscaler_gallery, std_output],
+            outputs=[upscaler_gallery, std_output, upscaler_status],
             show_progress=args.progress_bar,
         )
-
-        prompt_submit = prompt.submit(**kwargs)
-        neg_prompt_submit = negative_prompt.submit(**kwargs)
-        generate_click = stable_diffusion.click(**kwargs)
-        stop_batch.click(
-            fn=None, cancels=[prompt_submit, neg_prompt_submit, generate_click]
+        status_kwargs = dict(
+            fn=lambda bc, bs: status_label("Upscaler", 0, bc, bs),
+            inputs=[batch_count, batch_size],
+            outputs=upscaler_status,
+        )
+
+        prompt_submit = prompt.submit(**status_kwargs).then(**kwargs)
+        neg_prompt_submit = negative_prompt.submit(**status_kwargs).then(
+            **kwargs
+        )
+        generate_click = stable_diffusion.click(**status_kwargs).then(**kwargs)
+        stop_batch.click(
+            fn=cancel_sd,
+            cancels=[prompt_submit, neg_prompt_submit, generate_click],
         )

apps/stable_diffusion/web/utils/common_label_calc.py

@@ -0,0 +1,9 @@
+# functions for generating labels used in common by tabs across the UI
+
+
+def status_label(tab_name, batch_index=0, batch_count=1, batch_size=1):
+    if batch_index < batch_count:
+        bs = f"x{batch_size}" if batch_size > 1 else ""
+        return f"{tab_name} generating {batch_index+1}/{batch_count}{bs}"
+    else:
+        return f"{tab_name} complete"

apps/stable_diffusion/web/utils/gradio_configs.py

@@ -1,19 +1,48 @@
 import os
+import shutil
 import tempfile
 import gradio
-from os import listdir
+from time import time
 
 gradio_tmp_imgs_folder = os.path.join(os.getcwd(), "shark_tmp/")
+gradio_tmp_galleries_folder = os.path.join(gradio_tmp_imgs_folder, "galleries")
 
 
 # Clear all gradio tmp images
 def clear_gradio_tmp_imgs_folder():
     if not os.path.exists(gradio_tmp_imgs_folder):
         return
-    for fileName in listdir(gradio_tmp_imgs_folder):
-        # Delete tmp png files
-        if fileName.startswith("tmp") and fileName.endswith(".png"):
-            os.remove(gradio_tmp_imgs_folder + fileName)
+
+    # clear all gradio tmp files created by generation galleries
+    print(
+        "Clearing gradio temporary image files from a prior run. This may take some time..."
+    )
+    image_files = [
+        filename
+        for filename in os.listdir(gradio_tmp_imgs_folder)
+        if os.path.isfile(os.path.join(gradio_tmp_imgs_folder, filename))
+        and filename.startswith("tmp")
+        and filename.endswith(".png")
+    ]
+    if len(image_files) > 0:
+        cleanup_start = time()
+        for filename in image_files:
+            os.remove(gradio_tmp_imgs_folder + filename)
+        print(
+            f"Clearing generation temporary image files took {time() - cleanup_start:4f} seconds"
+        )
+    else:
+        print("no generation temporary files to clear")
+
+    # Clear all gradio tmp files created by output galleries
+    if os.path.exists(gradio_tmp_galleries_folder):
+        cleanup_start = time()
+        shutil.rmtree(gradio_tmp_galleries_folder, ignore_errors=True)
+        print(
+            f"Clearing output gallery temporary image files took {time() - cleanup_start:4f} seconds"
+        )
+    else:
+        print("no output gallery temporary files to clear")
 
 
 # Overwrite save_pil_to_file from gradio to save tmp images generated by gradio into our own tmp folder

apps/stable_diffusion/web/utils/metadata/__init__.py

@@ -0,0 +1,6 @@
+from .png_metadata import (
+    import_png_metadata,
+)
+from .display import (
+    displayable_metadata,
+)

apps/stable_diffusion/web/utils/metadata/csv_metadata.py

@@ -0,0 +1,31 @@
+import csv
+import os
+from .format import humanize, humanizable
+
+
+def csv_path(image_filename: str):
+    return os.path.join(os.path.dirname(image_filename), "imgs_details.csv")
+
+
+def has_csv(image_filename: str) -> bool:
+    return os.path.exists(csv_path(image_filename))
+
+
+def parse_csv(image_filename: str):
+    # We use a reader instead of a DictReader here for images_details.csv files due to the lack of
+    # headers, and then match up the return list for each row with our guess at which column format
+    # the file is using.
+
+    # we assume the final column of the csv has the original filename with full path and match that
+    # against the image_filename. We then exclude the filename from the output, hence the -1's.
+    csv_filename = csv_path(image_filename)
+
+    matches = [
+        humanize(row)
+        for row in csv.reader(open(csv_filename, "r", newline=""))
+        if row
+        and humanizable(row)
+        and os.path.basename(image_filename) in row[-1]
+    ]
+
+    return matches[0] if matches else {}

apps/stable_diffusion/web/utils/metadata/display.py

@@ -0,0 +1,50 @@
+import json
+import os
+from PIL import Image
+from .png_metadata import parse_generation_parameters
+from .exif_metadata import has_exif, parse_exif
+from .csv_metadata import has_csv, parse_csv
+from .format import compact, humanize
+
+
+def displayable_metadata(image_filename: str) -> dict:
+    pil_image = Image.open(image_filename)
+
+    # we have PNG generation parameters (preferred, as it's what the txt2img dropzone reads,
+    # and we go via that for SendTo, and is directly tied to the image)
+    if "parameters" in pil_image.info:
+        return {
+            "source": "png",
+            "parameters": compact(
+                parse_generation_parameters(pil_image.info["parameters"])
+            ),
+        }
+
+    # we have a matching json file (next most likely to be accurate when it's there)
+    json_path = os.path.splitext(image_filename)[0] + ".json"
+    if os.path.isfile(json_path):
+        with open(json_path) as params_file:
+            return {
+                "source": "json",
+                "parameters": compact(
+                    humanize(json.load(params_file), includes_filename=False)
+                ),
+            }
+
+    # we have a CSV file so try that (can be different shapes, and it usually has no
+    # headers/param names so of the things we we *know* have parameters, it's the
+    # last resort)
+    if has_csv(image_filename):
+        params = parse_csv(image_filename)
+        if params:  # we might not have found the filename in the csv
+            return {
+                "source": "csv",
+                "parameters": compact(params),  # already humanized
+            }
+
+    # EXIF data, probably a .jpeg, may well not include parameters, but at least it's *something*
+    if has_exif(image_filename):
+        return {"source": "exif", "parameters": parse_exif(pil_image)}
+
+    # we've got nothing
+    return None

apps/stable_diffusion/web/utils/metadata/exif_metadata.py

@@ -0,0 +1,52 @@
+from PIL import Image
+from PIL.ExifTags import Base as EXIFKeys, TAGS, IFD, GPSTAGS
+
+
+def has_exif(image_filename: str) -> bool:
+    return True if Image.open(image_filename).getexif() else False
+
+
+def parse_exif(pil_image: Image) -> dict:
+    img_exif = pil_image.getexif()
+
+    # See this stackoverflow answer for where most this comes from: https://stackoverflow.com/a/75357594
+    # I did try to use the exif library but it broke just as much as my initial attempt at this (albeit I
+    # I was probably using it wrong) so I reverted back to using PIL with more filtering and saved a
+    # dependency
+    exif_tags = {
+        TAGS.get(key, key): str(val)
+        for (key, val) in img_exif.items()
+        if key in TAGS
+        and key not in (EXIFKeys.ExifOffset, EXIFKeys.GPSInfo)
+        and val
+        and (not isinstance(val, bytes))
+        and (not str(val).isspace())
+    }
+
+    def try_get_ifd(ifd_id):
+        try:
+            return img_exif.get_ifd(ifd_id).items()
+        except KeyError:
+            return {}
+
+    ifd_tags = {
+        TAGS.get(key, key): str(val)
+        for ifd_id in IFD
+        for (key, val) in try_get_ifd(ifd_id)
+        if ifd_id != IFD.GPSInfo
+        and key in TAGS
+        and val
+        and (not isinstance(val, bytes))
+        and (not str(val).isspace())
+    }
+
+    gps_tags = {
+        GPSTAGS.get(key, key): str(val)
+        for (key, val) in try_get_ifd(IFD.GPSInfo)
+        if key in GPSTAGS
+        and val
+        and (not isinstance(val, bytes))
+        and (not str(val).isspace())
+    }
+
+    return {**exif_tags, **ifd_tags, **gps_tags}

