[not for land] towards QAT with exact forward pass

Summary:

Exploration for QAT with exact (not emulated) forward pass, WIP and not
ready for review.

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

Author: vkuzo

torchao/prototype/qat_exact/main.py

@@ -0,0 +1,193 @@
+"""
+Prototype of QAT with exact (instead of emulated) forward pass using
+integer matrix multiply.
+
+Quant spec:
+* int4 symmetric weights w/ group size 32 or 256,
+* int8 asymmetric per-token dynamic activations
+
+"""
+
+import copy
+
+import fire
+import torch
+import torch.nn as nn
+
+from torchao.float8.float8_utils import compute_error
+from torchao.prototype.qat_exact.reference_gemm import (
+    cpu_x_token_assym_fp8_w_group_sym_int4_gemm,
+    naive_x_token_assym_fp8_w_group_sym_int4_gemm,
+)
+from torchao.prototype.qat_exact.triton_gemm import int8_matmul_triton
+from torchao.quantization import quantize_
+from torchao.quantization.qat import (
+    FakeQuantizeConfig,
+    IntXQuantizationAwareTrainingConfig,
+)
+from torchao.quantization.qat.fake_quantizer import FakeQuantizer
+from torchao.quantization.quant_primitives import (
+    _DTYPE_TO_QVALUE_BOUNDS,
+    MappingType,
+)
+from torchao.quantization.utils import (
+    _get_per_token_block_size,
+)
+
+torch.manual_seed(0)
+
+
+def quantize_x(x_fp32):
+    # Dynamic quantization of activation
+    x_mapping_type = MappingType.ASYMMETRIC
+    per_token_block_size = _get_per_token_block_size(x_fp32)
+    x_quant_min, x_quant_max = _DTYPE_TO_QVALUE_BOUNDS[torch.int8]
+    x_eps = torch.finfo(torch.float32).eps
+    x_scales_type = torch.float32
+    x_zero_points_type = torch.int32
+    x_scale, x_zero_point = torch.ops.torchao.choose_qparams_affine(
+        x_fp32,
+        x_mapping_type.name,
+        per_token_block_size,
+        torch.int8,
+        x_quant_min,
+        x_quant_max,
+        x_eps,
+        x_scales_type,
+        x_zero_points_type,
+    )
+    x_i8 = torch.ops.torchao.quantize_affine(
+        x_fp32,
+        per_token_block_size,
+        x_scale,
+        x_zero_point,
+        torch.int8,
+        x_quant_min,
+        x_quant_max,
+    )
+    return x_i8, x_scale, x_zero_point
+
+
+class Int8PerTokenActivationInt4PerGroupWeightLinear(torch.nn.Linear):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # manually create fake quantizer configs
+        activation_config = FakeQuantizeConfig(
+            torch.int8, "per_token", is_symmetric=False
+        )
+        weight_config = FakeQuantizeConfig(torch.int4, group_size=32)
+
+        # manually create fake quantizers
+        # reference: `FakeQuantizedLinear` (https://github.com/pytorch/ao/blob/c2a6568a04075acc371a338206216bb65536fb27/torchao/quantization/qat/linear.py)
+        self.activation_fq = FakeQuantizer(activation_config)
+        self.weight_fq = FakeQuantizer(weight_config)
+
+    def forward(self, input):
+        # quantize x
+        input_i8, input_scale, input_zp = quantize_x(input)
+
+        # quantize w
+        _ = self.weight_fq(self.weight)
+        w_qmin, w_qmax = _DTYPE_TO_QVALUE_BOUNDS[torch.int4]
+        w_granularity = self.weight_fq.config.granularity
+        w_group_size = w_granularity.group_size
