FP8 Block quantize onnx export support

examples/diffusers/quantization/config.py

@@ -31,6 +31,21 @@
     "algorithm": "max",
 }
 
+FP8_SAGE_DEFAULT_CONFIG = {
+    "quant_cfg": {
+        "*weight_quantizer": {"num_bits": (4, 3), "axis": None},
+        "*input_quantizer": {"num_bits": (4, 3), "axis": None},
+        "*output_quantizer": {"enable": False},
+        "*[qkv]_bmm_quantizer": {"type": "dynamic", "num_bits": (4, 3), "block_sizes": {-2: 32}},
+        "*softmax_quantizer": {
+            "num_bits": (4, 3),
+            "axis": None,
+        },
+        "default": {"enable": False},
+    },
+    "algorithm": "max",
+}
+
 INT8_DEFAULT_CONFIG = {
     "quant_cfg": {
         "*weight_quantizer": {"num_bits": 8, "axis": 0},

examples/diffusers/quantization/quantize.py

@@ -939,7 +939,7 @@ def forward_loop(mod):
             backbone,
             model_config.model_type,
             quant_config.format,
-            quantize_mha=QuantizationConfig.quantize_mha,
+            quantize_mha=quant_config.quantize_mha,
         )
         logger.info("Quantization process completed successfully!")
 

modelopt/torch/quantization/export_onnx.py

@@ -235,6 +235,35 @@ def _fp8_quantize(
     return q_op
 
 
+def _fp8_block_quantize(
+    g: torch.onnx._internal.jit_utils.GraphContext,
+    inputs: torch.Value,
+    trt_high_precision_dtype: str,
+    block_sizes: list,
+):
+    """Helper Function for Quantization."""
+    output_shape = sym_help._get_tensor_sizes(inputs)
+
+    # TRT StronglyType only supports FP16 QDQs
+    # custom ops, so cast the input if needed.
+    input_type = inputs.type().scalarType()
+    assert trt_high_precision_dtype in (input_type, "Float"), (
+        "TRT StronglyType requires both weights and amax to be in the BF16/FP16, or the QDQ in Float."
+    )
+    if trt_high_precision_dtype != input_type:
+        inputs = g.op("Cast", inputs, to_i=onnx_dtype_map[trt_high_precision_dtype])
+    quantized_output, scales_output = g.op(
+        "trt::TRT_DynamicQuantize",
+        inputs,
+        block_shape_i=block_sizes,
+        outputs=2,
+        scale_type_i=onnx_dtype_map["Float"],
+        output_type_i=onnx_dtype_map["Float8"],
+    )
+    quantized_output.setType(inputs.type().with_dtype(torch.uint8).with_sizes(output_shape))
+    return quantized_output, scales_output
+
+
 def _fp8_dequantize(
     g: torch.onnx._internal.jit_utils.GraphContext,
     inputs: torch.Value,
@@ -262,20 +291,57 @@ def _fp8_dequantize(
     return out
 
 
+def _fp8_block_dequantize(
+    g: torch.onnx._internal.jit_utils.GraphContext,
+    inputs: torch.Value,
+    scales: torch.Value,
+    trt_high_precision_dtype: str,
+    otype: str | None = None,
+    block_sizes: list = [1, 1, 128, 1],
+):
+    """Helper Function for Dequantization."""
+    output_shape = sym_help._get_tensor_sizes(inputs)
+    assert trt_high_precision_dtype in (otype, "Float"), (
+        "TRT StronglyType requires both weights and amax to be in the BF16/FP16, or the QDQ in Float."
+    )
+    out = g.op(
+        "trt::TRT_BlockDequantize",
+        inputs,
+        scales,
+        block_shape_i=block_sizes,
+    ).setType(
+        inputs.type().with_dtype(torch_dtype_map[trt_high_precision_dtype]).with_sizes(output_shape)
+    )
+
+    # DQ outputs are currently constrained to FP32 due to a similar limitation in ORT
+    # custom ops, so cast the output if needed.
+    if trt_high_precision_dtype != otype:
+        out = g.op("Cast", out, to_i=onnx_dtype_map[otype])  # type: ignore[index]
+    return out
+
+
 def export_fp8(
     g: torch.onnx._internal.jit_utils.GraphContext,
     inputs: torch.Value,
-    amax: float,
+    amax: float | None,
     trt_high_precision_dtype: str | None,
+    block_sizes: list | None,
 ):
     """Export quantized model to FP8 ONNX."""
     scale = 1.0 if amax is None else 448.0 / float(amax)
     otype = inputs.type().scalarType()
     if trt_high_precision_dtype is None:
         trt_high_precision_dtype = otype
-
-    q_tensor = _fp8_quantize(g, inputs, 1.0 / scale, trt_high_precision_dtype)
-    return _fp8_dequantize(g, q_tensor, 1.0 / scale, trt_high_precision_dtype, otype)
+    if not block_sizes:
+        q_tensor = _fp8_quantize(g, inputs, 1.0 / scale, trt_high_precision_dtype)
+        return _fp8_dequantize(g, q_tensor, 1.0 / scale, trt_high_precision_dtype, otype)
+    else:
+        q_tensor, scales_output = _fp8_block_quantize(
+            g, inputs, trt_high_precision_dtype, block_sizes
+        )
+        return _fp8_block_dequantize(
+            g, q_tensor, scales_output, trt_high_precision_dtype, otype, block_sizes
+        )
 