apps/stable_diffusion/web/utils/metadata/format.py

@@ -0,0 +1,115 @@
+# As SHARK has evolved more columns have been added to images_details.csv. However, since
+# no version of the CSV has any headers (yet) we don't actually have anything within the
+# file that tells us which parameter each column is for. So this is a list of known patterns
+# indexed by length which is what we're going to have to use to guess which columns are the
+# right ones for the file we're looking at.
+
+# The same ordering is used for JSON, but these do have key names, however they are not very
+# human friendly, nor do they match up with the what is written to the .png headers
+
+# So these are functions to try and get something consistent out the raw input from all
+# these sources
+
+PARAMS_FORMATS = {
+    9: {
+        "VARIANT": "Model",
+        "SCHEDULER": "Sampler",
+        "PROMPT": "Prompt",
+        "NEG_PROMPT": "Negative prompt",
+        "SEED": "Seed",
+        "CFG_SCALE": "CFG scale",
+        "PRECISION": "Precision",
+        "STEPS": "Steps",
+        "OUTPUT": "Filename",
+    },
+    10: {
+        "MODEL": "Model",
+        "VARIANT": "Variant",
+        "SCHEDULER": "Sampler",
+        "PROMPT": "Prompt",
+        "NEG_PROMPT": "Negative prompt",
+        "SEED": "Seed",
+        "CFG_SCALE": "CFG scale",
+        "PRECISION": "Precision",
+        "STEPS": "Steps",
+        "OUTPUT": "Filename",
+    },
+    12: {
+        "VARIANT": "Model",
+        "SCHEDULER": "Sampler",
+        "PROMPT": "Prompt",
+        "NEG_PROMPT": "Negative prompt",
+        "SEED": "Seed",
+        "CFG_SCALE": "CFG scale",
+        "PRECISION": "Precision",
+        "STEPS": "Steps",
+        "HEIGHT": "Height",
+        "WIDTH": "Width",
+        "MAX_LENGTH": "Max Length",
+        "OUTPUT": "Filename",
+    },
+}
+
+PARAMS_FORMAT_LONGEST = PARAMS_FORMATS[max(PARAMS_FORMATS.keys())]
+
+
+def compact(metadata: dict) -> dict:
+    # we don't want to alter the original dictionary
+    result = dict(metadata)
+
+    # discard the filename because we should already have it
+    if result.keys() & {"Filename"}:
+        result.pop("Filename")
+
+    # make showing the sizes more compact by using only one line each
+    if result.keys() & {"Size-1", "Size-2"}:
+        result["Size"] = f"{result.pop('Size-1')}x{result.pop('Size-2')}"
+    elif result.keys() & {"Height", "Width"}:
+        result["Size"] = f"{result.pop('Height')}x{result.pop('Width')}"
+
+    if result.keys() & {"Hires resize-1", "Hires resize-1"}:
+        hires_y = result.pop("Hires resize-1")
+        hires_x = result.pop("Hires resize-2")
+
+        if hires_x == 0 and hires_y == 0:
+            result["Hires resize"] = "None"
+        else:
+            result["Hires resize"] = f"{hires_y}x{hires_x}"
+
+    return result
+
+
+def humanizable(metadata: dict | list[str], includes_filename=True) -> dict:
+    lookup_key = len(metadata) + (0 if includes_filename else 1)
+    return lookup_key in PARAMS_FORMATS.keys()
+
+
+def humanize(metadata: dict | list[str], includes_filename=True) -> dict:
+    lookup_key = len(metadata) + (0 if includes_filename else 1)
+
+    # For lists we can only work based on the length, we have no other information
+    if isinstance(metadata, list):
+        if humanizable(metadata, includes_filename):
+            return dict(zip(PARAMS_FORMATS[lookup_key].values(), metadata))
+        else:
+            raise KeyError(
+                f"Humanize could not find the format for a parameter list of length {len(metadata)}"
+            )
+
+    # For dictionaries we try to use the matching length parameter format if
+    # available, otherwise we use the longest. Then we swap keys in the
+    # metadata that match keys in the format for the friendlier name that we
+    # have set in the format value
+    if isinstance(metadata, dict):
+        if humanizable(metadata, includes_filename):
+            format = PARAMS_FORMATS[lookup_key]
+        else:
+            format = PARAMS_FORMAT_LONGEST
+
+        return {
+            format[key]: value
+            for (key, value) in metadata.items()
+            if key in format.keys()
+        }
+
+    raise TypeError("Can only humanize parameter lists or dictionaries")

cpp/README.md

@@ -40,7 +40,7 @@ cmake --build build/
 *Prepare the model*
 ```bash
 wget https://storage.googleapis.com/shark_tank/latest/resnet50_tf/resnet50_tf.mlir
-iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --iree-llvmcpu-embedded-linker-path=`python3 -c 'import sysconfig; print(sysconfig.get_paths()["purelib"])'`/iree/compiler/tools/../_mlir_libs/iree-lld --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --mlir-pass-pipeline-crash-reproducer=ist/core-reproducer.mlir --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 resnet50_tf.mlir -o resnet50_tf.vmfb
+iree-compile --iree-input-type=auto --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --iree-llvmcpu-embedded-linker-path=`python3 -c 'import sysconfig; print(sysconfig.get_paths()["purelib"])'`/iree/compiler/tools/../_mlir_libs/iree-lld --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --mlir-pass-pipeline-crash-reproducer=ist/core-reproducer.mlir --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 resnet50_tf.mlir -o resnet50_tf.vmfb
 ```
 *Prepare the input*
 
@@ -65,18 +65,18 @@ A tool for benchmarking other models is built and can be invoked with a command
 see `./build/vulkan_gui/iree-vulkan-gui --help` for an explanation on the function input. For example, stable diffusion unet can be tested with the following commands:
 ```bash
 wget https://storage.googleapis.com/shark_tank/quinn/stable_diff_tf/stable_diff_tf.mlir
-iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 stable_diff_tf.mlir -o stable_diff_tf.vmfb
+iree-compile --iree-input-type=auto --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 stable_diff_tf.mlir -o stable_diff_tf.vmfb
 ./build/vulkan_gui/iree-vulkan-gui --module-file=stable_diff_tf.vmfb --function_input=2x4x64x64xf32 --function_input=1xf32 --function_input=2x77x768xf32
 ```
 VAE and Autoencoder are also available
 ```bash
 # VAE
 wget https://storage.googleapis.com/shark_tank/quinn/stable_diff_tf/vae_tf/vae.mlir
-iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 vae.mlir -o vae.vmfb
+iree-compile --iree-input-type=auto --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 vae.mlir -o vae.vmfb
 ./build/vulkan_gui/iree-vulkan-gui --module-file=stable_diff_tf.vmfb --function_input=1x4x64x64xf32
 
 # CLIP Autoencoder
 wget https://storage.googleapis.com/shark_tank/quinn/stable_diff_tf/clip_tf/clip_autoencoder.mlir
-iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 clip_autoencoder.mlir -o clip_autoencoder.vmfb
+iree-compile --iree-input-type=auto --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvmcpu-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 clip_autoencoder.mlir -o clip_autoencoder.vmfb
 ./build/vulkan_gui/iree-vulkan-gui --module-file=stable_diff_tf.vmfb --function_input=1x77xi32 --function_input=1x77xi32
 ```

cpp/vision_inference/CMakeLists.txt

@@ -21,7 +21,7 @@ endif()
 # Compile mnist.mlir to mnist.vmfb.
 set(_COMPILE_TOOL_EXECUTABLE $<TARGET_FILE:iree-compile>)
 set(_COMPILE_ARGS)
-list(APPEND _COMPILE_ARGS "--iree-input-type=mhlo")
+list(APPEND _COMPILE_ARGS "--iree-input-type=auto")
 list(APPEND _COMPILE_ARGS "--iree-hal-target-backends=llvm-cpu")
 list(APPEND _COMPILE_ARGS "${IREE_SOURCE_DIR}/samples/models/mnist.mlir")
 list(APPEND _COMPILE_ARGS "-o")

dataset/requirements.txt

@@ -1,3 +1,3 @@
 # SHARK Annotator
-gradio==3.15.0
+gradio==3.34.0
 jsonlines

requirements.txt

@@ -19,7 +19,7 @@ transformers
 diffusers @ git+https://github.com/huggingface/diffusers@e47459c80f6f6a5a1c19d32c3fd74edf94f47aa2
 scipy
 ftfy
-gradio==3.22.0
+gradio==3.34.0
 altair
 omegaconf
 safetensors

setup_venv.sh

@@ -2,9 +2,10 @@
 # Sets up a venv suitable for running samples.
 # e.g:
 # ./setup_venv.sh  #setup a default $PYTHON3 shark.venv
-# Environment Variables by the script.
+# Environment variables used by the script.
 # PYTHON=$PYTHON3.10 ./setup_venv.sh  #pass a version of $PYTHON to use
 # VENV_DIR=myshark.venv #create a venv called myshark.venv
+# SKIP_VENV=1 #Don't create and activate a Python venv. Use the current environment. 
 # USE_IREE=1 #use stock IREE instead of Nod.ai's SHARK build
 # IMPORTER=1 #Install importer deps
 # BENCHMARK=1 #Install benchmark deps
@@ -26,15 +27,17 @@ PYTHON_VERSION_X_Y=`${PYTHON} -c 'import sys; version=sys.version_info[:2]; prin
 echo "Python: $PYTHON"
 echo "Python version: $PYTHON_VERSION_X_Y"
 
-if [[ -z "${CONDA_PREFIX}" ]]; then
-  # Not a conda env. So create a new VENV dir
-  VENV_DIR=${VENV_DIR:-shark.venv}
-  echo "Using pip venv.. Setting up venv dir: $VENV_DIR"
-  $PYTHON -m venv "$VENV_DIR" || die "Could not create venv."
-  source "$VENV_DIR/bin/activate" || die "Could not activate venv"
-  PYTHON="$(which python3)"
-else
-  echo "Found conda env $CONDA_DEFAULT_ENV. Running pip install inside the conda env"
+if [[ "$SKIP_VENV" != "1" ]]; then
+  if [[ -z "${CONDA_PREFIX}" ]]; then
+    # Not a conda env. So create a new VENV dir
+    VENV_DIR=${VENV_DIR:-shark.venv}
+    echo "Using pip venv.. Setting up venv dir: $VENV_DIR"
+    $PYTHON -m venv "$VENV_DIR" || die "Could not create venv."
+    source "$VENV_DIR/bin/activate" || die "Could not activate venv"
+    PYTHON="$(which python3)"
+  else
+    echo "Found conda env $CONDA_DEFAULT_ENV. Running pip install inside the conda env"
+  fi
 fi
 
 Red=`tput setaf 1`
@@ -147,8 +150,7 @@ if [[ ! -z "${ONNX}" ]]; then
   fi
 fi
 
-if [[ -z "${CONDA_PREFIX}" ]]; then
+if [[ -z "${CONDA_PREFIX}" && "$SKIP_VENV" != "1" ]]; then
   echo "${Green}Before running examples activate venv with:"
   echo "  ${Green}source $VENV_DIR/bin/activate"
 fi
-

shark/examples/shark_inference/minilm_jax.py

@@ -1,7 +1,7 @@
 from transformers import AutoTokenizer, FlaxAutoModel
 import torch
 import jax
-from typing import Union, Dict, List
+from typing import Union, Dict, List, Any
 import numpy as np
 from shark.shark_inference import SharkInference
 import io
@@ -36,18 +36,38 @@ def get_sample_input():
     )
 
 
-def export_to_mlir(sample_input: NumpyTree):
-    model = FlaxAutoModel.from_pretrained("microsoft/MiniLM-L12-H384-uncased")
-    model_mlir = jax.jit(model).lower(**sample_input).compiler_ir()
-    return str(model_mlir).encode()
+def get_jax_model():
+    return FlaxAutoModel.from_pretrained("microsoft/MiniLM-L12-H384-uncased")
+
+
+def export_jax_to_mlir(jax_model: Any, sample_input: NumpyTree):
+    model_mlir = jax.jit(jax_model).lower(**sample_input).compiler_ir()
+    byte_stream = io.BytesIO()
+    model_mlir.operation.write_bytecode(file=byte_stream)
+    return byte_stream.getvalue()
+
+
+def assert_array_list_allclose(x, y, *args, **kwargs):
+    assert len(x) == len(y)
+    for a, b in zip(x, y):
+        np.testing.assert_allclose(
+            np.asarray(a), np.asarray(b), *args, **kwargs
+        )
 
 
 sample_input = get_sample_input()
-mlir = export_to_mlir(sample_input)
+jax_model = get_jax_model()
+mlir = export_jax_to_mlir(jax_model, sample_input)
 