+        w_block_size = (1, w_group_size)
+        weight_int4 = torch.ops.torchao.quantize_affine(
+            self.weight,
+            w_block_size,
+            self.weight_fq.scale,
+            self.weight_fq.zero_point,
+            torch.int8,
+            w_qmin,
+            w_qmax,
+        )
+
+        # original reference
+        q_output = naive_x_token_assym_fp8_w_group_sym_int4_gemm(
+            input_i8.to(torch.int32),
+            input_scale,
+            input_zp,
+            weight_int4.to(torch.int32),
+            self.weight_fq.scale,
+            w_group_size,
+        )
+
+        # now also check Kimish's implementation
+        q_output2 = cpu_x_token_assym_fp8_w_group_sym_int4_gemm(
+            input_i8.cpu(),
+            input_scale.cpu(),
+            input_zp.cpu(),
+            weight_int4.cpu(),
+            self.weight_fq.scale.cpu(),
+            self.weight_fq.zero_point.cpu(),
+            self.bias,
+            self.weight_fq.config.granularity.group_size,
+        ).cuda()
+        sqnr = compute_error(q_output, q_output2)
+        print("sqnr vasiliy_reference vs kimish_reference", sqnr)
+
+        # finally, check vs triton gemm
+        q_output3 = int8_matmul_triton(
+            input_i8,
+            weight_int4.t(),
+            input_scale,
+            input_zp,
+            self.weight_fq.scale.t(),
+            w_group_size,
+        )
+
+        sqnr3 = compute_error(q_output, q_output3)
+        print("sqnr vasiliy_reference vs triton", sqnr3)
+
+        sqnr4 = compute_error(q_output2, q_output3)
+        print("sqnr kimish_reference vs triton", sqnr4)
+
+        return q_output, q_output2
+
+    @classmethod
+    def from_float(cls, mod: torch.nn.Linear):
+        new_mod = cls(mod.in_features, mod.out_features)
+        new_mod.weight = mod.weight
+        new_mod.bias = mod.bias
+        return new_mod
+
+
+def run():
+    M, K, N = 32, 64, 128
+
+    # TODO(before land): also implement bias=True
+    m_hp = nn.Sequential(nn.Linear(K, N, bias=False)).cuda()
+    mq_ref = copy.deepcopy(m_hp)
+    mq = copy.deepcopy(m_hp)
+
+    # create a baseline: QAT with fake quants. Our exact QAT's output should
+    # be close to this
+    activation_config = FakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False)
+    weight_config = FakeQuantizeConfig(torch.int4, group_size=32)
+    quantize_(
+        mq_ref,
+        IntXQuantizationAwareTrainingConfig(activation_config, weight_config),
+    )
+
+    # create the experiment: forward pass with an integer gemm
+    mq[0] = Int8PerTokenActivationInt4PerGroupWeightLinear.from_float(mq[0])
+
+    x_hp = torch.randn(M, K, device="cuda")
+    xq_ref = copy.deepcopy(x_hp)
+    xq = copy.deepcopy(x_hp)
+
+    with torch.no_grad():
+        y_hp = m_hp(x_hp)
+        yq_ref = mq_ref(xq_ref)
+        yq, yq2 = mq(xq)
+
+    sqnr_hp_qref = compute_error(y_hp, yq_ref)
+    sqnr_hp_q = compute_error(y_hp, yq)
+    sqnr_qref_q = compute_error(yq_ref, yq)
+    print("sqnr_hp_qref", sqnr_hp_qref)
+    print("sqnr_hp_q", sqnr_hp_q)
+    print("sqnr_qref_q", sqnr_qref_q)
+
+
+if __name__ == "__main__":
+    fire.Fire(run)