 
 def scaled_dot_product_attention(
@@ -365,11 +431,14 @@ def export_fp8_mha(
     dropout_p: float = 0.0,
     is_causal: bool = False,
     scale: torch._C.Value | None = None,
-    q_quantized_scale: float = 1.0,
-    k_quantized_scale: float = 1.0,
-    v_quantized_scale: float = 1.0,
+    q_quantized_scale: float | None = 1.0,
+    k_quantized_scale: float | None = 1.0,
+    v_quantized_scale: float | None = 1.0,
     high_precision_flag: str = "Half",
     disable_fp8_mha: bool = True,
+    q_block_shape: list | None = None,
+    k_block_shape: list | None = None,
+    v_block_shape: list | None = None,
 ):
     r"""Export quantized fMHA to FP8 ONNX.
 
@@ -440,10 +509,12 @@ def export_fp8_mha(
         v_input_dtype = value.type().scalarType()
         if {q_input_dtype, k_input_dtype, v_input_dtype} != {high_precision_flag}:
             raise ValueError("The quantized MHA must have 16-bit inputs.")
-        query_scaled = export_fp8(g, query_scaled, q_quantized_scale, high_precision_flag)
+        query_scaled = export_fp8(
+            g, query_scaled, q_quantized_scale, high_precision_flag, q_block_shape
+        )
         query_scaled = g.op("Cast", query_scaled, to_i=onnx_dtype_map["Float"])
         key_transposed_scaled = export_fp8(
-            g, key_transposed_scaled, k_quantized_scale, high_precision_flag
+            g, key_transposed_scaled, k_quantized_scale, high_precision_flag, k_block_shape
         )
         key_transposed_scaled = g.op("Cast", key_transposed_scaled, to_i=onnx_dtype_map["Float"])
     mul_qk = g.op("MatMul", query_scaled, key_transposed_scaled)
@@ -473,7 +544,8 @@ def export_fp8_mha(
 
     if not disable_fp8_mha:
         # Softmax's output scale is hard coded to 1.0
-        attn_weight = export_fp8(g, attn_weight, 1.0, high_precision_flag)
+        # We cannot do block quant for the softmax's output
+        attn_weight = export_fp8(g, attn_weight, 1.0, high_precision_flag, None)
         attn_weight = g.op("Cast", attn_weight, to_i=onnx_dtype_map["Float"])
 
     if dropout_p != 0:
@@ -483,7 +555,7 @@ def export_fp8_mha(
             g.op("Constant", value_t=torch.tensor(dropout_p, dtype=torch.float)),
         )
     if not disable_fp8_mha:
-        value = export_fp8(g, value, v_quantized_scale, high_precision_flag)
+        value = export_fp8(g, value, v_quantized_scale, high_precision_flag, v_block_shape)
         value = g.op("Cast", value, to_i=onnx_dtype_map["Float"])
         return g.op(
             "Cast",

modelopt/torch/quantization/nn/modules/tensor_quantizer.py

@@ -646,6 +646,29 @@ def _real_quantize(self, inputs):
         self._dequantize = True
         return outputs
 