 # Compile and load module.
 shark_inference = SharkInference(mlir_module=mlir, mlir_dialect="mhlo")
 shark_inference.compile()
 
 # Run main function.
-print(shark_inference("main", jax.tree_util.tree_flatten(sample_input)[0]))
+result = shark_inference("main", jax.tree_util.tree_flatten(sample_input)[0])
+
+# Run JAX model.
+reference_result = jax.tree_util.tree_flatten(jax_model(**sample_input))[0]
+
+# Verify result.
+assert_array_list_allclose(result, reference_result, atol=1e-5)

shark/examples/shark_inference/minilm_jax_requirements.txt

@@ -1,5 +1,6 @@
 flax
 jax[cpu]
 nodai-SHARK
+orbax
 transformers
 torch

shark/examples/shark_inference/resnet50_script.py

@@ -70,11 +70,11 @@ mlir_model, func_name, inputs, golden_out = download_model(
     "resnet50", frontend="torch"
 )
 
-shark_module = SharkInference(mlir_model, func_name, mlir_dialect="linalg")
+shark_module = SharkInference(mlir_model, mlir_dialect="linalg")
 shark_module.compile()
 path = shark_module.save_module()
 shark_module.load_module(path)
-result = shark_module.forward((img.detach().numpy(),))
+result = shark_module("forward", (img.detach().numpy(),))
 
 print("The top 3 results obtained via shark_runner is:")
 print(top3_possibilities(torch.from_numpy(result)))

shark/iree_utils/_common.py

@@ -45,10 +45,15 @@ def run_cmd(cmd, debug=False):
 
 def iree_device_map(device):
     uri_parts = device.split("://", 2)
+    iree_driver = (
+        _IREE_DEVICE_MAP[uri_parts[0]]
+        if uri_parts[0] in _IREE_DEVICE_MAP
+        else uri_parts[0]
+    )
     if len(uri_parts) == 1:
-        return _IREE_DEVICE_MAP[uri_parts[0]]
+        return iree_driver
     else:
-        return f"{_IREE_DEVICE_MAP[uri_parts[0]]}://{uri_parts[1]}"
+        return f"{iree_driver}://{uri_parts[1]}"
 
 
 def get_supported_device_list():
@@ -57,6 +62,8 @@ def get_supported_device_list():
 
 _IREE_DEVICE_MAP = {
     "cpu": "local-task",
+    "cpu-task": "local-task",
+    "cpu-sync": "local-sync",
     "cuda": "cuda",
     "vulkan": "vulkan",
     "metal": "vulkan",
@@ -68,11 +75,13 @@ _IREE_DEVICE_MAP = {
 def iree_target_map(device):
     if "://" in device:
         device = device.split("://")[0]
-    return _IREE_TARGET_MAP[device]
+    return _IREE_TARGET_MAP[device] if device in _IREE_TARGET_MAP else device
 
 
 _IREE_TARGET_MAP = {
     "cpu": "llvm-cpu",
+    "cpu-task": "llvm-cpu",
+    "cpu-sync": "llvm-cpu",
     "cuda": "cuda",
     "vulkan": "vulkan",
     "metal": "vulkan",
@@ -110,10 +119,8 @@ def check_device_drivers(device):
             subprocess.check_output("rocminfo")
         except Exception:
             return True
-    # Unknown device.
-    else:
-        return True
 
+    # Unknown device. We assume drivers are installed.
     return False
 
 

shark/iree_utils/compile_utils.py

@@ -60,10 +60,9 @@ def get_iree_device_args(device, extra_args=[]):
 def get_iree_frontend_args(frontend):
     if frontend in ["torch", "pytorch", "linalg", "tm_tensor"]:
         return ["--iree-llvmcpu-target-cpu-features=host"]
-    elif frontend in ["tensorflow", "tf", "mhlo"]:
+    elif frontend in ["tensorflow", "tf", "mhlo", "stablehlo"]:
         return [
             "--iree-llvmcpu-target-cpu-features=host",
-            "--iree-mhlo-demote-i64-to-i32=false",
             "--iree-flow-demote-i64-to-i32",
         ]
     else:
@@ -265,8 +264,8 @@ def compile_module_to_flatbuffer(
     args += extra_args
 
     if frontend in ["tensorflow", "tf"]:
-        input_type = "mhlo"
-    elif frontend in ["mhlo", "tosa"]:
+        input_type = "auto"
+    elif frontend in ["stablehlo", "tosa"]:
         input_type = frontend
     elif frontend in ["tflite", "tflite-tosa"]:
         input_type = "tosa"
@@ -367,7 +366,7 @@ def export_iree_module_to_vmfb(
 def export_module_to_mlir_file(module, frontend, directory: str):
     # TODO: write proper documentation.
     mlir_str = module
-    if frontend in ["tensorflow", "tf", "mhlo", "tflite"]:
+    if frontend in ["tensorflow", "tf", "mhlo", "stablehlo", "tflite"]:
         mlir_str = module.decode("utf-8")
     elif frontend in ["pytorch", "torch"]:
         mlir_str = module.operation.get_asm()

shark/iree_utils/vulkan_target_env_utils.py

@@ -117,7 +117,8 @@ def get_extensions(triple):
 
     if get_vendor(triple) == "NVIDIA" or arch == "rdna3":
         ext.append("VK_NV_cooperative_matrix")
-
+    if get_vendor(triple) == ["NVIDIA", "AMD", "Intel"]:
+        ext.append("VK_KHR_shader_integer_dot_product")
     return make_ext_list(ext_list=ext)
 
 
@@ -228,6 +229,7 @@ def get_vulkan_target_capabilities(triple):
         cap["shaderInt8"] = True
         cap["shaderInt16"] = True
         cap["shaderInt64"] = True
+        cap["shaderIntegerDotProduct"] = True
         cap["storageBuffer16BitAccess"] = True
         cap["storagePushConstant16"] = True
         cap["uniformAndStorageBuffer16BitAccess"] = True
@@ -236,12 +238,12 @@ def get_vulkan_target_capabilities(triple):
         cap["uniformAndStorageBuffer8BitAccess"] = True
         cap["variablePointers"] = True
         cap["variablePointersStorageBuffer"] = True
-
         if arch == "rdna3":
             # TODO: Get scope value
             cap["coopmatCases"] = [
                 "mSize = 16, nSize = 16, kSize = 16, aType = f16, bType = f16, cType = f16, resultType = f16, scope = #vk.scope<Subgroup>"
             ]
+
         if product == "rx5700xt":
             cap["storagePushConstant16"] = False
             cap["storagePushConstant8"] = False
@@ -274,7 +276,7 @@ def get_vulkan_target_capabilities(triple):
         cap["shaderInt8"] = True
         cap["shaderInt16"] = True
         cap["shaderInt64"] = True
-
+        cap["shaderIntegerDotProduct"] = True
         cap["storagePushConstant16"] = False
         cap["uniformAndStorageBuffer16BitAccess"] = True
         cap["storageBuffer8BitAccess"] = True
@@ -305,6 +307,7 @@ def get_vulkan_target_capabilities(triple):
         cap["shaderInt8"] = True
         cap["shaderInt16"] = True
         cap["shaderInt64"] = True
+        cap["shaderIntegerDotProduct"] = False
         cap["storageBuffer16BitAccess"] = True
         cap["storagePushConstant16"] = True
         cap["uniformAndStorageBuffer16BitAccess"] = True
@@ -367,6 +370,7 @@ def get_vulkan_target_capabilities(triple):
         cap["shaderInt8"] = True
         cap["shaderInt16"] = True
         cap["shaderInt64"] = False
+        cap["shaderIntegerDotProduct"] = True
         cap["storageBuffer16BitAccess"] = True
         cap["storagePushConstant16"] = True
         cap["uniformAndStorageBuffer16BitAccess"] = True
@@ -408,11 +412,12 @@ def get_vulkan_target_capabilities(triple):
             "Quad",
         ]
 
-        cap["shaderFloat16"] = True
+        cap["shaderFloat16"] = False
         cap["shaderFloat64"] = True
         cap["shaderInt8"] = True
         cap["shaderInt16"] = True
         cap["shaderInt64"] = True
+        cap["shaderIntegerDotProduct"] = True
         cap["storageBuffer16BitAccess"] = True
         cap["storagePushConstant16"] = True
         cap["uniformAndStorageBuffer16BitAccess"] = True
@@ -446,6 +451,7 @@ def get_vulkan_target_capabilities(triple):
         cap["shaderInt8"] = True
         cap["shaderInt16"] = True
         cap["shaderInt64"] = True
+        cap["shaderIntegerDotProduct"] = True
         cap["storageBuffer16BitAccess"] = True
         cap["storagePushConstant16"] = True
         cap["uniformAndStorageBuffer16BitAccess"] = True

shark/model_annotation.py

@@ -311,11 +311,18 @@ def add_attributes(op: ir.Operation, config: List[Dict]):
             split_k = config["split_k"]
     elif "SPIRV" in config["pipeline"]:
         pipeline = config["pipeline"]
-        tile_sizes = [
-            config["work_group_tile_sizes"],
-            config["parallel_tile_sizes"],
-            config["reduction_tile_sizes"],
-        ]
+        if pipeline == "SPIRVMatmulPromoteVectorize":
+            tile_sizes = [
+                config["work_group_tile_sizes"]
+                + [config["reduction_tile_sizes"][-1]],
+            ]
+        else:
+            tile_sizes = [
+                config["work_group_tile_sizes"],
+                config["parallel_tile_sizes"],
+                config["reduction_tile_sizes"],
+            ]
+
         workgroup_size = config["work_group_sizes"]
         if "vector_tile_sizes" in config.keys():
             tile_sizes += [config["vector_tile_sizes"]]

shark/shark_downloader.py

@@ -61,6 +61,8 @@ def download_public_file(
                 continue
 
         destination_filename = os.path.join(destination_folder_name, blob_name)
+        if os.path.isdir(destination_filename):
+            continue
         with open(destination_filename, "wb") as f:
             with tqdm.wrapattr(f, "write", total=blob.size) as file_obj:
                 storage_client.download_blob_to_file(blob, file_obj)
@@ -210,6 +212,9 @@ def download_model(
             + "_BS"
             + str(import_args["batch_size"])
         )
+    elif any(model in model_name for model in ["clip", "unet", "vae"]):
+        # TODO(Ean Garvey): rework extended naming such that device is only included in model_name after .vmfb compilation.
+        model_dir_name = model_name
     else:
         model_dir_name = model_name + "_" + frontend
     model_dir = os.path.join(WORKDIR, model_dir_name)
@@ -270,6 +275,9 @@ def download_model(
     tuned_str = "" if tuned is None else "_" + tuned
     suffix = f"{dyn_str}_{frontend}{tuned_str}.mlir"
     filename = os.path.join(model_dir, model_name + suffix)
+    print(
+        f"Verifying that model artifacts were downloaded successfully to {filename}..."
+    )
     if not os.path.exists(filename):
         from tank.generate_sharktank import gen_shark_files
 

