[ET-VK][patterns] Fuse torchao 4-bit quantized embedding to embedding_q4gsw

Pull Request resolved: #20381

TISO and other torchao-quantized models emit a `torchao.dequantize_affine -> aten.embedding` subgraph for their weight-only int4 quantized embedding. The existing `QuantizedEmbeddingMatch` only matches the `quantized_decomposed.embedding_4bit.dtype` fused op, so the torchao embedding never fused: its `dequantize_affine` const-folded to an fp32 weight, the resulting `aten.embedding` exceeded the buffer-element limit and fell back to CPU, and the fp32 constant bloated the serialized model.

This adds a separate `TorchAOQuantizedEmbeddingMatch` matcher that recognizes the torchao int4 `dequantize_affine -> aten.embedding` shape (qmin=-8/qmax=7, per-row group block_size `[1, G]`) and rewrites it to the existing `et_vk.embedding_q4gsw.default` op, repacking the unpacked int8 weight into the packed 4-bit layout. It asserts symmetric quantization (zero_point == 0, which the shader assumes) and guards against repacking a shared/tied weight more than once via an `et_vk_embedding_q4gsw_packed` meta flag. It is kept as a separate class from `QuantizedEmbeddingMatch` because the two dialects produce different graph shapes (one fused op vs a split dequant+gather), so a single class would only co-locate two disjoint parse paths.

On the en_US TISO backbone the embedding now delegates to Vulkan instead of falling back to CPU, and the serialized `.pte` drops from 418 MiB to 348 MiB.

This change was authored with Claude.
ghstack-source-id: 396618161
@exported-using-ghexport

Differential Revision: [D108457797](https://our.internmc.facebook.com/intern/diff/D108457797/)

Author: ssjia

backends/vulkan/patterns/quantized_embedding.py

@@ -18,6 +18,11 @@
 from executorch.exir.dialects._ops import ops as exir_ops
 
 
+embedding_4bit_target = exir_ops.edge.quantized_decomposed.embedding_4bit.dtype
+embedding_target = exir_ops.edge.aten.embedding.default
+torchao_dequantize_affine_target = exir_ops.edge.torchao.dequantize_affine.default
+
+
 class QuantizedEmbeddingMatch(PatternMatch):
     def __init__(self, node: torch.fx.Node) -> None:
         self.anchor_node = node
@@ -68,9 +73,6 @@ def __init__(self, node: torch.fx.Node) -> None:
         self.match_found = True
 
 
-embedding_4bit_target = exir_ops.edge.quantized_decomposed.embedding_4bit.dtype
-
-
 def _detect_tied_linear_weight(
     ep: ExportedProgram,
     weight_node: torch.fx.Node,
@@ -175,3 +177,163 @@ def replace_quantized_embedding_patterns(
 