+    def _get_block_sizes_list(self, shape):
+        """Convert block_sizes dict to list format based on tensor shape.
+
+        Args:
+            shape: The tensor shape to use for conversion (can be tuple or torch.Size)
+
+        Returns:
+            List of block sizes for each dimension, or None if block_sizes is None
+
+        Example:
+            block_sizes = {-2: 32} with shape [2, 24, 4608, 128] -> [1, 1, 32, 1]
+        """
+        if self.block_sizes is None:
+            return None
+
+        block_sizes_list = []
+        for dim in range(len(shape)):
+            # Check both positive and negative dimension indices
+            dim_negative = dim - len(shape)
+            block_size = self.block_sizes.get(dim, None) or self.block_sizes.get(dim_negative, None)
+            block_sizes_list.append(block_size if block_size is not None else 1)
+        return block_sizes_list
+
     def _fake_quantize(self, inputs):
         """Fake quantization."""
         amax = None
@@ -654,7 +677,7 @@ def _fake_quantize(self, inputs):
             self._validate_amax(amax)
 
         if self.block_sizes is not None and self.block_sizes.get("type", "static") == "dynamic":
-            # Block quantization, including dynamic and static block quantization
+            # Double scale Block quantization, including dynamic and static block quantization
             block_size = self.block_sizes.get(-1, None) or self.block_sizes.get(
                 inputs.dim() - 1, None
             )
@@ -675,6 +698,10 @@ def _fake_quantize(self, inputs):
             # Float-point quantization, e.g., FP8
             E, M = self._num_bits  # noqa: N806
 
+            # Convert block_sizes dict to list format
+            # Use original input shape if available (before reshaping), otherwise use current shape
+            shape_for_block_sizes = getattr(self, "_original_input_shape", inputs.shape)
+            block_sizes_list = self._get_block_sizes_list(shape_for_block_sizes)
             outputs = scaled_e4m3(
                 inputs,
                 amax,
@@ -683,6 +710,7 @@ def _fake_quantize(self, inputs):
                 M,
                 self._trt_high_precision_dtype,
                 self._pass_through_bwd,
+                block_sizes_list,
             )
 
         else:
@@ -931,9 +959,10 @@ def forward(self, inputs):
             and self.block_sizes.get("type", None) != "dynamic"
             and self._fake_quant
         ):
-            # Tensor reshaping is required for static block quantization
-            # Tensor shapes are handled separately by the quantization kernels for dynamic block quantization
+            # Reshape is required if the logic is not handled in the simulation kernel
+            # Only MX format and NVFP4 reshape are currently supported by the kernel.
             self._setup_for_blockquant(inputs)
+            setattr(self, "_original_input_shape", inputs.shape)
             inputs = self._process_for_blockquant(inputs)
 
         outputs = inputs
@@ -974,6 +1003,8 @@ def forward(self, inputs):
         ):
             outputs = self._reset_to_original_shape(outputs)
 
+        if hasattr(self, "_original_input_shape"):
+            delattr(self, "_original_input_shape")
         return outputs
 
     def _short_amax(self, fmt=".4f"):

modelopt/torch/quantization/plugins/diffusers.py

@@ -114,9 +114,26 @@ def _quantized_sdpa(self, *args, **kwargs):
         key = self.k_bmm_quantizer(key)
         value = self.v_bmm_quantizer(value)
 
-    q_quantized_scale = self.q_bmm_quantizer._get_amax(query)
-    k_quantized_scale = self.k_bmm_quantizer._get_amax(key)
-    v_quantized_scale = self.v_bmm_quantizer._get_amax(value)
+    if (
+        not self.q_bmm_quantizer._dynamic
+        and not self.k_bmm_quantizer._dynamic
+        and not self.v_bmm_quantizer._dynamic
+    ):
+        q_quantized_scale = self.q_bmm_quantizer._get_amax(query)
+        k_quantized_scale = self.k_bmm_quantizer._get_amax(key)
+        v_quantized_scale = self.v_bmm_quantizer._get_amax(value)
+    else:
+        assert (
+            self.q_bmm_quantizer._dynamic
+            and self.k_bmm_quantizer._dynamic
+            and self.v_bmm_quantizer._dynamic
+        ), "QKV QDQS must be in the same type"
+        q_quantized_scale, k_quantized_scale, v_quantized_scale = None, None, None
+
+    # Get block sizes lists for each quantizer if needed
+    q_block_sizes = self.q_bmm_quantizer._get_block_sizes_list(query.shape)  # type: ignore[union-attr]
+    k_block_sizes = self.k_bmm_quantizer._get_block_sizes_list(key.shape)  # type: ignore[union-attr]
+    v_block_sizes = self.v_bmm_quantizer._get_block_sizes_list(value.shape)  # type: ignore[union-attr]
 