shark/shark_generate_model_config.py

@@ -0,0 +1,38 @@
+import json
+from collections import OrderedDict
+
+
+class GenerateConfigFile:
+    def __init__(
+        self,
+        model,
+        num_sharding_stages: int,
+        sharding_stages_id: list[str] = None,
+    ):
+        self.model = model
+        self.num_sharding_stages = num_sharding_stages
+        self.sharding_stages_id = sharding_stages_id
+        assert self.num_sharding_stages == len(
+            self.sharding_stages_id
+        ), "Number of sharding stages should be equal to the list of their ID"
+
+    def generate_json(self):
+        model_dictionary = dict()
+
+        for name, m in self.model.named_modules():
+            if name == "":
+                continue
+
+            # Remove non-leaf nodes from the config as they aren't an operation
+            substring_before_final_period = name.split(".")[:-1]
+            substring_before_final_period = ".".join(
+                substring_before_final_period
+            )
+            if substring_before_final_period in model_dictionary:
+                del model_dictionary[substring_before_final_period]
+
+            layer_dict = {n: "None" for n in self.sharding_stages_id}
+            model_dictionary[name] = layer_dict
+
+        with open("model_config.json", "w") as outfile:
+            json.dump(model_dictionary, outfile)

shark/shark_importer.py

@@ -148,14 +148,23 @@ class SharkImporter:
 
     # Saves `function_name.npy`, `inputs.npz`, `golden_out.npz` and `model_name.mlir` in the directory `dir`.
     def save_data(
-        self, dir, model_name, mlir_data, func_name, inputs, outputs
+        self,
+        dir,
+        model_name,
+        mlir_data,
+        func_name,
+        inputs,
+        outputs,
+        mlir_type="linalg",
     ):
         import numpy as np
 
         inputs_name = "inputs.npz"
         outputs_name = "golden_out.npz"
         func_file_name = "function_name"
-        model_name_mlir = model_name + "_" + self.frontend + ".mlir"
+        model_name_mlir = (
+            model_name + "_" + self.frontend + "_" + mlir_type + ".mlir"
+        )
         print(f"saving {model_name_mlir} to {dir}")
         try:
             inputs = [x.cpu().detach() for x in inputs]
@@ -191,19 +200,23 @@ class SharkImporter:
         dir=tempfile.gettempdir(),
         model_name="model",
         golden_values=None,
+        mlir_type="linalg",
     ):
         if self.inputs == None:
             print(
                 f"There is no input provided: {self.inputs}, please provide inputs or simply run import_mlir."
             )
             sys.exit(1)
-        model_name_mlir = model_name + "_" + self.frontend + ".mlir"
+        model_name_mlir = (
+            model_name + "_" + self.frontend + "_" + mlir_type + ".mlir"
+        )
         artifact_path = os.path.join(dir, model_name_mlir)
         imported_mlir = self.import_mlir(
             is_dynamic,
             tracing_required,
             func_name,
             save_dir=artifact_path,
+            mlir_type=mlir_type,
         )
         # TODO: Make sure that any generic function name is accepted. Currently takes in the default function names.
         # TODO: Check for multiple outputs.
@@ -229,6 +242,7 @@ class SharkImporter:
                 imported_mlir[1],
                 self.inputs,
                 golden_out,
+                mlir_type,
             )
             return (
                 imported_mlir,
@@ -298,6 +312,47 @@ def get_f16_inputs(inputs, is_f16, f16_input_mask):
     return tuple(f16_masked_inputs)
 
 
+# Upcasts the block/list of ops.
+def add_upcast(fx_g):
+    import torch
+
+    for node in fx_g.graph.nodes:
+        if node.target in [torch.ops.aten.mul]:
+            # This is a very strict check.
+            if hasattr(node.args[1], "target"):
+                if (
+                    node.args[1].target in [torch.ops.aten.rsqrt]
+                    and node.args[1].args[0].target in [torch.ops.aten.add]
+                    and node.args[1].args[0].args[0].target
+                    in [torch.ops.aten.mean]
+                    and node.args[1].args[0].args[0].args[0].target
+                    in [torch.ops.aten.pow]
+                ):
+                    print("found an upcasting block let's upcast it.")
+                    pow_node = node.args[1].args[0].args[0].args[0]
+                    mul_node = node
+                    with fx_g.graph.inserting_before(pow_node):
+                        lhs = pow_node.args[0]
+                        upcast_lhs = fx_g.graph.call_function(
+                            torch.ops.aten._to_copy,
+                            args=(lhs,),
+                            kwargs={"dtype": torch.float32},
+                        )
+                        pow_node.args = (upcast_lhs, pow_node.args[1])
+                    with fx_g.graph.inserting_before(mul_node):
+                        new_node = fx_g.graph.call_function(
+                            torch.ops.aten._to_copy,
+                            args=(mul_node,),
+                            kwargs={"dtype": torch.float16},
+                        )
+                        mul_node.append(new_node)
+                        mul_node.replace_all_uses_with(new_node)
+                        new_node.args = (mul_node,)
+                        new_node.kwargs = {"dtype": torch.float16}
+
+    fx_g.graph.lint()
+
+
 def transform_fx(fx_g):
     import torch
 
@@ -306,6 +361,9 @@ def transform_fx(fx_g):
         "device": torch.device(type="cpu"),
         "pin_memory": False,
     }
+    kwargs_dict1 = {
+        "dtype": torch.float16,
+    }
     for node in fx_g.graph.nodes:
         if node.op == "call_function":
             if node.target in [
@@ -313,7 +371,38 @@ def transform_fx(fx_g):
                 torch.ops.aten.empty,
                 torch.ops.aten.zeros,
             ]:
-                node.kwargs = kwargs_dict
+                if node.kwargs.get("dtype") == torch.float32:
+                    node.kwargs = kwargs_dict
+
+            # Vicuna
+            if node.target in [
+                torch.ops.aten._to_copy,
+            ]:
+                if node.kwargs.get("dtype") == torch.float32:
+                    node.kwargs = kwargs_dict1
+
+            if node.target in [
+                torch.ops.aten.masked_fill,
+            ]:
+                if node.args[2] > torch.finfo(torch.half).max:
+                    max_val = torch.finfo(torch.half).max
+                    node.args = (node.args[0], node.args[1], max_val)
+                elif node.args[2] < torch.finfo(torch.half).min:
+                    min_val = torch.finfo(torch.half).min
+                    node.args = (node.args[0], node.args[1], min_val)
+
+            if node.target in [
+                torch.ops.aten.full,
+            ]:
+                if node.args[1] > torch.finfo(torch.half).max:
+                    max_val = torch.finfo(torch.half).max
+                    node.args = (node.args[0], max_val)
+                    node.kwargs = kwargs_dict
+                elif node.args[1] < torch.finfo(torch.half).min:
+                    min_val = torch.finfo(torch.half).min
+                    node.args = (node.args[0], min_val)
+                    node.kwargs = kwargs_dict
+
             # Inputs and outputs of aten.var.mean should be upcasted to fp32.
             if node.target in [torch.ops.aten.var_mean]:
                 with fx_g.graph.inserting_before(node):
@@ -323,6 +412,7 @@ def transform_fx(fx_g):
                         kwargs={},
                     )
                     node.args = (new_node, node.args[1])
+
             if node.name.startswith("getitem"):
                 with fx_g.graph.inserting_before(node):
                     if node.args[0].target in [torch.ops.aten.var_mean]:
@@ -335,6 +425,7 @@ def transform_fx(fx_g):
                         node.replace_all_uses_with(new_node)
                         new_node.args = (node,)
                         new_node.kwargs = {"dtype": torch.float16}
+
             # aten.empty should be filled with zeros.
             if node.target in [torch.ops.aten.empty]:
                 with fx_g.graph.inserting_after(node):
@@ -346,6 +437,14 @@ def transform_fx(fx_g):
                     node.replace_all_uses_with(new_node)
                     new_node.args = (node,)
 
+    # Required for cuda debugging.
+    # for node in fx_g.graph.nodes:
+    # if node.op == "call_function":
+    # if node.kwargs.get("device") == torch.device(type="cpu"):
+    # new_kwargs = node.kwargs.copy()
+    # new_kwargs["device"] = torch.device(type="cuda")
+    # node.kwargs = new_kwargs
+
     fx_g.graph.lint()
 
 
@@ -397,6 +496,9 @@ def import_with_fx(
     return_str=False,
     save_dir=tempfile.gettempdir(),
     model_name="model",
+    mlir_type="linalg",
+    is_dynamic=False,
+    tracing_required=False,
 ):
     import torch
     from torch.fx.experimental.proxy_tensor import make_fx
@@ -446,6 +548,8 @@ def import_with_fx(
     if is_f16:
         fx_g = fx_g.half()
         transform_fx(fx_g)
+        # TODO: Have to make it more generic.
+        add_upcast(fx_g)
         fx_g.recompile()
 
     if training:
@@ -453,6 +557,9 @@ def import_with_fx(
         inputs = flatten_training_input(inputs)
 
     ts_graph = torch.jit.script(fx_g)
+    if mlir_type == "torchscript":
+        return ts_graph
+
     inputs = get_f16_inputs(inputs, is_f16, f16_input_mask)
     mlir_importer = SharkImporter(
         ts_graph,
@@ -463,7 +570,12 @@ def import_with_fx(
 
     if debug:  # and not is_f16:
         (mlir_module, func_name), _, _ = mlir_importer.import_debug(
-            dir=save_dir, model_name=model_name, golden_values=golden_values
+            dir=save_dir,
+            model_name=model_name,
+            golden_values=golden_values,
+            mlir_type=mlir_type,
+            is_dynamic=is_dynamic,
+            tracing_required=tracing_required,
         )
         return mlir_module, func_name
 

shark/shark_runner.py

@@ -25,7 +25,14 @@ import sys
 
 
 # supported dialects by the shark-runtime.
-supported_dialects = {"linalg", "mhlo", "tosa", "tf-lite", "tm_tensor"}
+supported_dialects = {
+    "linalg",
+    "auto",
+    "stablehlo",
+    "tosa",
+    "tf-lite",
+    "tm_tensor",
+}
 