torchao/prototype/qat_exact/reference_gemm.py

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+import torch
+from torch._higher_order_ops.out_dtype import out_dtype
+
+
+def cpu_x_token_assym_fp8_w_group_sym_int4_gemm(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    weight_int4,
+    weight_scale,
+    weight_zero_point,
+    bias_fp32,
+    group_size,
+):
+    # For groupwise quantization, we need to handle the computation differently
+    # weight_i4 shape: [out_features, in_features]
+    # weight_scale shape: [out_features, in_features // group_size]
+    # weight_zero_point shape: [out_features, in_features // group_size]
+    out_features, in_features = weight_int4.shape
+    num_groups = in_features // group_size
+
+    # scales in xnnpack are stored as bf16 and converted to fp32 for computation
+    weight_scale = weight_scale.to(torch.bfloat16).to(torch.float32)
+
+    assert x_i8.dim() == 2, "x_i8 must be 2D tensor"
+    # Reshape for group-wise processing
+    # x: [batch_size, in_features] -> [batch_size, num_groups, group_size]
+    batch_size = x_i8.shape[0]
+    x_i8_grouped = x_i8.view(batch_size, num_groups, group_size)
+
+    # weight: [out_features, in_features] -> [out_features, num_groups, group_size]
+    weight_i4_grouped = weight_int4.view(out_features, num_groups, group_size)
+
+    # Convert to int16 for computation
+    x_i32_grouped = x_i8_grouped.to(torch.int32)
+    weight_i32_grouped = weight_i4_grouped.to(torch.int32)
+
+    # Perform groupwise integer linear operation
+    acc_fp32 = torch.zeros(
+        batch_size, out_features, dtype=torch.float32, device=x_i8.device
+    )
+
+    for group_idx in range(num_groups):
+        # Extract current group
+        x_group = x_i32_grouped[:, group_idx, :]  # [batch_size, group_size]
+        weight_group = weight_i32_grouped[:, group_idx, :]  # [out_features, group_size]
+        weight_group_col_sum = weight_group.sum(dim=-1)  # [out_features]
+
+        # Get scale for this group
+        weight_scale_group = weight_scale[:, group_idx]  # [out_features]
+
+        # Integer matmul: [batch_size, group_size] @ [group_size, out_features] -> [batch_size, out_features]
+        group_acc = out_dtype(
+            torch.ops.aten.linear.default,
+            torch.int32,
+            x_group,
+            weight_group,
+            None,
+        )
+
+        # Output has to be scaled by x_scale * weight_scale_group
+        # However we will first scale by weight_scale_group, that is accounting
+        # only for scale of weight, and then scale by x_scale at the end because
+        # x_scale applies to all groups
+        acc_fp32 = acc_fp32 + group_acc.to(torch.float32) * weight_scale_group.view(
+            1, -1
+        )
+
+        # we must also subtract x_zero_point * weight_group_sum
+        # since (X - x_zero_point) * W = X * W - x_zero_point * W
+        weights_col_sum_adjusted = (
+            weight_group_col_sum.to(torch.float32).view(1, -1)
+            * x_zero_point.view(-1, 1)
+            * weight_scale_group.view(1, -1)
+        )
+        acc_fp32 = acc_fp32 - weights_col_sum_adjusted
+    x_scale_multiplier = x_scale.view(-1, 1)
+    out_fp32 = acc_fp32 * x_scale_multiplier
+    if bias_fp32 is not None:
+        out_fp32 = out_fp32 + bias_fp32
+
+    return out_fp32
+
+
+def naive_x_token_assym_fp8_w_group_sym_int4_gemm(
+    act_q,
+    act_scale,
+    act_zp,
+    w_q,
+    w_scale,
+    w_group_size,
+) -> torch.Tensor:
+    #
+    # now we have the scales/zero_points/quant values for both gemm operands
+    # below is a manual slow gemm with integer operands and float rescaling,
+    # implemented using eager PyTorch ops. This should be slow but closely
+    # (but not exactly) matching a real int8,int8->int32 gemm with
+    # rescaling, with the only difference being that the sum inside of the
+    # dot product is done in float32 right now.
+    #
+    q_output = torch.zeros(
+        act_q.shape[0],
+        w_q.shape[0],
+        dtype=torch.float32,
+        device=act_q.device,
+    )
+    for m_idx in range(act_q.shape[0]):
+        for n_idx in range(w_q.shape[0]):
+            for g_idx in range(w_q.shape[1] // w_group_size):
+                k_start = g_idx * w_group_size
+                k_end = k_start + w_group_size
+                act_chunk = act_q[m_idx][k_start:k_end]
+                w_chunk = w_q[n_idx][k_start:k_end]
+
+                # (act_q - act_zp) * w_q
+                # result still in int32
+                elem_int32 = (act_chunk - act_zp[m_idx]) * w_chunk
+
+                # sum((act_q - act_zp) * w_q)
+                # this is in float32, so likely a small deviation from the real
+                # kernel, where the entire dot product would be in int32
+                sum_float32 = torch.sum(elem_int32)
+
+                # scale
+                act_scale_tmp = act_scale[m_idx].squeeze(-1)
+                w_scale_tmp = w_scale[n_idx][g_idx].squeeze(-1).bfloat16().float()
+                sum_scaled = sum_float32 * act_scale_tmp * w_scale_tmp
+
+                # accumulate
+                q_output[m_idx][n_idx] += sum_scaled
+
+    return q_output