     # We don't need to calibrate the output of softmax
     return self.bmm2_output_quantizer(
@@ -132,6 +149,9 @@ def _quantized_sdpa(self, *args, **kwargs):
             if hasattr(self.q_bmm_quantizer, "trt_high_precision_dtype")
             else "Half",
             self._disable_fp8_mha if hasattr(self, "_disable_fp8_mha") else True,
+            q_block_sizes,
+            k_block_sizes,
+            v_block_sizes,
         )
     )
 
@@ -185,6 +205,9 @@ def forward(
         v_quantized_scale=None,
         high_precision_flag=None,
         disable_fp8_mha=True,
+        q_block_shape: list | None = None,
+        k_block_shape: list | None = None,
+        v_block_shape: list | None = None,
     ):
         """Forward method."""
         ctx.save_for_backward(query, key, value, attn_mask)
@@ -203,7 +226,9 @@ def forward(
         )
 
     @staticmethod
-    @symbolic_helper.parse_args("v", "v", "v", "v", "f", "b", "v", "t", "t", "t", "s", "b")
+    @symbolic_helper.parse_args(
+        "v", "v", "v", "v", "f", "b", "v", "t", "t", "t", "s", "b", "is", "is", "is"
+    )
     def symbolic(
         g: jit_utils.GraphContext,
         query: torch._C.Value,
@@ -213,11 +238,14 @@ def symbolic(
         dropout_p: float = 0.0,
         is_causal: bool = False,
         scale: torch._C.Value | None = None,
-        q_quantized_scale: float = 1.0,
-        k_quantized_scale: float = 1.0,
-        v_quantized_scale: float = 1.0,
+        q_quantized_scale: float | None = 1.0,
+        k_quantized_scale: float | None = 1.0,
+        v_quantized_scale: float | None = 1.0,
         high_precision_flag: str = "Half",
         disable_fp8_mha: bool = True,
+        q_block_shape: list | None = None,
+        k_block_shape: list | None = None,
+        v_block_shape: list | None = None,
     ):
         """Symbolic method."""
         return export_fp8_mha(
@@ -234,4 +262,7 @@ def symbolic(
             v_quantized_scale,
             high_precision_flag,
             disable_fp8_mha,
+            q_block_shape,
+            k_block_shape,
+            v_block_shape,
         )

modelopt/torch/quantization/tensor_quant.py

@@ -412,7 +412,7 @@ class ScaledE4M3Function(Function):
     """E4M3fy input with scale."""
 
     @staticmethod
-    @symbolic_helper.parse_args("v", "t", "t", "i", "i", "s", "b")
+    @symbolic_helper.parse_args("v", "t", "t", "is", "i", "i", "s", "b")
     def symbolic(
         g,
         inputs,
@@ -422,11 +422,12 @@ def symbolic(
         M=3,  # noqa: N803
         trt_high_precision_dtype=None,
         pass_through_bwd=False,
+        block_sizes=None,
     ):
         """ONNX symbolic function."""
         from .export_onnx import export_fp8
 
-        return export_fp8(g, inputs, amax, trt_high_precision_dtype)
+        return export_fp8(g, inputs, amax, trt_high_precision_dtype, block_sizes)
 
     @staticmethod
     # Default values could cause errors from TorchDynamo during torch.export
@@ -439,6 +440,7 @@ def forward(
         M,  # noqa: N803
         trt_high_precision_dtype=None,
         pass_through_bwd=False,
+        block_sizes=None,
     ):
         """Forward method."""
         if E != 4 or M != 3:
@@ -466,7 +468,7 @@ def forward(
     @staticmethod
     def backward(ctx, grad_outputs):
         """Implements straight through estimation with clipping."""
-        return _fake_quant_backward_function(ctx, grad_outputs, num_args=7)
+        return _fake_quant_backward_function(ctx, grad_outputs, num_args=8)
 
 
 def _dynamic_block_quantize_forward(