 
 class SharkRunner:

shark/shark_trainer.py

@@ -59,6 +59,7 @@ class SharkTrainer:
             "torch",
             "tensorflow",
             "tf",
+            "stablehlo",
             "mhlo",
             "linalg",
             "tosa",
@@ -84,7 +85,7 @@ class SharkTrainer:
                 "tm_tensor",
                 extra_args=extra_args,
             )
-        elif self.frontend in ["tensorflow", "tf", "mhlo"]:
+        elif self.frontend in ["tensorflow", "tf", "mhlo", "stablehlo"]:
             self.shark_runner = SharkRunner(
                 self.model,
                 self.input,

shark/torch_mlir_utils.py

@@ -21,7 +21,7 @@ import io
 
 mlir_type_mapping_dict = {
     "linalg": torch_mlir.OutputType.LINALG_ON_TENSORS,
-    "mhlo": torch_mlir.OutputType.STABLEHLO,
+    "stablehlo": torch_mlir.OutputType.STABLEHLO,
     "tosa": torch_mlir.OutputType.TOSA,
 }
 

tank/all_models.csv

@@ -1,47 +1,47 @@
-resnet50,mhlo,tf,1e-2,1e-3,default,nhcw-nhwc,False,False,False,"","macos"
-albert-base-v2,mhlo,tf,1e-2,1e-2,default,None,False,False,False,"",""
-roberta-base,mhlo,tf,1e-02,1e-3,default,nhcw-nhwc,True,True,True,"","macos"
-bert-base-uncased,mhlo,tf,1e-2,1e-3,default,None,False,False,False,"","enabled_windows"
-camembert-base,mhlo,tf,1e-2,1e-3,default,None,True,True,True,"",""
-dbmdz/convbert-base-turkish-cased,mhlo,tf,1e-2,1e-3,default,nhcw-nhwc,True,True,False,"https://github.com/iree-org/iree/issues/9971",""
-distilbert-base-uncased,mhlo,tf,1e-2,1e-3,default,None,False,False,False,"",""
-facebook/convnext-tiny-224,mhlo,tf,1e-2,1e-3,tf_vit,nhcw-nhwc,True,True,False,"https://github.com/nod-ai/SHARK/issues/311 & https://github.com/nod-ai/SHARK/issues/342","macos"
-funnel-transformer/small,mhlo,tf,1e-2,1e-3,default,None,True,True,False,"https://github.com/nod-ai/SHARK/issues/201",""
-google/electra-small-discriminator,mhlo,tf,1e-2,1e-3,default,None,False,False,False,"",""
-google/mobilebert-uncased,mhlo,tf,1e-2,1e-3,default,None,True,False,False,"Fails during iree-compile",""
-google/vit-base-patch16-224,mhlo,tf,1e-2,1e-3,tf_vit,nhcw-nhwc,False,False,False,"",""
-microsoft/MiniLM-L12-H384-uncased,mhlo,tf,1e-2,1e-3,tf_hf,None,True,False,False,"Fails during iree-compile.",""
-microsoft/layoutlm-base-uncased,mhlo,tf,1e-2,1e-3,default,None,False,False,False,"",""
-microsoft/mpnet-base,mhlo,tf,1e-2,1e-2,default,None,True,True,True,"",""
+resnet50,stablehlo,tf,1e-2,1e-3,default,nhcw-nhwc,False,False,False,"","macos"
+albert-base-v2,stablehlo,tf,1e-2,1e-2,default,None,False,False,False,"",""
+roberta-base,stablehlo,tf,1e-02,1e-3,default,nhcw-nhwc,True,True,True,"","macos"
+bert-base-uncased,stablehlo,tf,1e-2,1e-3,default,None,False,False,False,"","enabled_windows"
+camembert-base,stablehlo,tf,1e-2,1e-3,default,None,True,True,True,"",""
+dbmdz/convbert-base-turkish-cased,stablehlo,tf,1e-2,1e-3,default,nhcw-nhwc,True,True,False,"https://github.com/iree-org/iree/issues/9971",""
+distilbert-base-uncased,stablehlo,tf,1e-2,1e-3,default,None,False,False,False,"",""
+facebook/convnext-tiny-224,stablehlo,tf,1e-2,1e-3,tf_vit,nhcw-nhwc,True,True,False,"https://github.com/nod-ai/SHARK/issues/311 & https://github.com/nod-ai/SHARK/issues/342","macos"
+funnel-transformer/small,stablehlo,tf,1e-2,1e-3,default,None,True,True,False,"https://github.com/nod-ai/SHARK/issues/201",""
+google/electra-small-discriminator,stablehlo,tf,1e-2,1e-3,default,None,False,False,False,"",""
+google/mobilebert-uncased,stablehlo,tf,1e-2,1e-3,default,None,True,False,False,"Fails during iree-compile","macos"
+google/vit-base-patch16-224,stablehlo,tf,1e-2,1e-3,tf_vit,nhcw-nhwc,False,False,False,"",""
+microsoft/MiniLM-L12-H384-uncased,stablehlo,tf,1e-2,1e-3,tf_hf,None,True,False,False,"Fails during iree-compile.",""
+microsoft/layoutlm-base-uncased,stablehlo,tf,1e-2,1e-3,default,None,False,False,False,"",""
+microsoft/mpnet-base,stablehlo,tf,1e-2,1e-2,default,None,True,True,True,"",""
 albert-base-v2,linalg,torch,1e-2,1e-3,default,None,True,True,True,"issue with aten.tanh in torch-mlir",""
 alexnet,linalg,torch,1e-2,1e-3,default,None,True,True,False,"https://github.com/nod-ai/SHARK/issues/879",""
 bert-base-cased,linalg,torch,1e-2,1e-3,default,None,False,True,False,"",""
 bert-base-uncased,linalg,torch,1e-2,1e-3,default,None,False,True,False,"",""
 bert-base-uncased_fp16,linalg,torch,1e-1,1e-1,default,None,True,True,True,"",""
 bert-large-uncased,linalg,torch,1e-2,1e-3,default,None,False,True,False,"",""
-bert-large-uncased,mhlo,tf,1e-2,1e-3,default,None,False,False,False,"",""
+bert-large-uncased,stablehlo,tf,1e-2,1e-3,default,None,False,False,False,"",""
 facebook/deit-small-distilled-patch16-224,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,True,False,"Fails during iree-compile.",""
 google/vit-base-patch16-224,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,True,False,"https://github.com/nod-ai/SHARK/issues/311",""
 microsoft/beit-base-patch16-224-pt22k-ft22k,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,True,False,"https://github.com/nod-ai/SHARK/issues/390","macos"
 microsoft/MiniLM-L12-H384-uncased,linalg,torch,1e-2,1e-3,default,None,False,True,False,"",""
-google/mobilebert-uncased,linalg,torch,1e-2,1e-3,default,None,False,True,False,"https://github.com/nod-ai/SHARK/issues/344",""
-mobilenet_v3_small,linalg,torch,1e-1,1e-2,default,nhcw-nhwc,False,True,False,"https://github.com/nod-ai/SHARK/issues/388","macos"
-nvidia/mit-b0,linalg,torch,1e-2,1e-3,default,None,True,True,False,"https://github.com/nod-ai/SHARK/issues/343","macos"
+google/mobilebert-uncased,linalg,torch,1e-2,1e-3,default,None,False,True,False,"https://github.com/nod-ai/SHARK/issues/344","macos"
+mobilenet_v3_small,linalg,torch,1e-1,1e-2,default,nhcw-nhwc,False,True,True,"https://github.com/nod-ai/SHARK/issues/388, https://github.com/nod-ai/SHARK/issues/1487","macos"
+nvidia/mit-b0,linalg,torch,1e-2,1e-3,default,None,True,True,True,"https://github.com/nod-ai/SHARK/issues/343,https://github.com/nod-ai/SHARK/issues/1487","macos"
 resnet101,linalg,torch,1e-2,1e-3,default,nhcw-nhwc/img2col,False,False,False,"","macos"
 resnet18,linalg,torch,1e-2,1e-3,default,None,True,True,False,"","macos"
 resnet50,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,False,"","macos"
 resnet50_fp16,linalg,torch,1e-2,1e-2,default,nhcw-nhwc/img2col,True,False,True,"",""
 squeezenet1_0,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,False,"","macos"
 wide_resnet50_2,linalg,torch,1e-2,1e-3,default,nhcw-nhwc/img2col,False,False,False,"","macos"
-efficientnet-v2-s,mhlo,tf,1e-02,1e-3,default,nhcw-nhwc,False,False,False,"","macos"
+efficientnet-v2-s,stablehlo,tf,1e-02,1e-3,default,nhcw-nhwc,False,False,False,"","macos"
 mnasnet1_0,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,True,True,True,"","macos"
-efficientnet_b0,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,True,True,False,"https://github.com/nod-ai/SHARK/issues/1243",""
-efficientnet_b7,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,True,False,"Fails on MacOS builder, VK device lost","macos"
-efficientnet_b0,mhlo,tf,1e-2,1e-3,default,nhcw-nhwc,False,False,False,"",""
-efficientnet_b7,mhlo,tf,1e-2,1e-3,default,nhcw-nhwc,False,False,False,"Fails on MacOS builder, VK device lost","macos"
-gpt2,mhlo,tf,1e-2,1e-3,default,None,True,False,False,"","macos"
-t5-base,linalg,torch,1e-2,1e-3,default,None,True,True,True,"Inputs for seq2seq models in torch currently unsupported.",""
-t5-base,mhlo,tf,1e-2,1e-3,default,None,False,False,False,"",""
+efficientnet_b0,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,True,True,True,"https://github.com/nod-ai/SHARK/issues/1487","macos"
+efficientnet_b7,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,True,True,"https://github.com/nod-ai/SHARK/issues/1487","macos"
+efficientnet_b0,stablehlo,tf,1e-2,1e-3,default,nhcw-nhwc,False,False,False,"",""
+efficientnet_b7,stablehlo,tf,1e-2,1e-3,default,nhcw-nhwc,False,False,False,"Fails on MacOS builder, VK device lost","macos"
+gpt2,stablehlo,tf,1e-2,1e-3,default,None,True,False,False,"","macos"
+t5-base,linalg,torch,1e-2,1e-3,default,None,True,True,True,"Inputs for seq2seq models in torch currently unsupported.","macos"
+t5-base,stablehlo,tf,1e-2,1e-3,default,None,False,False,False,"","macos"
 t5-large,linalg,torch,1e-2,1e-3,default,None,True,True,True,"Inputs for seq2seq models in torch currently unsupported","macos"
-t5-large,mhlo,tf,1e-2,1e-3,default,None,False,False,False,"","macos"
-stabilityai/stable-diffusion-2-1-base,linalg,torch,1e-3,1e-3,default,None,True,False,False,"",""
+t5-large,stablehlo,tf,1e-2,1e-3,default,None,False,False,False,"","macos"
+stabilityai/stable-diffusion-2-1-base,linalg,torch,1e-3,1e-3,default,None,True,False,False,"","macos"

tank/examples/MiniLM_tf/huggingface_MiniLM_run.py

@@ -75,7 +75,7 @@ if __name__ == "__main__":
         compiler_module,
         target_backends=[backend],
         extra_args=args,
-        input_type="mhlo",
+        input_type="auto",
     )
     # flatbuffer_blob = compile_str(compiler_module, target_backends=["dylib-llvm-aot"])
 

tank/examples/bert_fine_tuning/bert_fine_tune_tf.py

@@ -153,7 +153,7 @@ if __name__ == "__main__":
         compiler_module,
         target_backends=[backend],
         extra_args=args,
-        input_type="mhlo",
+        input_type="auto",
     )
 