     embedding_q4gsw_node.meta["val"] = match.anchor_node.meta["val"]
     match.anchor_node.replace_all_uses_with(embedding_q4gsw_node)
+
+
+class TorchAOQuantizedEmbeddingMatch(PatternMatch):
+    """Matches a torchao 4-bit weight-only quantized embedding and rewrites it
+    as a single et_vk.embedding_q4gsw.default node.
+
+    The recognized graph shape is a split torchao.dequantize_affine ->
+    aten.embedding, whose weight is unpacked int8 [vocab, embed_dim] with values
+    in [-8, 7]. This requires symmetric 4-bit signed quantization (quant_min=-8,
+    quant_max=7, zero_point=0) and per-row groupwise blocks (block_size=[1, G]),
+    which the runtime shader assumes via a fixed subtract-8 offset.
+    """
+
+    def __init__(self, node: torch.fx.Node) -> None:
+        self.anchor_node = node
+        self.match_found = False
+        self.all_nodes = [node]
+
+        # aten.embedding.default args: (weight, indices, *)
+        dequant_node = node.args[0]
+        self.indices_node = node.args[1]
+
+        if not isinstance(dequant_node, torch.fx.Node):
+            return
+        if dequant_node.target != torchao_dequantize_affine_target:
+            return
+
+        self.all_nodes.append(dequant_node)
+
+        # torchao.dequantize_affine args:
+        # (input, block_size, scale, zero_point, input_dtype, quant_min,
+        #  quant_max, ...)
+        block_size = dequant_node.args[1]
+        quant_min = dequant_node.args[5] if len(dequant_node.args) > 5 else None
+        quant_max = dequant_node.args[6] if len(dequant_node.args) > 6 else None
+
+        # The shader hardcodes the 4-bit signed offset (subtract 8), which
+        # corresponds to quant_min=-8, quant_max=7, zero_point=0.
+        if quant_min != -8 or quant_max != 7:
+            return
+
+        # block_size must be per-row groupwise: [1, group_size]
+        if not isinstance(block_size, (list, tuple)) or len(block_size) != 2:
+            return
+        if block_size[0] != 1:
+            return
+        self.group_size = int(block_size[1])
+
+        # Trace weight (args[0]), scales (args[2]) and zero_point (args[3]) to
+        # their placeholders. The symmetric (zero_point == 0) requirement is
+        # verified on the real tensor in the replacement function, where the
+        # ExportedProgram is available; checking the fake meta tensor here would
+        # trigger a data-dependent guard error.
+        weight_node, arg_chain = utils.trace_args_until_placeholder(
+            dequant_node.args[0]
+        )
+        if weight_node is None:
+            return
+        self.weight_node = weight_node
+        self.all_nodes.extend(arg_chain)
+
+        scales_node, arg_chain = utils.trace_args_until_placeholder(
+            dequant_node.args[2]
+        )
+        if scales_node is None:
+            return
+        self.scales_node = scales_node
+        self.all_nodes.extend(arg_chain)
+
+        self.zero_point_node, arg_chain = utils.trace_args_until_placeholder(
+            dequant_node.args[3]
+        )
+        self.all_nodes.extend(arg_chain)
+
+        self.match_found = True
+
+
+@register_pattern_detector("torchao_quantized_embedding")
+def find_torchao_quantized_embedding_patterns(
+    node: torch.fx.Node,
+) -> Optional[TorchAOQuantizedEmbeddingMatch]:
+    if node.target != embedding_target:
+        return None
+
+    matched_pattern = TorchAOQuantizedEmbeddingMatch(node)
+    if matched_pattern.match_found:
+        return matched_pattern
+    return None
+
+
+@register_pattern_replacement("torchao_quantized_embedding")
+def replace_torchao_quantized_embedding_patterns(
+    ep: ExportedProgram,
+    graph_module: torch.fx.GraphModule,
+    match: TorchAOQuantizedEmbeddingMatch,
+):
+    weight_tensor = get_param_tensor(ep, match.weight_node)
+    assert weight_tensor is not None
+
+    # The weight repack mutates the state dict entry in place, so it must run
+    # exactly once per backing storage; a second repack of the already-packed
+    # weight would corrupt it. The repack
+    # (align_width_and_update_state_dict -> update_program_state_dict) locates
+    # the entry to overwrite by the param/buffer FQN that backs the placeholder,
+    # so the idempotency guard keys on that same FQN (via
+    # utils.register_param_mutation). This dedups not only one placeholder
+    # shared by multiple call sites, but also distinct placeholder nodes that
+    # resolve to the same state dict storage (whose per-node meta would otherwise
+    # diverge). Distinct weights (distinct FQNs) still each pack once. The guard
+    # also raises if the same weight is later re-mutated with a different tag
+    # (i.e. an incompatible packing format), surfacing corruption loudly.
+    if utils.register_param_mutation(ep, match.weight_node, "embedding_q4gsw"):
+        # The shader applies a fixed signed-4-bit offset (subtract 8), which
+        # assumes symmetric quantization (zero_point == 0). Verify on the real
+        # tensor.
+        if match.zero_point_node is not None:
+            zero_point_tensor = get_param_tensor(ep, match.zero_point_node)
+            if zero_point_tensor is not None:
+                assert torch.all(
+                    zero_point_tensor == 0
+                ), "embedding_q4gsw requires symmetric quantization (zero_point == 0)"
+
+        # Repack the unpacked int8 weight [vocab, embed_dim] (values in [-8, 7])
+        # into the flat 4-bit packed format [vocab, embed_dim / 2] that the
+        # non-linear embedding_q4gsw path expects. Packing convention (must
+        # match the runtime shader and embedding_q4gsw_impl):
+        #   packed_byte = (even_val + 8) << 4 | (odd_val + 8)
+        # i.e. the even-index value goes in the high nibble, odd-index in the
+        # low.
+        unpacked_u8 = weight_tensor.to(torch.uint8) + 8
+        packed_weight = (unpacked_u8[:, ::2] << 4 | unpacked_u8[:, 1::2]).to(
+            torch.uint8
+        )
+
+        # Update the weight placeholder's state dict entry and fake-tensor meta
+        # to the repacked tensor. align_to=1 with force_update just forces the
+        # update; the packed width (embed_dim / 2) is already a multiple of 4.
+        utils.align_width_and_update_state_dict(
+            ep, match.weight_node, packed_weight, align_to=1, force_update=True
+        )
+
+    # Scales are symmetric per-group with layout [vocab, num_groups], matching
+    # the scale layout embedding_q4gsw expects (no transpose).
+    group_size = match.group_size
+
+    with graph_module.graph.inserting_before(match.anchor_node):
+        embedding_q4gsw_node = graph_module.graph.create_node(
+            "call_function",
+            exir_ops.edge.et_vk.embedding_q4gsw.default,
+            args=(
+                match.weight_node,
+                match.scales_node,
+                group_size,
+                match.indices_node,
+                False,
+            ),
+        )
+
+    embedding_q4gsw_node.meta["val"] = match.anchor_node.meta["val"]
+    match.anchor_node.replace_all_uses_with(embedding_q4gsw_node)