     # Save module as MLIR file in a directory

tank/examples/bert_tf/bert_large_run.py

@@ -96,7 +96,7 @@ if __name__ == "__main__":
         compiler_module,
         target_backends=[backend],
         extra_args=args,
-        input_type="mhlo",
+        input_type="auto",
     )
     # flatbuffer_blob = compile_str(compiler_module, target_backends=["dylib-llvm-aot"])
 

tank/examples/bert_tf/bert_small_run.py

@@ -91,7 +91,7 @@ if __name__ == "__main__":
         compiler_module,
         target_backends=[backend],
         extra_args=args,
-        input_type="mhlo",
+        input_type="auto",
     )
     # flatbuffer_blob = compile_str(compiler_module, target_backends=["dylib-llvm-aot"])
 

tank/examples/opt/opt_causallm.py

@@ -0,0 +1,143 @@
+import os
+import torch
+import numpy as np
+from shark_opt_wrapper import OPTForCausalLMModel
+from shark.iree_utils._common import (
+    check_device_drivers,
+    device_driver_info,
+)
+from shark.shark_inference import SharkInference
+from shark.shark_importer import import_with_fx
+from transformers import AutoTokenizer, OPTForCausalLM
+
+OPT_MODEL = "opt-1.3b"
+OPT_FS_NAME = "opt-1_3b"
+MAX_SEQUENCE_LENGTH = 30
+MAX_NEW_TOKENS = 20
+
+
+def create_module(model_name, tokenizer, device):
+    opt_base_model = OPTForCausalLM.from_pretrained("facebook/" + model_name)
+    opt_base_model.eval()
+    opt_model = OPTForCausalLMModel(opt_base_model)
+    encoded_inputs = tokenizer(
+        "What is the meaning of life?",
+        padding="max_length",
+        truncation=True,
+        max_length=MAX_SEQUENCE_LENGTH,
+        return_tensors="pt",
+    )
+    inputs = (
+        encoded_inputs["input_ids"],
+        encoded_inputs["attention_mask"],
+    )
+    # np.save("model_inputs_0.npy", inputs[0])
+    # np.save("model_inputs_1.npy", inputs[1])
+
+    mlir_path = f"./{OPT_FS_NAME}_causallm_{MAX_SEQUENCE_LENGTH}_torch.mlir"
+    if os.path.isfile(mlir_path):
+        with open(mlir_path, "r") as f:
+            model_mlir = f.read()
+        print(f"Loaded .mlir from {mlir_path}")
+    else:
+        (model_mlir, func_name) = import_with_fx(
+            model=opt_model,
+            inputs=inputs,
+            is_f16=False,
+            model_name=OPT_FS_NAME,
+            return_str=True,
+        )
+        with open(mlir_path, "w") as f:
+            f.write(model_mlir)
+        print(f"Saved mlir at {mlir_path}")
+
+    shark_module = SharkInference(
+        model_mlir,
+        device=device,
+        mlir_dialect="tm_tensor",
+        is_benchmark=False,
+    )
+
+    vmfb_name = f"{OPT_FS_NAME}_causallm_{MAX_SEQUENCE_LENGTH}_torch_{device}"
+    shark_module.save_module(module_name=vmfb_name)
+    vmfb_path = vmfb_name + ".vmfb"
+    return vmfb_path
+
+
+def shouldStop(tokens):
+    stop_ids = [50278, 50279, 50277, 0]
+    for stop_id in stop_ids:
+        if tokens[0][-1] == stop_id:
+            return True
+    return False
+
+
+def generate_new_token(shark_model, tokenizer, new_text):
+    model_inputs = tokenizer(
+        new_text,
+        padding="max_length",
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+        return_tensors="pt",
+    )
+    inputs = (
+        model_inputs["input_ids"],
+        model_inputs["attention_mask"],
+    )
+    sum_attentionmask = torch.sum(model_inputs.attention_mask)
+    output = shark_model("forward", inputs)
+    output = torch.FloatTensor(output[0])
+    next_toks = torch.topk(output, 1)
+    stop_generation = False
+    if shouldStop(next_toks.indices):
+        stop_generation = True
+    new_token = next_toks.indices[int(sum_attentionmask) - 1]
+    detok = tokenizer.decode(
+        new_token,
+        skip_special_tokens=False,
+        clean_up_tokenization_spaces=False,
+    )
+    ret_dict = {
+        "new_token": new_token,
+        "detok": detok,
+        "stop_generation": stop_generation,
+    }
+    return ret_dict
+
+
+if __name__ == "__main__":
+    tokenizer = AutoTokenizer.from_pretrained(
+        "facebook/" + OPT_MODEL, use_fast=False
+    )
+    vmfb_path = (
+        f"./{OPT_FS_NAME}_causallm_{MAX_SEQUENCE_LENGTH}_torch_cpu-sync.vmfb"
+    )
+    opt_shark_module = SharkInference(mlir_module=None, device="cpu-sync")
+    if os.path.isfile(vmfb_path):
+        opt_shark_module.load_module(vmfb_path)
+    else:
+        vmfb_path = create_module(OPT_MODEL, tokenizer, "cpu-sync")
+        opt_shark_module.load_module(vmfb_path)
+    while True:
+        try:
+            new_text = input("Give me a sentence to complete:")
+            new_text_init = new_text
+            words_list = []
+
+            for i in range(MAX_NEW_TOKENS):
+                generated_token_op = generate_new_token(
+                    opt_shark_module, tokenizer, new_text
+                )
+                detok = generated_token_op["detok"]
+                stop_generation = generated_token_op["stop_generation"]
+                if stop_generation:
+                    break
+                print(detok, end="", flush=True)
+                words_list.append(detok)
+                if detok == "":
+                    break
+                new_text = new_text + detok
+
+        except KeyboardInterrupt:
+            print("Exiting program.")
+            break

tank/examples/opt/opt_causallm_torch_test.py

@@ -0,0 +1,200 @@
+import unittest
+import os
+import pytest
+import torch
+import numpy as np
+from shark_opt_wrapper import OPTForCausalLMModel
+from shark.iree_utils._common import check_device_drivers, device_driver_info
+from shark.shark_inference import SharkInference
+from shark.shark_importer import import_with_fx
+from transformers import AutoTokenizer, OPTForCausalLM
+
+OPT_MODEL = "facebook/opt-1.3b"
+OPT_FS_NAME = "opt-1_3b"
+OPT_MODEL_66B = "facebook/opt-66b"
+
+
+class OPTModuleTester:
+    def __init__(
+        self,
+        benchmark=False,
+    ):
+        self.benchmark = benchmark
+
+    def create_and_check_module(self, dynamic, device, model_name):
+        tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
+        opt_model = OPTForCausalLM.from_pretrained(
+            model_name, return_dict=False
+        )
+        opt_model.eval()
+
+        model_inputs = tokenizer(
+            "The meaning of life is",
+            padding="max_length",
+            max_length=30,
+            truncation=True,
+            return_tensors="pt",
+        )
+        inputs = (
+            model_inputs.data["input_ids"],
+            model_inputs.data["attention_mask"],
+        )
+        act_out = opt_model(
+            inputs[0], attention_mask=inputs[1], return_dict=False
+        )[0]
+        (
+            mlir_module,
+            func_name,
+        ) = import_with_fx(
+            model=opt_model,
+            inputs=inputs,
+            is_f16=False,
+            model_name=OPT_FS_NAME,
+        )
+        del opt_model
+        opt_filename = f"./{OPT_FS_NAME}_causallm_30_torch_{device}"
+        mlir_path = os.path.join(opt_filename, ".mlir")
+        with open(mlir_path, "w") as f:
+            f.write(mlir_module)
+        print(f"Saved mlir at {mlir_path}")
+
+        shark_module = SharkInference(
+            mlir_module,
+            device=device,
+            mlir_dialect="tm_tensor",
+            is_benchmark=self.benchmark,
+        )
+
+        shark_module.compile()
+        results = shark_module("forward", inputs)
+        print(
+            "SHARK logits have shape: ",
+            str(results[0].shape) + " : " + str(results[0]),
+        )
+        print(
+            "PyTorch logits have shape: "
+            + str(act_out[0].shape)
+            + " : "
+            + str(act_out[0])
+        )
+        # exp_out = tokenizer.decode(act_out[0][0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
+        # shark_out = tokenizer.decode(results[0][0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
+        np.testing.assert_allclose(act_out[0].detach(), results[0])
+
+        if self.benchmark:
+            shark_module.shark_runner.benchmark_all_csv(
+                inputs,
+                "opt",
+                dynamic,
+                device,
+                "torch",
+            )
+
+
+class OPTModuleTest(unittest.TestCase):
+    @pytest.fixture(autouse=True)
+    def configure(self, pytestconfig):
+        self.module_tester = OPTModuleTester(self)
+        self.module_tester.save_mlir = False
+        self.module_tester.save_vmfb = False
+        self.module_tester.benchmark = pytestconfig.getoption("benchmark")
+
+    def test_1_3b_static_cpu(self):
+        dynamic = False
+        device = "cpu"
+        self.module_tester.create_and_check_module(dynamic, device, OPT_MODEL)
+
+    def test_1_3b_dynamic_cpu(self):
+        dynamic = True
+        device = "cpu"
+        self.module_tester.create_and_check_module(dynamic, device, OPT_MODEL)
+
+    @pytest.mark.skipif(
+        check_device_drivers("cuda"), reason=device_driver_info("cuda")
+    )
+    def test_1_3b_static_cuda(self):
+        dynamic = False
+        device = "cuda"
+        self.module_tester.create_and_check_module(dynamic, device, OPT_MODEL)
+
+    @pytest.mark.skipif(
+        check_device_drivers("cuda"), reason=device_driver_info("cuda")
+    )
+    def test_1_3b_dynamic_cuda(self):
+        dynamic = True
+        device = "cuda"
+        self.module_tester.create_and_check_module(dynamic, device, OPT_MODEL)
+
+    @pytest.mark.skipif(
+        check_device_drivers("vulkan"), reason=device_driver_info("vulkan")
+    )
+    def test_1_3b_static_vulkan(self):
+        dynamic = False
+        device = "vulkan"
+        self.module_tester.create_and_check_module(dynamic, device, OPT_MODEL)
+
+    @pytest.mark.skipif(
+        check_device_drivers("vulkan"), reason=device_driver_info("vulkan")
+    )
+    def test_1_3b_dynamic_vulkan(self):
+        dynamic = True
+        device = "vulkan"
+        self.module_tester.create_and_check_module(dynamic, device, OPT_MODEL)
+
+    # def test_66b_static_cpu(self):
+    #    dynamic = False
+    #    device = "cpu"
+    #    self.module_tester.create_and_check_module(
+    #        dynamic, device, OPT_MODEL_66B
+    #    )
+
+    # def test_66b_dynamic_cpu(self):
+    #    dynamic = True
+    #    device = "cpu"
+    #    self.module_tester.create_and_check_module(
+    #        dynamic, device, OPT_MODEL_66B
+    #    )
+
+    # @pytest.mark.skipif(
+    #    check_device_drivers("cuda"), reason=device_driver_info("cuda")
+    # )
+    # def test_66b_static_cuda(self):
+    #    dynamic = False
+    #    device = "cuda"
+    #    self.module_tester.create_and_check_module(
+    #        dynamic, device, OPT_MODEL_66B
+    #    )
+
+    # @pytest.mark.skipif(
+    #    check_device_drivers("cuda"), reason=device_driver_info("cuda")
+    # )
+    # def test_66b_dynamic_cuda(self):
+    #    dynamic = True
+    #    device = "cuda"
+    #    self.module_tester.create_and_check_module(
+    #        dynamic, device, OPT_MODEL_66B
+    #    )
+
+    # @pytest.mark.skipif(
+    #    check_device_drivers("vulkan"), reason=device_driver_info("vulkan")
+    # )
+    # def test_66b_static_vulkan(self):
+    #    dynamic = False
+    #    device = "vulkan"
+    #    self.module_tester.create_and_check_module(
+    #        dynamic, device, OPT_MODEL_66B
+    #    )
+
+    # @pytest.mark.skipif(
+    #    check_device_drivers("vulkan"), reason=device_driver_info("vulkan")
+    # )
+    # def test_66b_dynamic_vulkan(self):
+    #    dynamic = True
+    #    device = "vulkan"
+    #    self.module_tester.create_and_check_module(
+    #        dynamic, device, OPT_MODEL_66B
+    #    )
+
+
+if __name__ == "__main__":
+    unittest.main()

tank/examples/opt/opt_torch_test.py

@@ -2,11 +2,11 @@ import unittest
 
 import pytest
 import torch_mlir
-from hacked_hf_opt import OPTModel
+from shark_hf_opt import OPTModel
 from shark.iree_utils._common import check_device_drivers, device_driver_info
 from shark.shark_inference import SharkInference
 from tank.model_utils import compare_tensors
-from transformers import GPT2Tokenizer
+from transformers import AutoTokenizer
 
 OPT_MODEL = "facebook/opt-350m"
 OPT_MODEL_66B = "facebook/opt-66b"
@@ -20,7 +20,7 @@ class OPTModuleTester:
         self.benchmark = benchmark
 
     def create_and_check_module(self, dynamic, device, model_name):
-        tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
         # config = OPTConfig()
         # opt_model = OPTModel(config)
         opt_model = OPTModel.from_pretrained(model_name)
@@ -56,13 +56,12 @@ class OPTModuleTester:
 
         shark_module = SharkInference(
             model_mlir,
-            func_name,
             device=device,
             mlir_dialect="tm_tensor",
             is_benchmark=self.benchmark,
         )
         shark_module.compile()
-        results = shark_module.forward((input_ids, attention_mask))
+        results = shark_module("forward", (input_ids, attention_mask))
         assert compare_tensors(act_out, results)
 
         if self.benchmark:

tank/examples/opt/shark_hf_base_opt.py

@@ -0,0 +1,47 @@
+import os
+import torch
+from transformers import AutoTokenizer, OPTForCausalLM
+from shark.shark_inference import SharkInference
+from shark.shark_importer import import_with_fx
+from shark_opt_wrapper import OPTForCausalLMModel
+
+model_name = "facebook/opt-1.3b"
+base_model = OPTForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
+
+model = OPTForCausalLMModel(base_model)
+
+prompt = "What is the meaning of life?"
+model_inputs = tokenizer(prompt, return_tensors="pt")
+inputs = (
+    model_inputs["input_ids"],
+    model_inputs["attention_mask"],
+)
+
+(
+    mlir_module,
+    func_name,
+) = import_with_fx(
+    model=model,
+    inputs=inputs,
+    is_f16=False,
+    debug=True,
+    model_name=model_name.split("/")[1],
+    save_dir=".",
+)
+
+shark_module = SharkInference(
+    mlir_module,
+    device="cpu-sync",
+    mlir_dialect="tm_tensor",
+)
+shark_module.compile()
+# Generated logits.
+logits = shark_module("forward", inputs=inputs)
+print("SHARK module returns logits:")
+print(logits[0])
+
+hf_logits = base_model.forward(inputs[0], inputs[1], return_dict=False)[0]
+
+print("PyTorch baseline returns logits:")
+print(hf_logits)

tank/examples/opt/shark_opt_wrapper.py

@@ -0,0 +1,15 @@
+import torch
+
+
+class OPTForCausalLMModel(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, input_ids, attention_mask):
+        combine_input_dict = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
+        output = self.model(**combine_input_dict)
+        return output.logits

tank/examples/opt/shark_opt_wrapper_train.py

@@ -279,7 +279,6 @@ class OPTAttention(nn.Module):
         )
 
         attn_output = torch.bmm(attn_probs, value_states)
-
         if attn_output.size() != (
             bsz * self.num_heads,
             tgt_len,
@@ -314,6 +313,7 @@ class OPTDecoderLayer(nn.Module):
             num_heads=config.num_attention_heads,
             dropout=config.attention_dropout,
             is_decoder=True,
+            bias=config.enable_bias,
         )
         self.do_layer_norm_before = config.do_layer_norm_before
         self.dropout = config.dropout
@@ -321,10 +321,16 @@ class OPTDecoderLayer(nn.Module):
 
         self.activation_dropout = config.activation_dropout
 
-        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+        self.self_attn_layer_norm = nn.LayerNorm(
+            self.embed_dim,
+            elementwise_affine=config.layer_norm_elementwise_affine,
+        )
         self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim)
         self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim)
-        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+        self.final_layer_norm = nn.LayerNorm(
+            self.embed_dim,
+            elementwise_affine=config.layer_norm_elementwise_affine,
+        )
 
     def forward(
         self,
@@ -450,7 +456,14 @@ class OPTDecoder(OPTPreTrainedModel):
         else:
             self.project_in = None
 
-        self.layer_norm = None
+        if config.do_layer_norm_before and not config._remove_final_layer_norm:
+            self.final_layer_norm = nn.LayerNorm(
+                config.hidden_size,
+                elementwise_affine=config.layer_norm_elementwise_affine,
+            )
+        else:
+            self.final_layer_norm = None
+
         self.layers = nn.ModuleList(
             [OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)]
         )
@@ -647,6 +660,9 @@ class OPTDecoder(OPTPreTrainedModel):
             if output_attentions:
                 all_self_attns += (layer_outputs[1],)
 
+        if self.final_layer_norm is not None:
+            hidden_states = self.final_layer_norm(hidden_states)
+
         if self.project_out is not None:
             hidden_states = self.project_out(hidden_states)
 
@@ -832,7 +848,10 @@ class OPTForCausalLM(OPTPreTrainedModel):
             )
 
         if not return_dict:
-            output = (logits,) + outputs[1:]
+            if isinstance(outputs[1:], tuple):
+                output = (logits,) + outputs[1:]
+            else:
+                output = (logits, outputs[1:])
             return (loss,) + output if loss is not None else output
 
         return CausalLMOutputWithPast(

tank/generate_sharktank.py

@@ -37,10 +37,12 @@ def save_torch_model(torch_model_list, local_tank_cache, import_args):
     from tank.model_utils import (
         get_hf_model,
         get_hf_seq2seq_model,
+        get_hf_causallm_model,
         get_vision_model,
         get_hf_img_cls_model,
         get_fp16_model,
     )
+    from shark.shark_importer import import_with_fx
 
     with open(torch_model_list) as csvfile:
         torch_reader = csv.reader(csvfile, delimiter=",")
@@ -50,6 +52,8 @@ def save_torch_model(torch_model_list, local_tank_cache, import_args):
             tracing_required = row[1]
             model_type = row[2]
             is_dynamic = row[3]
+            mlir_type = row[4]
+            is_decompose = row[5]
 
             tracing_required = False if tracing_required == "False" else True
             is_dynamic = False if is_dynamic == "False" else True
@@ -91,6 +95,10 @@ def save_torch_model(torch_model_list, local_tank_cache, import_args):
                 model, input, _ = get_hf_seq2seq_model(
                     torch_model_name, import_args
                 )
+            elif model_type == "hf_causallm":
+                model, input, _ = get_hf_causallm_model(
+                    torch_model_name, import_args
+                )
             elif model_type == "hf_img_cls":
                 model, input, _ = get_hf_img_cls_model(
                     torch_model_name, import_args
@@ -111,25 +119,45 @@ def save_torch_model(torch_model_list, local_tank_cache, import_args):
                 )
             os.makedirs(torch_model_dir, exist_ok=True)
 
-            mlir_importer = SharkImporter(
-                model,
-                (input,),
-                frontend="torch",
-            )
-            mlir_importer.import_debug(
-                is_dynamic=False,
-                tracing_required=tracing_required,
-                dir=torch_model_dir,
-                model_name=torch_model_name,
-            )
-            # Generate torch dynamic models.
-            if is_dynamic:
+            if is_decompose:
+                # Add decomposition to some torch ops
+                # TODO add op whitelist/blacklist
+                import_with_fx(
+                    model,
+                    (input,),
+                    is_f16=False,
+                    f16_input_mask=None,
+                    debug=True,
+                    training=False,
+                    return_str=False,
+                    save_dir=torch_model_dir,
+                    model_name=torch_model_name,
+                    mlir_type=mlir_type,
+                    is_dynamic=False,
+                    tracing_required=tracing_required,
+                )
+            else:
+                mlir_importer = SharkImporter(
+                    model,
+                    (input,),
+                    frontend="torch",
+                )
                 mlir_importer.import_debug(
-                    is_dynamic=True,
+                    is_dynamic=False,
                     tracing_required=tracing_required,
                     dir=torch_model_dir,
-                    model_name=torch_model_name + "_dynamic",
+                    model_name=torch_model_name,
+                    mlir_type=mlir_type,
                 )
+                # Generate torch dynamic models.
+                if is_dynamic:
+                    mlir_importer.import_debug(
+                        is_dynamic=True,
+                        tracing_required=tracing_required,
+                        dir=torch_model_dir,
+                        model_name=torch_model_name + "_dynamic",
+                        mlir_type=mlir_type,
+                    )
 
 
 def save_tf_model(tf_model_list, local_tank_cache, import_args):

tank/model_utils.py

@@ -176,6 +176,43 @@ def get_hf_seq2seq_model(name, import_args):
     return m, test_input, actual_out
 
 
+##################### Hugging Face CausalLM Models ###################################
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+def prepare_sentence_tokens(hf_model: str, sentence: str):
+    tokenizer = AutoTokenizer.from_pretrained(hf_model)
+    return torch.tensor([tokenizer.encode(sentence)])
+
+
+class HFCausalLM(torch.nn.Module):
+    def __init__(self, model_name: str):
+        super().__init__()
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name,  # The pretrained model name.
+            # The number of output labels--2 for binary classification.
+            num_labels=2,
+            # Whether the model returns attentions weights.
+            output_attentions=False,
+            # Whether the model returns all hidden-states.
+            output_hidden_states=False,
+            torchscript=True,
+        )
+        self.model.eval()
+
+    def forward(self, tokens):
+        return self.model.forward(tokens)[0]
+
+
+def get_hf_causallm_model(name, import_args):
+    m = HFCausalLM(name)
+    test_input = prepare_sentence_tokens(
+        name, "this project is very interesting"
+    )
+    actual_out = m.forward(*test_input)
+    return m, test_input, actual_out
+
+
 ################################################################################
 
 ##################### Torch Vision Models    ###################################

tank/test_models.py

@@ -64,6 +64,7 @@ def get_valid_test_params():
         device
         for device in get_supported_device_list()
         if not check_device_drivers(device)
+        and device not in ["cpu-sync", "cpu-task"]
     ]
     dynamic_list = (True, False)
     # TODO: This is soooo ugly, but for some reason creating the dict at runtime
@@ -92,6 +93,8 @@ def get_valid_test_params():
 def is_valid_case(test_params):
     if test_params[0] == True and test_params[2]["framework"] == "tf":
         return False
+    if test_params[2]["framework"] == "tf":
+        return False
     elif "fp16" in test_params[2]["model_name"] and test_params[1] != "cuda":
         return False
     else:
@@ -348,7 +351,11 @@ class SharkModuleTest(unittest.TestCase):
             self.pytestconfig.getoption("dispatch_benchmarks_dir")
         )
 
-        if config["xfail_cpu"] == "True" and device == "cpu":
+        if config["xfail_cpu"] == "True" and device in [
+            "cpu",
+            "cpu-sync",
+            "cpu-task",
+        ]:
             pytest.xfail(reason=config["xfail_reason"])
 
         if config["xfail_cuda"] == "True" and device == "cuda":

tank/torch_model_list.csv

@@ -1,23 +1,27 @@
-model_name, use_tracing, model_type, dynamic, param_count, tags, notes
-efficientnet_b0,True,vision,False,5.3M,"image-classification;cnn;conv2d;depthwise-conv","Smallest EfficientNet variant with 224x224 input"
-efficientnet_b7,True,vision,False,66M,"image-classification;cnn;conv2d;depthwise-conv","Largest EfficientNet variant with 600x600 input"
-microsoft/MiniLM-L12-H384-uncased,True,hf,True,66M,"nlp;bert-variant;transformer-encoder","Large version has 12 layers; 384 hidden size; Smaller than BERTbase (66M params vs 109M params)"
-bert-base-uncased,True,hf,True,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
-bert-base-cased,True,hf,True,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
-google/mobilebert-uncased,True,hf,True,25M,"nlp,bert-variant,transformer-encoder,mobile","24 layers, 512 hidden size, 128 embedding"
-alexnet,False,vision,True,61M,"cnn,parallel-layers","The CNN that revolutionized computer vision (move away from hand-crafted features to neural networks),10 years old now and probably no longer used in prod."
-resnet18,False,vision,True,11M,"cnn,image-classification,residuals,resnet-variant","1 7x7 conv2d and the rest are 3x3 conv2d"
-resnet50,False,vision,True,23M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
-resnet101,False,vision,True,29M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
-squeezenet1_0,False,vision,True,1.25M,"cnn,image-classification,mobile,parallel-layers","Parallel conv2d (1x1 conv to compress -> (3x3 expand | 1x1 expand) -> concat)"
-wide_resnet50_2,False,vision,True,69M,"cnn,image-classification,residuals,resnet-variant","Resnet variant where model depth is decreased and width is increased."
-mobilenet_v3_small,False,vision,True,2.5M,"image-classification,cnn,mobile",N/A
-google/vit-base-patch16-224,True,hf_img_cls,False,86M,"image-classification,vision-transformer,transformer-encoder",N/A
-microsoft/resnet-50,True,hf_img_cls,False,23M,"image-classification,cnn,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
-facebook/deit-small-distilled-patch16-224,True,hf_img_cls,False,22M,"image-classification,vision-transformer,cnn",N/A
-microsoft/beit-base-patch16-224-pt22k-ft22k,True,hf_img_cls,False,86M,"image-classification,transformer-encoder,bert-variant,vision-transformer",N/A
-nvidia/mit-b0,True,hf_img_cls,False,3.7M,"image-classification,transformer-encoder",SegFormer
-mnasnet1_0,False,vision,True,-,"cnn, torchvision, mobile, architecture-search","Outperforms other mobile CNNs on Accuracy vs. Latency"
-resnet50_fp16,False,vision,True,23M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
-bert-base-uncased_fp16,True,fp16,False,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
-bert-large-uncased,True,hf,True,330M,"nlp;bert-variant;transformer-encoder","24 layers, 1024 hidden units, 16 attention heads"
+model_name, use_tracing, model_type, dynamic, mlir_type, decompose, param_count, tags, notes
+efficientnet_b0,True,vision,False,linalg,False,5.3M,"image-classification;cnn;conv2d;depthwise-conv","Smallest EfficientNet variant with 224x224 input"
+efficientnet_b7,True,vision,False,linalg,False,66M,"image-classification;cnn;conv2d;depthwise-conv","Largest EfficientNet variant with 600x600 input"
+microsoft/MiniLM-L12-H384-uncased,True,hf,True,linalg,False,66M,"nlp;bert-variant;transformer-encoder","Large version has 12 layers; 384 hidden size; Smaller than BERTbase (66M params vs 109M params)"
+bert-base-uncased,True,hf,True,linalg,False,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
+bert-base-cased,True,hf,True,linalg,False,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
+google/mobilebert-uncased,True,hf,True,linalg,False,25M,"nlp,bert-variant,transformer-encoder,mobile","24 layers, 512 hidden size, 128 embedding"
+alexnet,False,vision,True,linalg,False,61M,"cnn,parallel-layers","The CNN that revolutionized computer vision (move away from hand-crafted features to neural networks),10 years old now and probably no longer used in prod."
+resnet18,False,vision,True,linalg,False,11M,"cnn,image-classification,residuals,resnet-variant","1 7x7 conv2d and the rest are 3x3 conv2d"
+resnet50,False,vision,True,linalg,False,23M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
+resnet101,False,vision,True,linalg,False,29M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
+squeezenet1_0,False,vision,True,linalg,False,1.25M,"cnn,image-classification,mobile,parallel-layers","Parallel conv2d (1x1 conv to compress -> (3x3 expand | 1x1 expand) -> concat)"
+wide_resnet50_2,False,vision,True,linalg,False,69M,"cnn,image-classification,residuals,resnet-variant","Resnet variant where model depth is decreased and width is increased."
+mobilenet_v3_small,False,vision,True,linalg,False,2.5M,"image-classification,cnn,mobile",N/A
+google/vit-base-patch16-224,True,hf_img_cls,False,linalg,False,86M,"image-classification,vision-transformer,transformer-encoder",N/A
+microsoft/resnet-50,True,hf_img_cls,False,linalg,False,23M,"image-classification,cnn,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
+facebook/deit-small-distilled-patch16-224,True,hf_img_cls,False,linalg,False,22M,"image-classification,vision-transformer,cnn",N/A
+microsoft/beit-base-patch16-224-pt22k-ft22k,True,hf_img_cls,False,linalg,False,86M,"image-classification,transformer-encoder,bert-variant,vision-transformer",N/A
+nvidia/mit-b0,True,hf_img_cls,False,linalg,False,3.7M,"image-classification,transformer-encoder",SegFormer
+mnasnet1_0,False,vision,True,linalg,False,-,"cnn, torchvision, mobile, architecture-search","Outperforms other mobile CNNs on Accuracy vs. Latency"
+resnet50_fp16,False,vision,True,linalg,False,23M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
+bert-base-uncased_fp16,True,fp16,False,linalg,False,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
+bert-large-uncased,True,hf,True,linalg,False,330M,"nlp;bert-variant;transformer-encoder","24 layers, 1024 hidden units, 16 attention heads"
+bert-base-uncased,True,hf,False,stablehlo,False,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
+gpt2,True,hf_causallm,False,stablehlo,True,125M,"nlp;transformer-encoder","-"
+facebook/opt-125m,True,hf,False,stablehlo,True,125M,"nlp;transformer-encoder","-"
+distilgpt2,True,hf,False,stablehlo,True,88M,"nlp;transformer-encoder","-"

tank_version.json

@@ -1,3 +1,3 @@
 {
-	"version": "2023-03-31_02d52bb"
+	"version": "nightly"
 }